There is no doubt that disruption is happening across industries. Riding on the emergence of advanced digital technologies and other innovations, industries are transforming in response to market trends. This transformation often referred to as digital transformation, is seen as a key driver in the emergence of the leading companies of the future.
Technologies like Augmented Reality, Virtual Reality, Blockchain, Artificial Intelligence, and others are not only influencing massive trends individually but according to Jim Carroll, futurist and disruption advisor to Fortune 500 companies, the convergence of these trends will result in a megatrend that will permanently upend and transform all industries. Responding to these mini and megatrends will determine how well organizations adapt to a digital-first future.
Three times a year, Silicon Valley Innovation Center holds an executive immersion program called Leading Digital Transformation. The program is designed to help corporate executives learn about the latest trends in digital transformation, directly from the Silicon Valley insiders.
Over the five-day program, executives visit and interact with Silicon Valley startups building the next generation of products in AI, VR, the blockchain, and others while also attending in-depth talks and workshops by innovation leaders from within the Silicon Valley ecosystem.
Additionally, visits to innovative tech giants like HPE, Amazon, Google, and Apple provide first-hand insights into the innovation strategies these companies use to dominate their respective industries. In this article, we highlight key insights of Day 2 of the Leading Digital Transformation program held in July 2018.
Evaluating the Impact of Artificial Intelligence (AI): Trends and Failures
In 1959, Arthur Samuel described Machine Learning as a field of study that gives computers the ability to learn without being explicitly programmed. Over the intervening years, the field of AI, of which Machine Learning or ML is a sub-field, has rapidly advanced. The speaker, founder, and partner at NAISS and an AI expert, began his talk by referencing a quote by NVIDIA CEO and co-founder Jensen Huang, “Software is eating the world, but AI will eat software.”
The quote stems from an emerging trend where traditional rule-based software programming is rapidly being replaced by ML data-driven rules discovery. This shift is happening as traditional software programming reaches its limits, limits that ML applications are uniquely suited to surpass.
Organizations interested in ML, he said, must first realize that the field of AI if broad and includes ML, Natural Language Processing, expert systems, vision, speech, planning and robotics applications. He said that the area most enterprises need to focus on to solve current problems or optimize current processes is ML.
Quoting Jeff Bezos, founder, and CEO of Amazon, Ed pointed out that to extract maximum value from ML, organizations must approach it as an enabling technology rather than a stand-alone tool. This means that ML should be deployed as a tool that helps optimize specific business cases within the various verticals within an organization.
“I would say, a lot of the value that we’re getting from machine learning is actually happening kind of beneath the surface. It is things like improved search results, improved product recommendations for customers, improved forecasting for inventory management, and literally hundreds of other things beneath the surface.”
Jeff Bezos, Founder, and CEO Amazon
The speaker was, however, quick to caution that AI hype in the media frequently over-estimates the outcomes of implanting AI while under-estimating the difficulty of implementation. Organizations will do well to distinguish what is practical and can be commercialized and what is still in the realm of theory.
To help make this distinction, he provided examples of ML startups in Silicon Valley that provide ML-as-a-Service, saying corporations can benefit greatly from partnering with them. Silicon Valley startups like BigML, DataRobot, Drive.ai, Cloudera, and others have the ready tools and infrastructure to help forward-thinking corporations discover and unlock ML potential within their processes.
Fast Tracking Business Process Transformation with Conversational AI
The field of AI continues to draw interest from the enterprise sector as practical applications are considered a possible enabler in the quest to serve millions of increasingly digitized customers. One field of AI that has drawn particular interest is conversational AI. These are a set of technologies that allow customers to access often human-centric services like support in a chat format without the need to speak with a human.
To provide an overview of what conversational AI means for different industries, a global director from Avaamo was on hand to take participants through real-world applications of the technology and how companies utilizing them are dramatically transforming how customers access services.
The speaker started by explaining the difference between current chatbots and conversational AI. Most chatbots people use today are built around linear conversations, which follow a step format. If a user asks a question that has not been hard-coded into the conversation matrix, the bot is unable to continue to conversation. Conversational AI, on the other hand, allows for multi-turn conversations, like what two humans would have.
For instance, if the customer asks a question unrelated to the current conversation, the AI can switch track and maintain the conversation. To deliver this type of AI, he said, Avaamo has built a full stack platform that integrates front-end tools, machine learning, data science automation, and enterprise integrations. The result is a platform that affords enterprises a plug-and-play solution that utilizes internal data resources to offer conversational AI solutions to customers.
The speaker offered an example of one of Avaamo’s clients, Fannie Mae. The lending giant wanted to make a twelve-hundred-page document easier to navigate and understand. Using machine learning, Avaamo was able to deploy a conversational AI chatbot that allowed users to ask specific questions around the document.
The bot would ask some qualifying questions and then respond with the relevant document sections. Through this automation, Fannie Mae was able to cut time spent navigating the document significantly. Organizations with many employees and customers can significantly benefit from such solutions and can do so by partnering with Avaamo or other innovative Silicon Valley startups building solutions in the field of conversational AI.
Integration of New Technologies Through Lean Startup Principles
Lean management principles have been successfully demonstrated in the manufacturing industry with Toyota as perhaps the most famous example of a large corporation using lean management to go from struggling business to a global leader. The lean startup methodology is an updated version of this philosophy that goes a step further by utilizing lean startup principles to accelerate learning and innovation within a company.
The founder and CEO of Octetra, who led the session, started the last session of the day by explaining the difference between traditional linear product development processes and modern iterative ones, a difference that is at the core of the lean startup methodology.
“Linear processes follow a waterfall trajectory, going from one step to the next all through the product development cycle,” he explained. This is the traditional approach to product development that typically involves building a best-guess product, generating PR buzz and doing a launch event, signing up users and then figuring out whether users want the product.
Lean, iterative processes flip this approach, he said, starting with small experiments that deliver a minimum viable product as quickly as possible, learning from early adopters and then either pivoting (changing direction) if the hypothesis fails or persevering if proven.
Integrating lean principles within a corporate setting requires a company to focus on the following lean principles:
- Continuous delivery
- Cross-functional teams
- Religious measurement of key metrics
- Inspection and adaptation.
Responding to a question from one of the attendants on how to know whether experiments are scalable, the speaker pointed out that the idea behind lean startup principles is to run continuous incremental experiments. However, he emphasized that waiting to get all the answers would return the organization to the traditional way of doing things. Instead, the company should be willing to run increasingly bigger experiments and not hesitate to pivot if the data begins to disprove the hypothesis.
Day 2 Conclusion
Day 2 of the Leading Digital Transformation program took participants on a deep dive into next-generation technologies and business models transforming the enterprise landscape. In addition to the sessions outlined above, participants also visited Google and Hewlett Packard Enterprises, where they learned how to build an impactful digital brand and the role of AI, ML, and IoT in the digital age, respectively.
Participants completed the day’s activities with a solid understanding of practical ways they can integrate innovative technologies and business models into their specific enterprise use cases. In Day 3 of the program, participants learned about the digitization of everything and how this megatrend is poised to disrupt established business models dramatically.