Answer
A workable pilot starts with a baseline measured before the AI tool touches anything: average ticket resolution time, minutes spent searching for a procedure, or the backlog of documentation updates sitting untouched. Without that number, any post-pilot claim of improvement is just an impression. Pick a single team and a single workflow, run the tool for a defined window such as six to eight weeks, and compare the same metric under the same conditions rather than mixing in unrelated process changes that happened during the same period.
Buyers should also watch for a few common failure patterns. One is measuring activity instead of outcome, counting how many questions the AI answered rather than whether those answers actually reduced escalations or rework. Another is letting the success criteria shift mid-pilot once the original numbers look unimpressive, which makes the exercise impossible to evaluate honestly. A third is skipping the human-in-the-loop cost, since review time spent checking AI output still counts against any time savings claimed. The strongest pilots report a small number of concrete, boring metrics tied to one workflow, not a broad narrative about transformation, and they are willing to report a negative result if the tool did not move the number that mattered.