How to scope an AI pilot that actually proves value
Most AI pilots fail not because the model is weak, but because nobody defined what success looks like. Here is how to scope one that produces a real answer.
A good pilot is narrow on purpose. Pick one department, two recurring workflows, and a measurable baseline you can compare against in four weeks — first-draft time, review pass rate, or the number of tools a task currently touches.
Tie the pilot to work that already happens every week. A campaign brief, a proposal draft, or ticket triage is a far better test than a generic "try AI" announcement, because the team can feel the difference immediately.
Decide the review step before you start. Every workflow needs a named reviewer and a clear definition of "good enough to ship." This is what separates a governed rollout from shadow AI usage.
At the end of the pilot, you should be able to answer one question honestly: did this make a repeatable task faster or better, without creating new risk? If yes, expand by workflow and department. If no, you learned cheaply.