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\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Latest

\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

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\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

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\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
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Latest

\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Algorithms<\/h3>\n\n\n\n

Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

Algorithms<\/h3>\n\n\n\n

Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Hardware<\/h3>\n\n\n\n

To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

Algorithms<\/h3>\n\n\n\n

Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

Hardware<\/h3>\n\n\n\n

To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

Algorithms<\/h3>\n\n\n\n

Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n

Data<\/h3>\n\n\n\n

Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

Hardware<\/h3>\n\n\n\n

To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

Algorithms<\/h3>\n\n\n\n

Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

Tools<\/h3>\n\n\n\n

Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

Expertise<\/h3>\n\n\n\n

Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

Real-world Machine Learning Examples<\/h2>\n\n\n\n

Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

Waymo - Autonomous Cars<\/h3>\n\n\n\n

By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

Strategic ML Application <\/h3>\n\n\n\n

Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

Find Your Truth<\/h2>\n\n\n\n

\u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

Establish Functional Learning Organization<\/h2>\n\n\n\n

\u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

Seek Profitability Opportunities<\/h2>\n\n\n\n

\u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

\u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
Search

Latest

\n
  1. Data<\/li>
  2. Hardware<\/li>
  3. Algorithms<\/li>
  4. Tools<\/li>
  5. Expertise<\/li><\/ol>\n\n\n\n

    Data<\/h3>\n\n\n\n

    Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

    Hardware<\/h3>\n\n\n\n

    To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

    Algorithms<\/h3>\n\n\n\n

    Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

    Tools<\/h3>\n\n\n\n

    Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

    Expertise<\/h3>\n\n\n\n

    Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

    Real-world Machine Learning Examples<\/h2>\n\n\n\n

    Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

    Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

    Waymo - Autonomous Cars<\/h3>\n\n\n\n

    By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

    Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

    Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

    Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

    Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

    Strategic ML Application <\/h3>\n\n\n\n

    Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

    WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

    Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

    Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

    This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

    Find Your Truth<\/h2>\n\n\n\n

    \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

    Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

    Establish Functional Learning Organization<\/h2>\n\n\n\n

    \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

    For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

    Seek Profitability Opportunities<\/h2>\n\n\n\n

    \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

    This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

    Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

    Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

    \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

    VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
    \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
    Search

    Latest

    \n

    Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

    1. Data<\/li>
    2. Hardware<\/li>
    3. Algorithms<\/li>
    4. Tools<\/li>
    5. Expertise<\/li><\/ol>\n\n\n\n

      Data<\/h3>\n\n\n\n

      Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

      Hardware<\/h3>\n\n\n\n

      To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

      Algorithms<\/h3>\n\n\n\n

      Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

      Tools<\/h3>\n\n\n\n

      Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

      Expertise<\/h3>\n\n\n\n

      Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

      Real-world Machine Learning Examples<\/h2>\n\n\n\n

      Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

      Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

      Waymo - Autonomous Cars<\/h3>\n\n\n\n

      By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

      Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

      Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

      Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

      Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

      Strategic ML Application <\/h3>\n\n\n\n

      Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

      WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

      Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

      Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

      This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

      Find Your Truth<\/h2>\n\n\n\n

      \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

      Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

      Establish Functional Learning Organization<\/h2>\n\n\n\n

      \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

      For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

      Seek Profitability Opportunities<\/h2>\n\n\n\n

      \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

      This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

      Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

      Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

      \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

      VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
      \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
      Search

      Latest

      \n

      Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

      Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

      1. Data<\/li>
      2. Hardware<\/li>
      3. Algorithms<\/li>
      4. Tools<\/li>
      5. Expertise<\/li><\/ol>\n\n\n\n

        Data<\/h3>\n\n\n\n

        Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

        Hardware<\/h3>\n\n\n\n

        To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

        Algorithms<\/h3>\n\n\n\n

        Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

        Tools<\/h3>\n\n\n\n

        Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

        Expertise<\/h3>\n\n\n\n

        Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

        Real-world Machine Learning Examples<\/h2>\n\n\n\n

        Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

        Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

        Waymo - Autonomous Cars<\/h3>\n\n\n\n

        By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

        Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

        Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

        Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

        Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

        Strategic ML Application <\/h3>\n\n\n\n

        Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

        WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

        Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

        Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

        This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

        Find Your Truth<\/h2>\n\n\n\n

        \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

        Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

        Establish Functional Learning Organization<\/h2>\n\n\n\n

        \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

        For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

        Seek Profitability Opportunities<\/h2>\n\n\n\n

        \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

        This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

        Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

        Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

        \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

        VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
        \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
        Search

        Latest

        \n

        The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

        Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

        Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

        1. Data<\/li>
        2. Hardware<\/li>
        3. Algorithms<\/li>
        4. Tools<\/li>
        5. Expertise<\/li><\/ol>\n\n\n\n

          Data<\/h3>\n\n\n\n

          Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

          Hardware<\/h3>\n\n\n\n

          To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

          Algorithms<\/h3>\n\n\n\n

          Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

          Tools<\/h3>\n\n\n\n

          Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

          Expertise<\/h3>\n\n\n\n

          Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

          Real-world Machine Learning Examples<\/h2>\n\n\n\n

          Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

          Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

          Waymo - Autonomous Cars<\/h3>\n\n\n\n

          By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

          Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

          Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

          Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

          Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

          Strategic ML Application <\/h3>\n\n\n\n

          Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

          WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

          Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

          Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

          This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

          Find Your Truth<\/h2>\n\n\n\n

          \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

          Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

          Establish Functional Learning Organization<\/h2>\n\n\n\n

          \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

          For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

          Seek Profitability Opportunities<\/h2>\n\n\n\n

          \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

          This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

          Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

          Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

          \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

          VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
          \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
          Search

          Latest

          \n

          As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

          The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

          Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

          Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

          1. Data<\/li>
          2. Hardware<\/li>
          3. Algorithms<\/li>
          4. Tools<\/li>
          5. Expertise<\/li><\/ol>\n\n\n\n

            Data<\/h3>\n\n\n\n

            Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

            Hardware<\/h3>\n\n\n\n

            To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

            Algorithms<\/h3>\n\n\n\n

            Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

            Tools<\/h3>\n\n\n\n

            Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

            Expertise<\/h3>\n\n\n\n

            Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

            Real-world Machine Learning Examples<\/h2>\n\n\n\n

            Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

            Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

            Waymo - Autonomous Cars<\/h3>\n\n\n\n

            By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

            Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

            Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

            Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

            Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

            Strategic ML Application <\/h3>\n\n\n\n

            Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

            WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

            Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

            Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

            This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

            Find Your Truth<\/h2>\n\n\n\n

            \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

            Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

            Establish Functional Learning Organization<\/h2>\n\n\n\n

            \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

            For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

            Seek Profitability Opportunities<\/h2>\n\n\n\n

            \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

            This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

            Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

            Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

            \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

            VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
            \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};
            Search

            Latest

            \n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

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            \n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            Unlike traditional rule-based programming, ML utilizes data-driven rule discovery. In this case, instead of putting in a set of commands, we would feed the algorithm a data set that includes the various sizes of apples, various colors, various origins and so on. The algorithm would then combine all these pieces of data in various ways to generate an outcome. Assuming we only accept large red apples from France, the algorithm would arrive at this same conclusion through reductive computation. That is, it will eliminate all the combinations that result in a rejected outcome. In this way, the algorithm can self-train to look for greater nuances, in the same way that a human would when determining a set of choices.<\/p>\n\n\n\n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            Imagine for a moment that you have gone to the local farmer\u2019s market to buy apples. When you arrive, you find you must select between good apples and bad apples. If we were to build a software program to make this choice, we would have to input rules to tell it how to do so. So, for instance, we would say select for size, color, and origin. If you want the program to use more parameters, you must input these as well. The result is a program that does one thing repeatedly in what is known as traditional rules-based software programming. This type of software is what currently powers most enterprises today. Next, let us look at how a machine learning algorithm would complete the same task.<\/p>\n\n\n\n

            Unlike traditional rule-based programming, ML utilizes data-driven rule discovery. In this case, instead of putting in a set of commands, we would feed the algorithm a data set that includes the various sizes of apples, various colors, various origins and so on. The algorithm would then combine all these pieces of data in various ways to generate an outcome. Assuming we only accept large red apples from France, the algorithm would arrive at this same conclusion through reductive computation. That is, it will eliminate all the combinations that result in a rejected outcome. In this way, the algorithm can self-train to look for greater nuances, in the same way that a human would when determining a set of choices.<\/p>\n\n\n\n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            Machine Learning vs. Software Programming<\/h2>\n\n\n\n

            Imagine for a moment that you have gone to the local farmer\u2019s market to buy apples. When you arrive, you find you must select between good apples and bad apples. If we were to build a software program to make this choice, we would have to input rules to tell it how to do so. So, for instance, we would say select for size, color, and origin. If you want the program to use more parameters, you must input these as well. The result is a program that does one thing repeatedly in what is known as traditional rules-based software programming. This type of software is what currently powers most enterprises today. Next, let us look at how a machine learning algorithm would complete the same task.<\/p>\n\n\n\n

            Unlike traditional rule-based programming, ML utilizes data-driven rule discovery. In this case, instead of putting in a set of commands, we would feed the algorithm a data set that includes the various sizes of apples, various colors, various origins and so on. The algorithm would then combine all these pieces of data in various ways to generate an outcome. Assuming we only accept large red apples from France, the algorithm would arrive at this same conclusion through reductive computation. That is, it will eliminate all the combinations that result in a rejected outcome. In this way, the algorithm can self-train to look for greater nuances, in the same way that a human would when determining a set of choices.<\/p>\n\n\n\n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

            Latest

            \n

            This illustration is perhaps the perfect stage-setter for the next iteration of advancements in enterprise digital technology applications. Rapid advances in ML are now seen as having the potential to supplant traditional software programming within the enterprise context. To further understand how ML applies to the enterprise, let us first look at another simple illustration.<\/p>\n\n\n\n

            Machine Learning vs. Software Programming<\/h2>\n\n\n\n

            Imagine for a moment that you have gone to the local farmer\u2019s market to buy apples. When you arrive, you find you must select between good apples and bad apples. If we were to build a software program to make this choice, we would have to input rules to tell it how to do so. So, for instance, we would say select for size, color, and origin. If you want the program to use more parameters, you must input these as well. The result is a program that does one thing repeatedly in what is known as traditional rules-based software programming. This type of software is what currently powers most enterprises today. Next, let us look at how a machine learning algorithm would complete the same task.<\/p>\n\n\n\n

            Unlike traditional rule-based programming, ML utilizes data-driven rule discovery. In this case, instead of putting in a set of commands, we would feed the algorithm a data set that includes the various sizes of apples, various colors, various origins and so on. The algorithm would then combine all these pieces of data in various ways to generate an outcome. Assuming we only accept large red apples from France, the algorithm would arrive at this same conclusion through reductive computation. That is, it will eliminate all the combinations that result in a rejected outcome. In this way, the algorithm can self-train to look for greater nuances, in the same way that a human would when determining a set of choices.<\/p>\n\n\n\n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

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            \n

            Software is eating the world, but AI will eat up software.<\/p>Jensen Huang\u0431, NVIDIA CEO<\/cite><\/blockquote>\n\n\n\n

            This illustration is perhaps the perfect stage-setter for the next iteration of advancements in enterprise digital technology applications. Rapid advances in ML are now seen as having the potential to supplant traditional software programming within the enterprise context. To further understand how ML applies to the enterprise, let us first look at another simple illustration.<\/p>\n\n\n\n

            Machine Learning vs. Software Programming<\/h2>\n\n\n\n

            Imagine for a moment that you have gone to the local farmer\u2019s market to buy apples. When you arrive, you find you must select between good apples and bad apples. If we were to build a software program to make this choice, we would have to input rules to tell it how to do so. So, for instance, we would say select for size, color, and origin. If you want the program to use more parameters, you must input these as well. The result is a program that does one thing repeatedly in what is known as traditional rules-based software programming. This type of software is what currently powers most enterprises today. Next, let us look at how a machine learning algorithm would complete the same task.<\/p>\n\n\n\n

            Unlike traditional rule-based programming, ML utilizes data-driven rule discovery. In this case, instead of putting in a set of commands, we would feed the algorithm a data set that includes the various sizes of apples, various colors, various origins and so on. The algorithm would then combine all these pieces of data in various ways to generate an outcome. Assuming we only accept large red apples from France, the algorithm would arrive at this same conclusion through reductive computation. That is, it will eliminate all the combinations that result in a rejected outcome. In this way, the algorithm can self-train to look for greater nuances, in the same way that a human would when determining a set of choices.<\/p>\n\n\n\n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

            Search

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            \n

            Founded 12 years ago, the Spotify music streaming service is today as ubiquitous as radio. Where it differs from radio, however, is that whenever any one of its millions of listeners tunes in, they find a music playlist customized to their individual taste. This magical experience is made possible through Machine Learning (ML) technology, a branch of Artificial Intelligence (AI), which \u201clearns\u201d listening preferences to create customized playlists that are potentially in the billions of song combinations. This massive scale of personalization is only made possible by the advent of ML technologies. While this is illustration applies to the music industry, ML applications cut across multiple industries, making it necessary for corporations to explore ways to use the technology to fend off competitors.<\/p>\n\n\n\n

            Software is eating the world, but AI will eat up software.<\/p>Jensen Huang\u0431, NVIDIA CEO<\/cite><\/blockquote>\n\n\n\n

            This illustration is perhaps the perfect stage-setter for the next iteration of advancements in enterprise digital technology applications. Rapid advances in ML are now seen as having the potential to supplant traditional software programming within the enterprise context. To further understand how ML applies to the enterprise, let us first look at another simple illustration.<\/p>\n\n\n\n

            Machine Learning vs. Software Programming<\/h2>\n\n\n\n

            Imagine for a moment that you have gone to the local farmer\u2019s market to buy apples. When you arrive, you find you must select between good apples and bad apples. If we were to build a software program to make this choice, we would have to input rules to tell it how to do so. So, for instance, we would say select for size, color, and origin. If you want the program to use more parameters, you must input these as well. The result is a program that does one thing repeatedly in what is known as traditional rules-based software programming. This type of software is what currently powers most enterprises today. Next, let us look at how a machine learning algorithm would complete the same task.<\/p>\n\n\n\n

            Unlike traditional rule-based programming, ML utilizes data-driven rule discovery. In this case, instead of putting in a set of commands, we would feed the algorithm a data set that includes the various sizes of apples, various colors, various origins and so on. The algorithm would then combine all these pieces of data in various ways to generate an outcome. Assuming we only accept large red apples from France, the algorithm would arrive at this same conclusion through reductive computation. That is, it will eliminate all the combinations that result in a rejected outcome. In this way, the algorithm can self-train to look for greater nuances, in the same way that a human would when determining a set of choices.<\/p>\n\n\n\n

            Ai Business Case<\/h2>\n\n\n\n

            The idea that it is possible to train algorithms to make choices has tremendous applications in the enterprise. Consider the company Arterys. Offering medical imaging cloud AI, the company uses machine learning to process radiology scans to identify anomalies. By using each subsequent scan as a basis to improve future results, the AI can spot tumors faster and more effectively than a human radiologist would. However, it is not enough to look at such awe-inspiring examples to know that ML is poised to accelerate in the enterprise setting. One need only look at the amount of money going into ML to see a rapidly accelerating trend.<\/p>\n\n\n\n

            Venture Capital Investment Growth in ML<\/h2>\n\n\n\n

            According to CB Insights, in Q1 of 2012, there was only one publicly disclosed merger and acquisition or M&A deal in the ML space. By Q1 of 2017, that figure had soared to 34  publicly disclosed deals. While tech giants like Google and Amazon are leading this wave of acquisitions, the same report shows that other legacy businesses like IBM, Nokia and GE are also getting in on the action. This rapid acceleration in the space demonstrates an increasing urgency to acquire the necessary technology to apply ML in more mainstream ways. What is shaping up is the greatest enterprise platform revolution since desktop computing.<\/p>\n\n\n\n

            Enterprise Platform Revolution<\/h2>\n\n\n\n
            \"\"<\/figure><\/div>\n\n\n\n

            As with all technological revolutions, adoption always follows a bell curve of what is known as the hype cycle. Referencing the Gartner hype cycle research methodology, we find ML just beginning to come off the peak of inflated expectations. From the chart, Gartner predicts that ML is two to five years away from the plateau of productivity, a point that represents a mainstream platform revolution. For enterprises looking at ML, now is the right time to begin experimenting with the technology as it provides first-mover advantage before laggards move to adopt the technology.<\/p>\n\n\n\n

            The real opportunity ML represents, however, is its industry agnostic nature. Companies across industry verticals can find useful and productive applications to boost their competitive advantages. ML-as-a-Service infrastructure investments from tech companies like Google, Amazon, IBM, and others provide a ready opportunity for forward-thinking firms to start experimenting with ML without having to make massive investments.<\/p>\n\n\n\n

            Enterprise Machine Learning Adoption Drivers<\/h2>\n\n\n\n

            Firms that are still unsure about investing in ML must know platform revolutions take the form of massively disruptive self-perpetuating cycles that leverage emergent technologies to accelerate. In the case of ML, there are five key drivers of adoption:<\/p>\n\n\n\n

            1. Data<\/li>
            2. Hardware<\/li>
            3. Algorithms<\/li>
            4. Tools<\/li>
            5. Expertise<\/li><\/ol>\n\n\n\n

              Data<\/h3>\n\n\n\n

              Data is the foundation of ML. Today, petabytes of data are available for ML purposes. Intel CEO Brian Krzanich calls data the new oil. In the same way oil fueled an entire industrial revolution, he sees data as the new oil fueling the ongoing digital transformation revolution.<\/p>\n\n\n\n

              Hardware<\/h3>\n\n\n\n

              To process all this data, AI-focused chip development like NVIDIA\u2019s Tesla GPU as well as chips from other companies like Intel, AMD, and Qualcomm, is on the rise.<\/p>\n\n\n\n

              Algorithms<\/h3>\n\n\n\n

              Deep Learning, a type of complex ML, is at the core of some of the most advanced AI applications such as IBM\u2019s Watson and Google\u2019s Deep Mind. Such algorithms are creating new possibilities like natural language processing, autonomous cars, image recognition, among others.<\/p>\n\n\n\n

              Tools<\/h3>\n\n\n\n

              Currently, available ML tools represent capabilities that allow firms to utilize these ML resources in practical and scalable ways. Such tools include ML-as-a-Service, ML APIs, and open source ML technologies like TensorFlow, Keras, Microsoft Cognitive Toolkit, among others.<\/p>\n\n\n\n

              Expertise<\/h3>\n\n\n\n

              Human expertise brings ML to life by modeling potential use cases. As perhaps the most crucial component of all five, this represents an opportunity for corporate leaders to be at the forefront of discovering ML applications that can help vault their firms to the next level of growth.<\/p>\n\n\n\n

              Real-world Machine Learning Examples<\/h2>\n\n\n\n

              Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n

              Incorporating hundreds of thousands of anonymized patient records, Mount Sinai Hospital\u2019s Deep Patient can diagnose hard-to-catch ailments by processing patient data and cross-referencing with machine-learned data.<\/p>\n\n\n\n

              Waymo - Autonomous Cars<\/h3>\n\n\n\n

              By subjecting autonomous vehicle (AV) ML algorithms to thousands of miles of real-world driving, Waymo is training its autonomous cars to one day drive safely with no human intervention.<\/p>\n\n\n\n

              Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n

              Google Duplex, an advanced personal assistant AI, can call a business and, using natural-sounding speech, book an appointment. This application is but the tip of the iceberg of what is possible.<\/p>\n\n\n\n

              Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n

              Crunching billions of data points sent in from Android devices running Google Maps, Google Maps not only correctly estimates transit ETA\u2019s, but also provides alternative routes.<\/p>\n\n\n\n

              Strategic ML Application <\/h3>\n\n\n\n

              Companies like Google and Baidu typically have upwards of a thousand engineers focused on ML applications. While this may not be viable for most companies, it is important to have an ML strategy in place. Such a strategy could mean either internal provisioning of resources or partnering with startups in the ML space. In either case, not having an ML strategy in place means exposing the firm to disruptive threats from other forward-thinking players currently experimenting with ML in internal innovation labs.<\/p>\n\n\n\n

              WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n

              Machine Learning is the next frontier in enterprise software applications. Where desktop computing gave rise to the current information age, machine learning is poised to create enterprise computing capabilities we are only beginning to comprehend. This article is an excerpt of a one-hour deep-dive webinar into enterprise machine learning by Ed Fernandez, co-founder of innovation advisory firm NAISS, early-stage, and startup VC and mentor at Singularity University.<\/p>\n","post_title":"The Race Towards Enterprise-Level Machine Learning Applications","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"the-race-towards-enterprise-level-machine-learning-applications","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/the-race-towards-enterprise-level-machine-learning-applications\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":619,"post_author":"1","post_date":"2018-11-02 09:21:00","post_date_gmt":"2018-11-02 16:21:00","post_content":"\n

              Rapid advances in digital technologies and the resulting disruptive innovations sweeping multiple industries has made innovation at a corporate level an imperative. This (non-exhaustive) list<\/a> from Investopedia identifies 20 industries that are about to or already undergoing massive disruption fueled by digital technologies. While corporations are responding to this twin threat and opportunity by applying digital transformation methods and other initiatives, there is one weakness these strategies have that can thwart the overall impact of such internal efforts on the corporation\u2019s defensibility and profitability.<\/p>\n\n\n\n

              This weakness has to do with commercialization of innovation. While most corporations are rushing to incorporate cutting-edge digital technologies into their existing products or create new products altogether, they must be cognizant of the fact that the market will ultimately determine the viability of such innovations. To put it differently, customers will not buy technology, but solutions. This is a strong recommendation Andrew Goldner and Sean Sheppard, co-founders of GrowthX<\/a>, put forward when we spoke with them about commercializing corporate innovation.<\/p>\n\n\n\n

              Find Your Truth<\/h2>\n\n\n\n

              \u201cIt starts with the truth,\u201d says Sean. When corporations embark on an innovation agenda, they must start by first determining the truth of the effort they are undertaking. If new product development is underway, the truth could mean determining whether there is product\/market fit, or put differently, whether a market exists for that product, or if a pivot to something different is necessary, or whether to shelve the product altogether. The challenge corporations face is they lack a framework by which to discover this truth. Such a framework is necessary to provide a roadmap that is replicable across all innovations the corporation chooses to undertake.<\/p>\n\n\n\n

              Undertaking such a framework requires either an internal entrepreneur or an entrepreneur-in-residence, which could be one or a handful of people tasked with rapidly iterating on feedback emanating from the market on a given innovation. This iteration must be done in small non-scalable ways in order to arrive faster at the truth about that innovation. \u201cCan you determine whether or not there is a business model and a way to monetize that innovation? And what does that look like? How big is that opportunity beyond your early customers?\u201d are some of the questions Sean urges corporations to ask about their innovations. The answers to these questions will often point to the truth about the innovation. But in order to embrace this culture of seeking out the truth, organizations must first create and implement functional learning organization.<\/p>\n\n\n\n

              Establish Functional Learning Organization<\/h2>\n\n\n\n

              \u201cYou have to believe that learning leads to revenue and if you do believe that and the organization is behind it, you\u2019re going to find your truth,\u201d counsels Sean. Andrew provides more perspective to this by adding that corporate culture is often a barrier to finding this truth. As entrenched corporate culture and mindsets are difficult to replace, the duo point to a more measured and effective means of achieving incremental change. They call it establishing a functional learning organization. \u201cDo it in very small bits. Try to create functional learning out of small groups and teams,\u201d explains Sean, \u201cto establish measured learning and entrench data-driven decision making.\u201d The importance of starting with measured learning is that it creates momentum that leads to the next step, and then the next.<\/p>\n\n\n\n

              For this strategy to work, says Sean, \u201cyou actually need to be out interfacing with those customers and those early customers, which you shouldn\u2019t be selling to, you should be recruiting for joint development.\u201d This points to a crucial factor corporations must address throughout their innovation cycles; listening and learning from the market is tied to revenue. As the small units within the organization undergo functional learning and increasingly find the truth about the products they are responsible for, there emerges a direct correlation with the organization\u2019s revenue. Sean sums it up this way, \u201cIf you don\u2019t have that mindset and people don\u2019t buy into the fact that learning leads to revenue, then more often than not (your innovation agenda) is going to fail.\u201d<\/p>\n\n\n\n

              Seek Profitability Opportunities<\/h2>\n\n\n\n

              \u201cWhere we find the biggest gap to be addressed that\u2019s holding most big companies back from the big bets they\u2019re looking to make is the commercialization,\u201d advances Andrew. He goes on to explain that most innovation that goes on in corporations has to do with improving the status quo, not making disruptive big bets. While conversations around innovation may be ongoing within corporations, what is often missing is the how, he says. Sean provides an answer to this dilemma, \u201cWe\u2019ve moved out of this age of developed technology into this age of applied technology where it\u2019s never been easier to get a product to market yet it\u2019s never been harder to sell it.\u201d<\/p>\n\n\n\n

              This creates a scenario where corporations must put more emphasis on<\/strong> market development earlier in the technology and research and product development phases to figure out what they should be spending their time and money and resources on by actually solving problems for customers and end-users instead of just building cool tech.<\/strong><\/p>\n\n\n\n

              Such a market-first, product-second mindset is what corporations need to foster within their teams in order to discover commercialization and profitability opportunities within their innovation agendas before spending substantial resources upfront.<\/p>\n\n\n\n

              Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n

              \u201cIt\u2019s not about finding a market; it\u2019s about finding the truth,\u201d emphasizes Andrew. While it is advisable for corporations to partner with startups from Silicon Valley to build next-generation products, finding the truth means knowing when an innovation will be profitable and when it may not be. The imperative, therefore, becomes to seek out the truth behind your innovations before undertaking a full-scale roll-out. Sean concludes by saying that to sustain corporate innovation commercialization, the current innovation economy will force product companies to transform into services companies that focus more on listening to the market through data, than on building new products.<\/p>\n\n\n\n

              VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
              \nhttps:\/\/youtu.be\/k4tmo6aVtzw\n<\/div><\/figure>\n","post_title":"Transform Corporate Innovation into Commercial Success","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"transform-corporate-innovation-into-commercial-success","to_ping":"","pinged":"","post_modified":"2019-12-27 20:45:15","post_modified_gmt":"2019-12-28 04:45:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/transform-corporate-innovation-into-commercial-success\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":false,"total_page":1},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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            Blog: November 2, 2018

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