Translating AI into Business Value

At Boston Consulting Group, he was the technical guy who helped teams think about data and analytics to solve client problems. When he moved to Gartner and joined the leadership team for their data and analytics consulting practice, the people around him were more technical than he was. That made him the business guy. “It was the same me,” says Moore, who is currently an associate clinical professor at the University of Chicago, “but in one environment I was the technical person, and in another I was the business person who knew technical stuff.”

That dual perspective runs through everything Moore brings to the Chief AI Officer (CAIO) program at the University of Chicago’s Booth School of Business Executive Education, where he teaches a course on building AI strategy rooted in business objectives. In a program that draws both technical professionals and business leaders looking to move into AI leadership, Moore’s expertise solves for a key tension in the role by bridging the gap between what the technology can do and what the organization needs.

For Moore, the biggest mistake in AI leadership is starting from the technology. “Everybody’s jumping up and down going, ‘Agentic, agentic, agentic!’” he says. “But the CEO and the other CXOs aren’t thinking about agentic. They’re thinking, ‘I’ve got problems. I need to increase revenue. I need to decrease costs.’” The job of the CAIO, as Moore sees it, is to walk into the CEO’s office on day one and ask a single question: What’s keeping you up at night? Everything else lands downstream from that answer. He tells students that if the first words out of their mouths to a chief executive are “area under the curve,” they’ve already lost. While the technical fluency needs to be there, what matters is translating it into the language of business outcomes.

Moore’s insight is that, even if AI creates value by helping teams work faster or better, that’s not the same as capturing value. A copilot that saves time matters to the C-suite only if the organization can turn that efficiency into higher revenue or a better way of operating. Otherwise, the gain might be interesting, but it’s still unrealized.

This is the sort of approach Moore has for thinking about analytics more broadly. It involves seeing analytics as a spectrum starting with simple heuristic models and then extending to regression and machine learning, with more advanced AI arriving after that. This means that CAIOs who confine their scope to AI will inevitably face obstacles when they encounter problems that AI can’t solve. Even if a simpler model might work perfectly well, they’ll have no alternative to offer. “Usually, one of the first things you’ll hear is, ‘I don’t have the data,’ or ‘I don’t have clean enough data,’” Moore says. “So I start with a heuristic model, and then as I get the data and clean it up, I can build a regression model, and then eventually machine learning. It’s a progression over time.”

Data, Moore argues, is so intrinsic to the AI conversation that he describes data and AI as two sides of the same coin. What’s more, it’s continuously flipping, meaning you need data to build models, and you also need to know what models you want in order to know what data to collect. This leads most organizations to collect the data that’s easy, Moore says. Some of that data is useful, but about a third of it will be garbage. Typically, real investment is required to capture data that will make models valuable.

Just as important is where AI sits in the organization. Moore has seen it housed under marketing, operations, finance, and the CIO’s office. But with silos come challenges. If the CAIO reports to the CFO, building AI models for HR becomes a cross-departmental negotiation. Moore’s view is that the CAIO should report to the chief executive and support all the other C-suite functions. “As soon as they’re in one of those silos,” he says, “it becomes harder.”

In his course, Moore leaves students with three core tools: a framework for assessing organizational AI maturity across vision, strategy, metrics, governance, people, processes, and technology; a method for building an opportunity portfolio that scores potential projects on business impact and feasibility; and a structured approach to building and presenting a deck to senior executives. Each week builds on the last, so that by the final class students have a complete AI strategy rooted in their own organization that they present live. “I’ve had people come back and tell me they took those presentations directly to their leadership teams and got buy-in,” Moore says.

As a Chicago Booth alum himself, Moore sees the University as naturally positioned for this kind of program. The “Booth DNA,” as he describes it, is critical thinking applied to business problems. It’s an orientation that mirrors what Moore believes the CAIO role requires: less a technologist who happens to know business or a business leader who happens to know AI, more someone who can hold both and translate between them. With AI now table stakes, Moore says the question is understanding the problem it’s meant to solve. The CAIO Program is designed for leaders who want to make that connection.