I have always liked Seth’s favorite line of, “There is no AI without IA,” where “IA” is information architecture—another term for ontology. It’s both catchy and mostly true. Of course, as an academic (though a pretty practical one), I am inclined to look for exceptions to general rules, and I think there are some exceptions to that sensible rule.
You will note in this book that the primary examples of AI that Earley uses are the conversational approaches that organizations are widely adopting for interactions with customers and employees. His appealing rule about AI and IA is true for the great majority of conversational AI applications. Perhaps there are exceptions to it if you include, for example, statistical translation programs like Google Translate in the category of conversational AI. I’m not sure they belong in the category, but they do facilitate conversation with speakers of other languages.
The other exception domain is also debatable. I have argued with Seth that machine learning—statistical approaches to prediction and classification that many observers include as AI—doesn’t necessarily demand an ontology. It requires data (often great heaping gobs of it) on multiple variables (also called features) in rows and columns, but we don’t need to know the terminological relationships between them.
But Earley’s counterargument is that if you are managing enterprise data, it is hugely beneficial to have an ontology that describes what data entities you have in your organization and how they relate. If you have a customer ID data element, for example, it’s very useful to know that it includes current customers, former customers, and prospects, and that customer orders are aggregated by that ID. Earley has a particular passion for customer and product data, and I agree that they are perhaps the most important types for most organizations.
I am not a fan of massive projects to create enterprise-level ontologies (often known as “master data management” or “enterprise information architecture” projects), but there is no doubt that your organization should know what data elements you have access to, and how they relate to other data. And I believe that ontology efforts are of more value if they are relatively narrow in scope. So I partially concede the argument to Earley even for machine learning. And in the book he describes a “bottom-up, data-and content-centric” approach to creating ontologies, which I generally believe is more effective.
In any case, there is far more to this book than urgings about ontology. It also addresses such topics as the critical role of data in digital transformation, techniques like tagging data with metadata, organizational challenges for effective data, and the role of data in the customer experience. My favorite story in that regard involves granite countertop cleaners, but I won’t spoil your suspense by relating it.
A great strength of the book is that it is replete with such examples from Earley’s experience and the work of his consulting firm. He has worked with companies across many different industries—many of them very large and successful firms—that had problems with their data in the context of AI. His firm has built successful and sophisticated intelligent agent or chatbot systems for both customers and employees.
I have written two books on AI and read many more, and I do not know of any books that have such useful and detailed advice on the relationship between data and successful conversational AI systems. If that’s what you need, read on.
Thomas H. Davenport
President’s Distinguished Professor at Babson College, Research Fellow at MIT Initiative on the Digital Economy, and author of Only Humans Need Apply and The AI Advantage
CHAPTER 1
THE PROMISE AND THE CHALLENGE OF AI
How will the future be different as a result of artificial intelligence? And what must your company do to stake its claim on that future? These questions are on the minds of every forward-thinking business leader, and they should be on your mind, too, whatever industry you are in. This book aims to provide you with answers to these questions so you can stop wondering what to do and get busy doing it.
When the web came along in the mid-90s, it transformed the behavior of customers and remade whole industries. New powerhouses like Amazon and Google arose and challenged established media and bricks-and-mortar retail companies. Even as Tower Records, Borders, and dozens of newspapers succumbed to the pressures of the digital revolution and the internet marketplace, other major players of that era—such as Home Depot and The Wall Street Journal—learned to thrive by capitalizing on the web’s vast new marketing reach and economies of scale.
AI has been called the greatest invention in human history—greater than fire, greater than the Industrial Revolution.1 That’s hyperbole, but the magnitude of the change is indeed vast. AI is “reinventing the way we invent”2 and will usher in a new era of human capabilities.
We are at an inflection point in human history.
That’s all well and good, you say. AI will change everything really soon—I think—but meanwhile my customers want lower prices while venture-funded startups are trying to steal those customers from me, my call center agents are overloaded with support calls, my competition is getting more challenging . . . and it takes my organization weeks to onboard new products. Meanwhile, inside the company, my employees continually complain that they “can’t find their stuff.” How are we going to take advantage of all of this great new technology if they can’t locate the marketing plan that worked so well last quarter or they have to recreate procedure documents yet again because they don’t know which ones are up to date?
The question for realizing the promise of AI is, How do you get there from here? At the 30,000-foot level, there are books explaining how AI will solve world hunger, while others are saying it will throw half of all humans out of work. At the other end of the spectrum, there are technical books that require a PhD in data engineering to understand. My intention is to be more helpful. This book will help you address practical issues and show you how to make the incremental changes that will prepare your company to deliver AI’s promise of revolutionary change. It will help you take the necessary steps down the path to transforming how your customers relate to your company. I will help you avoid career-limiting errors and I will show you how to set your BS detector for AI claims to “high.” I will help you catch this wave, rather than be crushed by it. This book contains a number of practical approaches for solving information problems. Without them, your AI will not work the way it needs to for your organization.
To understand what it will feel like to be a customer in this new world, let’s look in on a day in the life of one of those customers, an executive of a hypothetical manufacturer using hypothetical products from well-known brands, just a few years from now. (While Merrill Lynch, Grainger, and Expedia are real companies and may be working on chatbots, the bots in this scenario are invented representations of a fictional possible future.)
LIFE IN AN AI-POWERED WORLD
Allen Perkins feels powerful.
It is January of 2024. Perkins is the senior manufacturing manager for Hecker Heavy Locomotives, an Ohio company with a growing reputation for making the world’s highest-quality passenger train locomotives. Perkins feels powerful not because of the massive machines he helps to build, but because of the web of information that accommodates his every decision, whether at work or at home. His adept management of artificially intelligent resources has earned him a promotion, and now he must deliver.
As his self-driving Tesla heads for his office, Perkins can use the time to focus on his personal finances. The markets have been volatile, and he wonders if he should make any changes in his portfolio.