you don’t know what is possible, you cannot think about those possibilities and consider what they mean for the business.
In the early days of the web, organizations were just thinking about how they might use it to get their marketing materials in front of more consumers. They were not thinking about the capabilities of iPhones to carry the equivalent of shelves of compact discs or every photo album owned, or about what it might be like for consumers to carry a digital bank teller in their pocket. They certainly weren’t imagining entirely new business models like ridesharing services. Those capabilities evolved and required the concurrent evolution of various supporting components. Some organizations were ahead of the curve and disrupted industries, while others underinvested and were left behind. Still others overinvested before the market was ready and wasted resources.
The other operational challenge is that the different parts of the organization—internal systems and processes—move at different speeds and change at different rates. For example, enterprise resource planning (ERP) systems are relatively stable and do not change frequently. A long development cycle is required to add or evolve core modules and functionality. At the other end of the spectrum are social media applications and programs that change extremely rapidly. Inherent mismatches in these clock speeds in the organization exacerbate the data and architecture challenges (see Figure 1-1). As the AI landscape evolves, governance structures, processes, and decision-making need to allow for adaptability and to support cultural change that will be part of the new organization dynamics.
The challenge is that business always changes at a faster pace than the internal IT organization can support, and technology changes faster than humans and organizational processes can absorb. But even if IT could keep up with the best-of-breed tools for the business, people would not have the capacity to absorb change that quickly.
Figure 1-1: Varying Clock Speeds throughout the Organization
Decisions about the pace of change need to be methodical and data driven, not based solely on opinions. A metrics-driven framework for managing decision-making and resource allocation removes guesswork and ensures that your investments will produce value. Sustainable, metrics-driven governance is the single determinant of successful AI programs. Throughout this book, and especially in chapter 10, I’ll show you how to set up a governance playbook that can be updated and evolve as the capabilities of your AI-powered enterprise continue to mature.
Governance is built around organizational structure and reporting models, aspects that need to evolve and be updated as capabilities mature in AI-powered organizations. For example, old school “spray and pray” mass marketing approaches and skill sets need to be updated with data-driven digital marketing precision. The workforce will have a new makeup in the AI-powered enterprise, and just as machinery amplified physical human strengths, machine intelligence will amplify cognitive human strengths.
In this chapter, we began to explore in broad brushstrokes how your organization needs to transform foundational processes and further evolve fundamental capabilities as you embark on your AI-powered path forward. Decisions in each of the three areas—data, technology, and operationalization—need to be grounded in the organization’s strategy for how it will serve the customer, and must be based on data-driven approaches, not trial and error.
Ontology Supports Everything
All of these elements—data, technology, and operationalization—relate 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.
