Many AI pilots never finish: they start without a baseline, run on sample data and end in a demo instead of a decision. A pilot is only useful if everyone agrees in advance what result would justify going further. These four steps keep the pilot short, honest and tied to a decision leadership actually makes.

Step 1 — Choose the workflow

Goal: Pick a pilot that can prove something.

  1. List candidate workflows with volume, pain and available data.
  2. Pick one with a named owner and a measurable baseline.
  3. Measure the baseline before any AI is involved.
  4. Check the data can be used under your AI policy.

You finish with: One workflow, an owner and a measured baseline.

Step 2 — Agree the criteria

Goal: Decide in advance what counts as success.

  1. Write the success criteria against the baseline.
  2. Write stop criteria, such as accuracy or effort thresholds.
  3. Set a time box of a few weeks.
  4. Name who makes the final decision.

You finish with: Written success and stop criteria, a time box and a decision maker.

Step 3 — Run with real users

Goal: Test on real work, not a demo.

  1. Use real data and the people who do the work.
  2. Keep a person reviewing every output.
  3. Log errors, edits and time spent.
  4. Hold a short weekly check against the criteria.

You finish with: Pilot results measured against the baseline.

Step 4 — Decide and record

Goal: End with a clear decision.

  1. Compare results with the success and stop criteria.
  2. Decide to scale, change or stop.
  3. Record the decision and the reasons.
  4. If scaling, plan the move to production with owners and dates.

You finish with: A recorded decision and, if scaling, a production plan.

Common pitfalls

  • Starting without a measured baseline.
  • Agreeing success criteria after seeing the results.
  • Running on sample data instead of real work.
  • Letting the pilot run on with no decision date.

See it as a workflow

Leadership

An AI pilot with a go or no-go decision

Run a short, time-boxed AI pilot on one real workflow, with success and stop criteria agreed up front, so leadership ends it with a clear decision to scale, change or stop instead of an open-ended experiment.

See the workflow

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