Most AI programs cannot prove they paid off. That is the trap this episode is built to help you avoid — a portfolio of pilots that all demo well, and nothing you can defend when the budget review comes.

Chris Bradley takes on the number that reset the enterprise AI conversation — a widely-cited MIT study finding 95% of pilots delivered no measurable return — and locates the real cause. Much of what is sold as agentic AI is generative AI wearing an agent title. It advises. A person still completes the work. And the promised time savings were never structurally possible.

His frame is two kinds of leverage. Decision leverage sharpens one person's judgment — real value, capped at one seat. Labor leverage completes operational work across the tens or hundreds of people who do it — order entry, financial operations — and that is where return becomes provable. He walks the two measurement instruments the Lab uses: the time study for labor value, and value stream mapping for the larger prize — revenue sitting in an order backlog, cash held up by credit-and-rebill delays, margin leaking one pricing error at a time.

He closes on the half of the equation leadership owns: directing freed capacity into growth and customer service — Efficiency AI funding Opportunity AI — and a four-move method for valuing any use case before anyone writes a business case.