Quick answer
A good AI pilot checklist covers a single defined use case, the data and permissions needed, a baseline of current performance, an evaluation set with success thresholds, a fixed budget and timeline, named owners, risk and privacy review, and a decision meeting that ends in scale, adjust or stop.
Key takeaways
- One use case and one decision question per pilot.
- Success thresholds must be agreed before results are seen.
- Data access and privacy approval are often the longest steps.
- A pilot that ends in “stop” with evidence is still a success.
Who this guide is for
Leaders and project owners preparing a first or second AI pilot who want a clear go or no-go decision at the end.
Define the use case and decision
Write one sentence describing the task, user and expected improvement, such as “extract invoice fields for the accounts team to reduce manual entry”. Then write the decision the pilot must inform: whether to fund a production build. Pilots that try to explore several use cases rarely produce a clear answer. Agree what “good enough” means for the business, not just for the model.
Confirm data availability and permission
List the data the pilot needs, where it lives, who owns it and whether it can be used for this purpose. Check privacy obligations, customer contracts and internal policies. Prepare a representative sample, including messy and unusual cases. Getting approval and access frequently takes longer than building the prototype, so start it first.
Measure the baseline
Record how the task performs today: time per item, error rate, cost or response time. Without a baseline, you cannot say whether the pilot improved anything. Use the same measurement method later. If baseline data does not exist, take a short manual sample before the pilot starts.
Build an evaluation set and thresholds
Create a labelled set of examples with correct outputs, reviewed by domain experts. Agree thresholds in advance, such as minimum accuracy on critical fields and maximum acceptable error types. Separate errors that are inconvenient from errors that are harmful. Agreeing thresholds before seeing results prevents moving the goalposts.
Set budget, timeline and scope limits
Fix a budget ceiling covering build, model usage and staff time, and a timeline of a few weeks. Define what is in scope, such as one document type or one inbox, and what is explicitly excluded, such as production integration or mobile access. A pilot can use simplified infrastructure, but should use realistic data and real users for feedback.
Review risk, security and oversight
Identify what could go wrong: incorrect output acted upon, data leakage, bias against certain inputs, cost overrun. Decide human review steps for the pilot. Check model provider terms on data retention and training. Involve security and compliance early with a short summary rather than after the prototype is built.
Hold the decision meeting
At the end, present results against thresholds, cost per task, user feedback, risks found and an estimate for production. Choose to scale, adjust and re-test, or stop. Document the reasoning. Production planning should include integration, monitoring, evaluation in operation, support ownership and running cost, which are usually larger than pilot costs.
Scope checklist
- What single decision will the pilot inform?
- Is data access approved for this purpose?
- What is the current baseline?
- What thresholds define success?
- What is the budget ceiling and decision date?
Frequently asked questions
How long should an AI pilot take?
Many focused pilots run for a few weeks. Longer pilots often indicate unclear scope. Data access approval can extend timelines, so start it early.
What is the difference between an AI proof of concept and a pilot?
A proof of concept tests technical feasibility, often on sample data. A pilot tests usefulness with real users and realistic data, and produces a decision about production investment.
Why do AI pilots fail to reach production?
Common reasons include undefined success criteria, unrepresentative data, ignored integration and operating costs, missing ownership and lack of a decision process.
Prepared with AI assistance as an engineering decision guide. Examples are illustrative unless explicitly labelled as an internal implementation. No independent expert review or customer outcome is claimed.
Planning an AI assistant or document workflow?
Share the use case, information sources, and review requirements. Scope, access, dependencies and commercial terms are agreed before delivery. Do not submit passwords, API keys or confidential records.