An AI pilot is useful when it resolves a specific question, not when it simply demonstrates that a model can produce an impressive answer. Start with a workflow where the current friction is visible and a better outcome can be described.
Start with the system, not the slogan
Choose a narrow task. Identify the user, the current process, the acceptable failure modes and the decision the pilot should inform. Avoid giving a prototype access to broad datasets simply because those datasets are available.
Make the invisible explicit
Create an evaluation plan before testing. Define representative examples, what a good answer looks like, what must trigger human review and what failure would make the approach unsuitable. Include latency, cost, privacy and user effort alongside answer quality.
Validate the assumptions
Set data and access guardrails. Classify what the experiment may use, who can see results, how outputs are retained and what users must be told. Treat prompt injection, leakage and over-reliance as risks to investigate—not as problems that disappear with a better prompt.
A successful pilot gives a team evidence: what is feasible, where the limits are, which safeguards are required and whether the next investment is justified.