Move enterprise AI from pilot to everyday use.

A demonstration can show that a model produces a useful response. It rarely shows what happens when a document is out of date, a user lacks permission, the source is missing or the request falls outside the intended task. These are ordinary operating conditions, and they deserve a place in the design.

Our starting point is to describe the work without mentioning a model. Who needs to do what, using which information, and who accepts the result? A policy assistant, for example, needs a narrower purpose than “answer employee questions.” It might help staff find approved policies and identify the relevant section, while directing individual interpretations to the policy owner.

The technical team can keep an application available, but it cannot independently decide whether a business answer is acceptable. Assign a workflow owner who can define success, approve source material and resolve questions about scope.

Separate that responsibility from ownership of the data, the application and the final decision. In a small team one person may hold several roles. The useful step is making those roles explicit, including who can pause the workflow and who can approve a change.

Build a small, representative set of tasks before choosing a headline quality target. Include routine questions, ambiguous questions, missing information and requests the system should decline. Add examples in each language used in the workflow, with reviewers able to assess those languages.

Measure distinct things separately: whether the answer is supported, whether retrieval respects access, how much review is needed and how long the complete task takes. A fluent response can still fail on any of these dimensions. Compare the result with a documented baseline of the existing process.

Keep the evaluation set manageable and versioned. Record why each example belongs in it, the expected evidence and any acceptable alternative answers. This makes evaluation repeatable when a model, prompt or source collection changes.

Define what happens after release. Sources need owners and refresh rules. Model or configuration changes need evaluation. Users need a route to report an incorrect answer, and the team needs enough context to investigate it without collecting unnecessary sensitive information.

Start with a bounded group and a reversible workflow. Expand based on evidence from use, rather than the number of demonstrations completed. A useful first release might handle fewer tasks than the demo, but handle them with clearer expectations.

Use a short release record to bring the evidence together. Describe the user group, source set, evaluation results, known limitations and support route. The decision should explain why this scope is ready for use and what remains outside it. Give the business owner and technical owner a shared view of the same record.

Agree release conditions before the final demonstration. Examples include showing a valid source for supported answers, preventing retrieval of restricted documents and routing unanswered questions to an identified owner. Choose conditions that fit the workflow; a generic accuracy percentage can hide a failure that matters to the business.

Also define a pause condition. A permissions failure may require immediate suspension of the affected feature, while a formatting problem may be suitable for the next improvement cycle. Document who makes that distinction and how users will continue their work. A fallback is more useful when it has been tried by the people expected to use it.

Consider a team introducing an assistant for an approved policy library. The first release serves a defined employee group and helps people find the relevant policy passage. It leaves personal case decisions with the policy owner. That boundary makes it possible to evaluate a useful task with clear expectations.

Before release, the team checks routine questions, a question with no approved answer, a withdrawn policy and a request for a restricted document. Reviewers record the source returned, whether the answer is supported and whether the suggested next step is appropriate. They review the complete experience rather than a response in isolation.

During the initial use period, an owner reviews recurring questions and reported errors. A confusing source may need an editorial change; a missed passage may need retrieval work. Those are different improvements. Keep the original evaluation cases and add new ones so a change can be checked against both familiar and newly discovered conditions.

• Name the workflow owner and the person who accepts the output.
• Write the intended task and its explicit boundaries.
• Select approved sources and define their access and refresh rules.
• Agree evaluation examples, review criteria and a fallback process.
• Document the conditions for expanding, changing or pausing the release.

The enterprise AI readiness checklist turns these decisions into a working discussion. For a broader approach to lifecycle risk, see the voluntary NIST AI Risk Management Framework.