How to design AI pilot projects that actually inform decisions

Clarify one narrow problem for the pilot

An AI pilot should start from a single, clear problem in daily work. Pick a task that is repetitive and painful, for example replying to common support emails or drafting internal reports. Describe the problem in one sentence that your colleagues recognize. Then decide which part of the task the AI should help with, for example draft first version, suggest options, or classify inputs. Avoid broad goals like “improve productivity” and describe specific outcomes such as “cut time per ticket by 30 percent” or “reduce manual data copying”. This keeps the pilot focused and easier to evaluate.

Time box the pilot and lock the scope

Set a fixed start and end date for your AI pilot, typically four to eight weeks. A clear time box creates urgency and protects you from endless tinkering. Before you start, agree on what will and will not be included, for example one team, one language, one channel. Document this in a short pilot brief that everyone sees. During the pilot, you can fix small issues, however you avoid adding new use cases or teams. If you discover big new ideas, you capture them for a future pilot. This discipline keeps the test manageable and makes results easier to interpret.

Make the pilot testable with simple metrics

A useful AI pilot has a small set of metrics that you can measure with low effort. Focus on three things: time saved, quality of output, and risk or errors. For time, you can ask a few users to track how long the task takes with and without AI for a sample of cases. For quality, use simple ratings like “usable as is, needs light edit, unusable”. For risk, track issues such as incorrect data or policy breaches. Define in advance which data you will collect, how often, and who is responsible. Simple, consistent data beats complex dashboards that no one updates.

Define success and kill criteria before you start

Before you launch, decide what “good enough to scale” and “time to stop” look like. Success criteria could be numbers, for example average 25 percent time saved, or clear patterns, for example most users rate outputs as usable with light edits. Kill criteria protect you from wishful thinking. Examples are repeated serious errors, no sign of time savings after a set number of trials, or strong user resistance that does not improve. Write these criteria in plain language and agree on them with stakeholders. This turns the pilot into a real test instead of a showcase.

Plan decision making and next steps upfront

An AI pilot only has value if it leads to a clear decision. Before you start, decide who will make that decision and when. Plan one short review meeting shortly after the pilot ends. In that meeting, you compare the agreed metrics, success and kill criteria, and user feedback. Prepare three possible outcomes: scale the solution with a clear plan, repeat a modified pilot to fix a specific issue, or stop and document what you learned. Communicate this to the team so they know the pilot is not just a tech experiment but part of a real business choice.

Useful links

• NIST AI program — Background on AI risk and evaluation principles from a standards body.
• OECD AI — High level guidance on trustworthy and effective AI use in organisations.

Keep learning