This article by Seth Earley, author of The AI-Powered Enterprise, was published on the Harvard Business Review website on April 28, 2020.

Every big company now manages a proliferation of sites, apps, and technology systems for interacting with buyers and managing everything in the business, from customers and clients to inventory and products. These systems are spitting out data continuously. But even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use that data to improve customer service, reduce costs, and speed the core processes that pro­vide competitive advantage.

AI was supposed to help with that. But as an executive at a major life insurance company recently told me (Seth), “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.” Why?

My 20 years of experience working with companies on their information technology have shown me the reason: because promises of AI vendors don’t pay off unless a company’s data systems are properly prepared for AI. Data is locked in silos, inaccessible, poorly structured, and most importantly, not organized in such a way as to be used as the fuel that makes AI work. Instead, to reap the benefits of AI, companies need to create something called an ontology, a comprehensive characterization of the architecture of all of its data.