There is a reasonable explanation for the more than 30-point gap. Technology teams tend to focus on metrics they can directly measure and influence — user adoption, model performance, productivity gains, process automation, cycle-time reduction, system utilization, platform stability. CEOs and directors, by contrast, evaluate AI through the lens of broader enterprise outcomes, including revenue growth, profitability, margin expansion, customer experience, competitive differentiation, risk reduction and shareholder value. When they cannot see how AI affects these outcomes directly, confidence declines. Both groups are looking for evidence of success, but through fundamentally different lenses.

The implications are real. If executives cannot agree on whether AI is creating value, it becomes difficult to sustain investment, prioritize initiatives and scale adoption.

In my conversations with technology and business leaders, AI adoption is no longer the primary challenge. Demonstrating value is. It’s one thing to automate X number of tasks to improve employee productivity (activity metrics); it’s another thing entirely to demonstrate how AI is helping retain customers, reduce product defects, accelerate sales conversion rates or improve profitability (business impact metrics). I believe closing the gap between AI adoption and business value realization begins with technology leaders asking a basic question: Why are we doing this and how will we measure it?