Choose a baseline you can repeat
Before introducing the tool, observe how the task is completed today. Record a representative sample of effort and the quality checks already required. Avoid measuring only the easiest examples. Include enough variation to expose the exceptions that consume time. A baseline does not need to be elaborate, but it should describe the same task you will measure after the pilot.
Count the review effort
AI may produce a first draft quickly while moving work into verification. Measure the full process: preparation, generation, review, correction and final use. Ask reviewers to record why they changed an answer. Repeated errors can reveal a missing source or an unsuitable task. A saving is meaningful when the final usable result takes less effort without lowering the standard required for the work.
Track quality separately from speed
Define what makes an output acceptable. For a summary, that might include correct names, important facts and an accurate next action. For a classification task, review misrouted cases. Keep a small evaluation set so changes can be compared over time. Do not let a single average hide a serious failure category. The acceptable error profile depends on what the system is being asked to do.
Make a decision, not an endless experiment
Set a review date and decide whether to expand, revise or stop. Include staff feedback and ongoing operating cost in the decision. If the result is promising, widen one part of the scope and evaluate again. If the benefit is unclear, investigate whether the workflow itself needs simplification. A pilot is successful when it produces a useful decision, even if that decision is to choose a different approach.
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