Measuring AI Results: From Pilots to Proof

Every funded pilot eventually meets the same meeting. The money was approved, the tool was used, the team has stories, the dashboard has colour — and someone with the right to ask says, plainly: is it working? Most pilots cannot answer, not because nothing happened, but because nobody decided beforehand what working would mean, what it would be measured against, or what evidence would settle it. This course teaches measurement as a sponsor's discipline: claims made testable, baselines taken before the change, outputs separated from outcomes, every cost counted — licenses, time, review burden, and rework — confounders named, quality and risk standing beside every speed measure, real adoption measured in depth and breadth, and kill, fix, and scale criteria written before launch. Then the harder half: the evidence pack a board can rely on, the review meeting held to a verdict, what you championed killed when the criteria say to, and bad news reported upward straight, early, and whole. The course works on your real pilot throughout: bring one you must judge, anonymized, and leave with its plan written or its verdict signed.