An AI pilot should answer a practical question: can this approach improve a specific task enough to justify using it? Begin with the work and the evidence you need, rather than choosing a model and looking for a place to use it.
Choose a bounded task
Pick a task with a clear input, output and owner. Examples to investigate include extracting fields from documents, finding relevant internal guidance or preparing a draft for someone to review.
Compare the idea with an ordinary search feature, a rule or an existing tool. AI is one possible approach, not a requirement for every automation.
Establish a baseline
Describe how the task is handled today. Record the time involved, the common errors and how someone decides that the result is good enough. This gives the pilot something meaningful to compare against.
For a document-extraction task, an evaluation might check whether the correct fields are returned, whether missing information is flagged and whether a reviewer can verify each result.
Set data and action boundaries
Decide what information the system may access, who may use it and which providers may process it. Use data you are authorised to use. Agree retention and access arrangements before connecting business records.
Separate producing a suggestion from taking an action. A pilot that drafts an email does not need permission to send it. A pilot that proposes a database change does not need unrestricted write access.
Test representative and difficult examples
Include normal inputs, incomplete information, misleading instructions and cases outside the intended scope. Decide when the system should ask for clarification, decline or hand the task to a person.
Keep some examples separate from the ones used to tune the approach. Review correctness, response time, cost and the effort required to check the result. Record failures so improvements can be compared against the same cases.
Plan a limited rollout
Start with suitable users, clear review responsibilities and a way to stop or fall back to the previous process. Observe errors and repeat the evaluation when a model, prompt, data source or integration changes.
The NIST AI Risk Management Framework provides broader guidance for organisations managing AI risk. A small pilot should still make its purpose, limits and evaluation explicit.
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