From AI pilot to production

A pilot can appear impressive in a demonstration while leaving basic questions unanswered. Who selected the examples? Were the source documents current? What happens when a user asks an unexpected question? Before a wider rollout, define acceptance evidence that reflects the task as people actually perform it.

For a knowledge assistant, that might include source relevance, answer completeness, and appropriate acknowledgement of missing information. For document processing, it might include field-level correctness and a clear route for exceptions. A single overall score can hide important differences between ordinary and difficult cases.

A prototype often operates on a static collection of information. A production application must account for updates, access permissions, failed ingestion, and the availability of connected services. Those conditions can change what a user sees even when the model itself remains unchanged.

Map the dependencies and decide how the experience behaves when one is unavailable. A tool should not imply that an action succeeded if the receiving system rejected it. It should also preserve enough context for an operational owner to investigate a problem without exposing unnecessary information.

The value of a pilot should be reviewed alongside the work required to keep it useful. Source maintenance, exception review, monitoring, support, and evaluation all consume time. Model and infrastructure usage can vary with task volume and the design of the workflow.

A practical business case compares these responsibilities with the improvement the application is intended to create. If the pilot reduces preparation time but requires substantial correction, that should influence the next design. The decision to expand should be based on the combined experience, not only the model’s ability to produce an answer.

Before progressing, confirm that the use case still has a clear business owner and a defined audience. Review evaluation results with representative users. Document known limitations and the conditions under which the tool should escalate or stop.

Finally, confirm how changes will be released and who owns the operating responsibilities. A production plan is stronger when these decisions are part of the handover rather than questions left to the first users.

• Representative evaluation cases and acceptance criteria
• Approved information and access boundaries
• Failure handling and human escalation
• Content, deployment, and support ownership