\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
\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
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
\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
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 \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 \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 \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 \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 \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 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 \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 \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 \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 \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 \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 \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 \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 \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 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 \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 \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 \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 \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 \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 \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 \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 \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 \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 \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 \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 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 \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 \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 \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 \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 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 \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 \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 \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 \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 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 \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 \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 \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 \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 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 \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 \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 \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 \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 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 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 \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 \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 \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 \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 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 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 \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 \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 \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 \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 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 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 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 \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 \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 \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 \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 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 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 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 \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 \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 \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 \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 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 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 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 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 \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 \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 \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 \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 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 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 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 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 \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 \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 \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 \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 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, 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 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 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 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 \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 \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 \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 \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 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, 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 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 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 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 \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 \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 \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 \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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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 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 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 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, 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 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 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 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 \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 \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 \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 \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 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 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 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 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 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, 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 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 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 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\nSustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Real-world Machine Learning Examples<\/h2>\n\n\n\n
Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Real-world Machine Learning Examples<\/h2>\n\n\n\n
Mount Sinai Hospital Deep Patient - Medical Diagnosis<\/h3>\n\n\n\n
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Expertise<\/h3>\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
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Expertise<\/h3>\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
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Tools<\/h3>\n\n\n\n
Expertise<\/h3>\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
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Tools<\/h3>\n\n\n\n
Expertise<\/h3>\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
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
Find Your Truth<\/h2>\n\n\n\n
Establish Functional Learning Organization<\/h2>\n\n\n\n
Seek Profitability Opportunities<\/h2>\n\n\n\n
Sustaining Corporate Innovation Commercialization<\/h2>\n\n\n\n
VIDEO: Interview with Andrew Goldner and Sean Sheppard<\/h1>\n\n\n\n
\n
Latest
Algorithms<\/h3>\n\n\n\n
Tools<\/h3>\n\n\n\n
Expertise<\/h3>\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
Waymo - Autonomous Cars<\/h3>\n\n\n\n
Google Duplex - Autonomous Personal Assistant<\/h3>\n\n\n\n
Google Maps \u2013 Transit Optimization<\/h3>\n\n\n\n
Strategic ML Application <\/h3>\n\n\n\n
WEBINAR: Towards Zero Clicks: Machine Learning and AI for Every Business<\/h2>\n\n\n\n
