Will you be staying at the Hilton at the airport again? They’ve got the best price for a three-star hotel.”
“Go ahead and book it. And get me a rental car, too. Thanks, Pete.”
Perkins muses for a moment about travel reservations. All that clicking and checking he used to do. . . . But the bots pulled it together in a way he never could have before. Perkins likes the feeling of control that gives him—and the efficiency, too.
Perkins doesn’t need to think about what is going on behind the scenes to make investing, manufacturing, and travel so much easier than they used to be. But every time he interacts with a bot, he is drawn deeper into an ecosystem of artificial intelligence. He has grown to rely on the companies that were making his life easier—those that were ready with AI. Everybody else is working with those companies, too, and the competition is slowly fading into irrelevance.
WHAT IT TAKES TO SUCCEED IN THE AI FUTURE
We’re on the verge of the future that Allen Perkins lives in. But not every company will make it into that future.
We know how we want our companies to work. Enterprises ought to be customer-focused, responsive, and digital. They should deliver to each employee and customer exactly what they need, at the moment they need it. The data and technology to do this are available now.
Any big company is likely to have an abundance of technology. It has systems for customers, inventory, and products, along with websites and mobile apps. These systems are spitting out data all day long. Within that data is exactly the information needed to make a business more responsive. The problem is, the data is often not used as it could (and should) be.
Artificial intelligence ought to take that data and turn it into effective execution, just as it did for Allen Perkins. IBM’s Watson and Amazon’s Alexa seem pretty smart. But despite the billions of dollars spent so far on bots and other tools, AI continues to stumble. Why can’t it magically take all that data and make an enterprise run faster and better? Why can’t it deliver the experience that Allen Perkins would expect?
Our organizations are up to their eyeballs in technology, and every venture capitalist believes that yet another tool is what the industry needs. But even after multiple generations of investments and billions of dollars of digital transformations, organizations are still struggling with information overload, with providing excellent customer service, with reducing costs and improving efficiencies, with speeding the core processes that provide a competitive advantage. Why is this happening? Because the foundational principles that I will discuss throughout this book are ignored, given short shrift, deprived of resources, or considered an afterthought. The elements that are required to make the shiny new technologies live up to their promise require hard work that is not sexy and shiny. There are new tools and approaches that make these efforts more efficient, and ways to embed new approaches to dealing with information and data, but they still require discipline, focus, attention, and resources.
Perhaps your organization has experimented with AI. An executive at a major life insurance company recently told me, “Every one of our competitors and most of the organizations of our size in other industries have spent at least a few million dollars on failed AI initiatives.” In some cases, technology vendors have sold “aspirational capabilities”—functionality that was not yet in the current software. But in most cases, the cause of the failure was overestimation of what was truly “out-of-the-box” functionality, overly ambitious “moonshot” programs that were central to major digital transformation efforts but unattainable in practice, or existing organizational processes incompatible with new AI approaches. Leadership may have bought into the promise of AI without adequate support from the front lines of the business. Technology organizations may not have been adequately prepared to take on new tools and significant process changes. In many cases, the technology may have been potentially capable of functionality, but the data, locked in siloed systems, was inaccessible, poorly structured, or improperly curated.
Many AI programs attempt to deal with unstructured information and replicate how humans perform certain tasks, such as answering support questions or personalizing a customer experience. That may require pulling information from multiple systems and weaving together multiple processes, including some that have historically been done manually. To deliver on its promise, AI needs the correct “training data,” including content, metadata (descriptions of data), and operational knowledge. If that data and corresponding outcomes are not available in a way that the system can process, then the AI will fail.
How do you make those data and outcomes accessible to power the AI? That’s where the ontology comes in.
THE CENTRAL ROLE OF THE ONTOLOGY
AI cannot start with a blank page. It leverages information structures and architecture. Artificial intelligence begins with information architecture. In other words, there is no AI without IA.3
AI works only when it understands the soul of your business. It needs the key that unlocks that understanding. That’s the science behind the magic of AI. The key that unlocks that understanding is an ontology: a representation of what matters within the company and makes it unique, including products and services, solutions and processes, organizational structures, protocols, customer characteristics, manufacturing methods, knowledge, content and data of all types. It’s a concept that, correctly built, managed, and applied, makes the difference between the promise of AI and delivering sustainably on that promise.
Simply put, an ontology reveals what is going on inside your business—it’s the DNA of the enterprise.
An ontology is a consistent representation of data and data relationships that can inform and power AI technologies. In different contexts, it can include or become expressed as any of the following: a data model, a content model, an information model, a data/ content/information architecture, master data, or metadata. But an ontology is more than each of these things in themselves. However you describe it, the ontology is essential to and at the heart of AI-driven technologies. To be clear, an ontology is not a single, static thing; it is never complete, and it changes as the organization changes and as it is applied throughout the enterprise.
The ontology is the master knowledge scaffolding of the organization. Multiple data and architectural components are created from that scaffolding, so without a thoughtful and consistent approach to developing, applying, and evolving the ontology, progress in moving toward AI-driven transformation will be slow, costly, and less effective. The components of the ontology are the ones we have mentioned: metadata structures, reference data, taxonomies, controlled vocabularies, thesaurus structures, lexicons, dictionaries, and master data correctly designed into the information technology ecosystem. The ontology is at the heart of the information design of the AI-powered enterprise and it becomes an asset of ever-increasing value.
While it is true that some algorithms can operate on data without an external structure, they still operate based on the features programmed into the underlying system. Even if there is no structure to the raw data, the algorithm will perform better if more of that structure is provided as an input—as an element of the ontology.
Ontologies are a complex topic. For that reason, I’ve dedicated a whole chapter to them: chapter 2. For now, just know that the ontology is what makes the difference in whether AI drives your enterprise forward or just adds to the incompatible welter of technology that is slowing you down.
ORGANIZATIONS ARE LIKE BIOLOGICAL ORGANISMS
I like the economist Gareth Morgan’s metaphor likening businesses to organisms living in an ecosystem competing for resources.4 It explains
