The AI-Powered Enterprise. Seth Earley

to having consistent fundamental organizing principles reflected in the ontology. Ontologies capture more than data representations. These structures begin at the level of concepts that are important to the business and are then translated into processes, systems, navigational constructs, applications, and yes, data structures. In addition to allowing systems to talk to one another, these naming conventions, organizing constructs, conceptual relationships, and labels speed information flows; enable data, content, and information to be integrated; streamline end-to-end processes; permit the aggregation of data sources; and stimulate faster adaptability in a rapidly changing ecosystem. They allow for the contextualization of insights, the cataloguing of metrics, and the creation of feedback mechanisms for continual improvement.

      In the end, the foundation of an enterprise ontology captures the intelligence of the organization and becomes the scaffolding that guides and optimizes information flows, powers and contextualizes insights from AI systems, and becomes the brains behind tools like chatbots and cognitive search.

       COGNITIVE AI AND ONTOLOGIES

      Many of the examples in this book are from a group of applications referred to as “cognitive” AI. This class of AI seeks to improve how humans interact with computers and requires an intentional approach to building out the underlying knowledge architecture—or, as we discuss throughout the book, the ontology.

      There are other classes of machine learning and analytics types of AI whose algorithms may not depend as extensively on this knowledge engineering approach. However, I want to make two distinctions here. While this book focuses a great deal on cognitive types of AI, I will show how other types of AI that leverage a range of purely data-centric approaches (deep learning and neural networks, for example) can also work better with defined data structures—including knowledge architectures and ontologies.

      For example, machine learning algorithms may not need an ontology to function, but applying the results to the business does require the consistency and efficiency provided by an ontology and the resulting knowledge architecture. Similarly, while a neural network may not require an ontology to perform the analysis, application of that analysis does.

      Many of the approaches in this book are part of a technology-agnostic tool kit for making changes and improvements that lead to greater efficiencies, increased revenue, and greater differentiation in the marketplace. In many organizations, these approaches have not reached their full potential with current technology. Many of the problems that organizations are turning to AI for are in part due to the fact that the correct approaches have not been operationalized using existing technology. In other words, we are using AI (or trying to do so) to make up for our past sins in poor information hygiene. This is the nature of the industry—fast-changing tools and the inability to absorb and manage change effectively over generations of technology adoption. These approaches are even more critical in today’s competitive landscape.

       YOUR CHARGE FOR THE FUTURE

      This is a practical book for CEOs, CMOs, and technology executives who want to transform their business to get a jump on the opportunities of the AI future. It’s not just about the technology of AI. It’s about how to manage the change, step by step. It’s also about understanding where to get the money and where the quick wins are. In short, it’s about learning what a business driven by ontology-powered artificial intelligence can do and how to make that happen.

      Gaining this edge depends on establishing a foundation that includes pieces of the organizational and technology puzzle. Some of these are likely familiar—such as having the right people supported by the right tools and processes—but they will require new approaches, while others are entirely new (such as fresh ways of looking at how technology can support your customers). In some cases, the missing ingredients will be a new sense of discipline and an increased level of resourcing and commitment applied to known approaches to problems.

      To get this edge, executives at your enterprise must create a vision that empowers AI to uniquely deliver your value proposition in support of your customers’ experience, as well as a strategic plan for differentiating from your competitors. It will require developing new supporting processes and new competencies. I will outline the steps for benchmarking the maturity of each of these components and developing a plan for bridging gaps between where you are and where you need to be.

      No twenty-first-century executive can succeed without understanding the role of AI in enterprises and how to make this technology work effectively. It’s time to go beyond clouds, big data, and mobile fantasies. It’s time to learn what it takes to power your enterprise with AI, now.

       TAKEAWAYS FROM CHAPTER 1

      In this chapter, I’ve described how AI will change the priorities for enterprises and how ontology can play a critical role in taking advantage of those opportunities. These are the main points in this chapter:

      •AI marks an inflection point in human history.

      •AI initiatives fail if the information they depend on is not properly structured.

      •The ontology—a central repository of data terms and relationships—is what makes it possible for AI projects to succeed.

      •Organizations are like organisms that depend on freely flowing information to survive.

      •Agility and adaptability are the qualities that enable organizations to thrive in their broader ecosystem.

      •The failure of holistic communication, data incompatibility, and junk data prevent companies from operating effectively.

      •Tagging speeds the flow of information.

      •Competitive advantage arises from the ways that organizations manage data, technology, and operationalization.

       CHAPTER 2

      BUILDING THE ONTOLOGY

      It costs over a billion dollars to build a “fab”—a semiconductor fabrication plant. At a price like that, it had better be up and running every possible hour of the day and night.

      That’s the problem that Applied Materials was struggling to manage. The company bills itself as “the leader in material engineering solutions used to produce virtually every new chip and advanced display in the world.”¹ Its field service technicians are tasked with getting fabs running after problems have taken them offline. A down fab can cost its owner millions of dollars per day in lost business. If Applied Materials can’t fix the problem quickly, it can end up on the hook for large penalties for failing to deliver on its service-level agreements—not to mention the damage to its reputation. It’s a tough job, because semiconductor manufacturing processes are mind-bogglingly complex.

      The know-how to keep a semiconductor fab running was spread throughout so many systems and processes within Applied Materials that up to 40% of its field service technicians’ time was spent searching for the information they needed. Each plant was unique, so technicians needed to be able to locate the exact configuration of the equipment and procedures for any plant they were working in. The technicians work in dust-free, ultraclean environments and have elaborate and time-consuming processes for getting in and out of the plants. Some fabs even prohibit laptops or tablets, so technicians had to equip themselves with all the information they would need to solve any suspected problem before entering the plant.

      Techs in this environment hedged their bets. They stocked their service vehicles with a wide variety of costly components, since not having the right part ready would lead to expensive additional delays. With 3,000 technicians in the field, this practice tied up tens of millions of dollars of inventory. Technicians became frustrated with attempting to locate service information across 14 different systems. Adding complexity, Applied Materials technicians working for one customer might have to maintain trade secrets for that customer, making it impossible for them to share all their information with other technicians.

      Because this challenge