A good AI pilot project is small enough to finish in a few weeks and important enough that the result matters. The goal isn't to prove AI is exciting — it's to learn, cheaply, whether one specific use is worth keeping.
Step 1: List repetitive tasks
Ask each team lead for three tasks that are repetitive, text-heavy and annoying. Common examples in service firms: drafting proposals, summarising calls, answering the same client questions, preparing weekly reports.
Step 2: Score and choose one
Rate each task on frequency, time spent, and risk if the output is wrong. Choose the task with high frequency, meaningful time, and low risk. One pilot, not five.
Step 3: Record a baseline
Before changing anything, measure how long the task takes and how often it needs rework. Without a baseline, you'll be arguing opinions at the end.
Step 4: Set boundaries
Decide which data the tool can see, what it is allowed to do, and where a person must review. Start with suggestions only — the tool drafts, a human approves.
Step 5: Run it for four to six weeks
Give the people involved time to learn. Collect examples of good and bad outputs. Hold a short weekly check-in.
Step 6: Decide with the numbers
Compare total time, quality, and staff feedback against the baseline. Then make a clear call: keep and expand, adjust and re-test, or stop. Stopping a pilot that didn't work is a good outcome — it saved you from a larger mistake.
Common mistakes
- Choosing a task nobody owns.
- Measuring only draft speed, not editing time.
- Connecting tools to sensitive systems on day one.
- Running several pilots at once and learning nothing clearly.
Our AI implementation roadmap shows how a successful pilot turns into a wider plan. To find the best starting point for your business, take the free AI Readiness Scorecard.