What an AI pilot can actually tell you

Start with the job

An AI pilot is easier to interpret when its job is written down. “Help the team work faster” is too broad to explain what happened. A more useful question identifies the task, the people doing it and the point at which a result is considered acceptable. The pilot can then be read as evidence about that bounded workflow.

Compare the starting point

Ask how the task was done before the trial. Did the comparison include similar material, similar deadlines and the same review standard? A time-saving claim means less when the new process is compared with an unusually difficult earlier example. You do not need to invent a performance number to explain why the baseline matters.

Count the work after the first answer

A draft can arrive quickly while checking, correction and rework take longer. NIST’s AI risk-management resources are useful context for examining performance within a real use setting, rather than treating a model output as the whole outcome. They do not certify an individual pilot. [I01] Ask which steps were measured and which remained outside the report.

Try a hypothetical case

Imagine an assistant preparing short meeting summaries. It handles routine meetings well but omits a qualification when the source is ambiguous. This example is illustrative. A useful evaluation would include the review effort and the ambiguous cases, not only the clean final summaries. The question becomes whether the overall process helps a defined team do a defined job.

Choose the next question

Before repeating a success claim, ask what was tested, what was compared and what happened when the tool failed. A small pilot can justify another carefully designed test without proving a company-wide transformation. Keep that distinction in the headline and in the conclusion.