The AI-Powered Enterprise. Seth Earley

the AI at all of your data” as some in the industry have claimed. Depending on the application it can also require curation and structuring for ingestion into many classes of AI tools, including cognitive systems. More structure will allow the algorithm to function more precisely.

      For cognitive applications such as chatbots, the required training data is the same information that humans need but with a different structure and format. Predictive modeling AI needs training data to build recognition patterns and examples to learn from. Data is more important than the algorithms, and bad data will provide bad results.

      Perhaps technology vendors have assured you that their AI will fix your data. That’s optimistic.⁵ In fact, many of the AI technologies on the market are actually Band-Aid solutions that try to make up for our sins in data and content curation. Because of a lack of resourcing and poor data hygiene, organizations are paying the price, and that price includes trying to fix data with AI, even though it’s the organization’s own data processes and governance that are at fault. Yes, AI can help, but there’s more to it than what the large systems integrators are telling you.

      The way to fix this problem is to harmonize your data with consistent data structures and models, creating a Rosetta Stone that helps your systems communicate and provides a waypoint so your AI can navigate your messy, fast moving, diverse, unstructured and structured data universe. That Rosetta Stone is the ontology.

      While many master data types are challenging and prone to failure, the approaches we will discuss will increase your chances of success and will move the needle on multiple projects. Rather than striving for a “single source of truth,” the goal is to increase the consistency and quality of information so that there is less friction throughout the organization. There will be differences in organizing principles and structures, but rather than being accidental, they will be intentional. The ontology becomes a reference point to inform where information structures need to be harmonized and where there can be (intentional) differences.

       Technology: Having the Right Tools to Serve Customers (Internal and External)

      Choosing the right technologies and integrating them successfully is a critical part of building out your AI program. It is not simply about adopting machine learning tools and technologies that are labeled as “AI,” because, these days, every technology vendor says what it does is “AI.”

      Forget AI for a moment. Recognize that knowledge workers of all types, from engineers to marketers to designers, interact with technologies to accomplish their day-to-day tasks that directly or indirectly support the ways that customers interact. These workers work with systems on their smartphones, laptops, tablets, and intelligent connected appliances and devices. Having the correct technologies integrated in an adaptable, flexible way allows for processes and functionality to evolve in parallel with customer needs and the competitive landscape. Having the right tools enabled and enhanced by AI to serve each stage of the customer journey makes the business run faster, better, and in a way more aligned with customer needs.

      The real challenge is that, if you’re like most organizations, you probably have a patchwork of systems, technologies, and processes that have evolved organically over time. These have inevitably led to messy and complex integrations, manual processes, and workarounds that make things more difficult in the long run. Some people refer to this as technical debt—the shortcuts taken in the hope of deploying technology more quickly. But just like debt in the real world, this approach is costly, and you have to pay for the shortcuts, sometimes at interest rates that would be classified as usury if they were on a consumer’s credit card. What appears to save time and money in the short run, when accumulated over numerous projects and multiple years, hamstrings the business and prevents adaptation as each leader kicks the can down the road for the next person to deal with. And now that person is you.

      Adapting legacy systems built on a patchwork of different platforms and technologies to changing conditions is a slow and laborious process. But it’s essential to making the tools and technology effective and the data actionable.

       Operationalization: Leading with Vision and Managing Change

      Every project, whether a departmental reorganization or a ten-year growth plan, begins with vision and strategy. What is different about an AI strategy is that the possibilities are entirely new, which means you will have to look at the business and customer relationships in entirely new ways. Whenever there are fundamental shifts in technologies, what becomes possible is not just an extension of where we are but an entirely new way of being or interacting. If 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