An AI pilot should start with a recurring task whose result can be checked: drafting a response, classifying a request or finding information in an approved knowledge base. Assign a process owner and describe which actions the system performs and which remain with the employee. Before development, record the current effort required and typical errors. This gives the pilot a clear question: does it improve the chosen process once the time spent checking and correcting the result is included?
Prepare data and an evaluation sample
Collect examples of ordinary cases and exceptions: short requests, ambiguous wording, incomplete data and questions outside the task’s scope. For each example, define an acceptable result with a subject matter expert. Separate materials for tuning from those used for final evaluation so that the team does not assess quality only on familiar examples. Separately agree on the data to be used, access, processing location and retention period. An external service and internal infrastructure both need to be assessed against the specific information and project conditions.
If the system answers using documents, record the permitted sources, their versions and update procedures. Requiring a reference to the passage used is helpful so that an employee can check the answer. Provide a state in which the system reports that it lacks sufficient grounds for an answer and passes the task to a person. Assess reliability through the content and source: coherent text can contain a mistaken interpretation. For answers that affect an organization’s decisions or commitments, the process owner should define the review procedure particularly clearly.
Measure usefulness alongside costs
Select metrics to fit the task. For classification, missed relevant categories and incorrect assignments matter; for drafts, the extent of edits and the time required to prepare the final response; for search, whether a verified result is found. Also measure employee time, processing costs and the share of cases referred for manual review. Set criteria for continuing the pilot in advance. Your approach to identifying and assessing risks can be supplemented with the voluntary NIST AI Risk Management Framework, adapted to your process.
A useful first launch uses a limited stream of work with mandatory employee review. Record the model version, settings, sources used and result evaluation so that the causes of errors can be examined. Check difficult cases separately from the average metric: a rare but substantial error may require the workflow to change. In the working interface, provide ways to correct an output, reject a suggestion and pass the task through the normal process. Employees need to understand the tool’s limits and remain responsible for the final action.
Make a decision based on the pilot results
At the end, compare metrics with the original process and describe where the benefit is confirmed and where changes are needed. Possible decisions include expanding the stream of work, retaining assistance for only some cases or stopping the experiment. Before scaling, assign an owner for the evaluation sample and define update and quality reassessment procedures. Changing the model, instructions or document base may affect the result. A verifiable pilot gives the organization evidence for a decision and establishes a basis for subsequent operation.