What is true, useful, and meaningful?: A leadership lens for AI

Artificial intelligence confronts leaders with a distinctive challenge. It is not simply a new tool, but a class of systems that increasingly act, adapt, and participate in decisions once governed by human judgment alone.

As a result, executives face a growing stream of consequential choices. They must decide which tools or copilots to roll out across the organization; which AI-supported processes, products, or ideas to pilot; which experiments to scale and which to abandon; and where to intervene top-down by redesigning processes, offerings, or entire areas around new AI capabilities. These decisions are recurring, interdependent, and often made under conditions of uncertainty and time pressure.

Yet discussions surrounding these choices often feel urgent but unsatisfying. Technical assessments emphasize benchmark performance and rapid capability gains; business cases highlight productivity, scale, and competitive advantage; ethical debates raise questions of accountability, professional judgment, and human agency. Rather than converging, these conversations frequently talk past one another.

The difficulty is not a lack of intelligence, information, or intent. It is that leaders are often trying to answer different kinds of questions at the same time, without fully recognizing how different those questions are.

In practice, decisions about AI tend to conflate three distinct conversations: what is true, what is useful, and what is meaningful. Each demands a different form of evidence, reasoning, and judgment. Each becomes distorted when answered through the lens of the others.