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AI PilotProduction ReadinessEvaluation

Independent AI Pilot Review Before Go-Live

3 min readThomas Stermole
Thomas Stermole holds a checklist beside an open laptop.

Your AI pilot works in the demo. As you prepare to roll it out to a team, one question remains: can you rely on its behaviour in day-to-day work? An independent technical review is worth considering when you see a specific quality issue but have yet to establish its cause or its impact on release.

When an independent review is useful

An internal assistant sometimes cites outdated policies. An agent takes additional actions after receiving an ambiguous result. Business and IT teams disagree about whether the same answer passes. In each case, focus the review on a specific workflow and identify the findings that would block release.

If the cause and fix are clear, such as an outdated index with an assigned owner, resolve that issue first. External review is more valuable when several causes are plausible, the team needs a second opinion or the consequences are difficult to assess.

How to scope the review

Choose one workflow that matters for the rollout, such as answering questions about service contracts. Provide an approved test environment, representative cases, relevant user roles and a business owner who can judge whether the results are correct. Use test data that covers the business rules, access boundaries and exceptions you expect in production.

Review questionEvidence to request
Are answers reliable?Representative questions, expected sources, business review and traceable failure cases.
Do access controls hold?The same scenario run with different test roles, including a case where access has changed.
What happens when something fails?Recorded behaviour when evidence is missing, a request times out or an integration is unavailable.
Can a fix be verified?Repeatable test cases with recorded model, index, configuration and data versions.

The NIST Generative AI Profile covers documented pre-deployment testing. See the AI pilot failure-path acceptance tests for examples of partial failures and safe handoffs.

What should the review deliver?

  1. Release. The agreed workflow meets the criteria for the intended user group.
  2. Limit the rollout. Release a smaller scope that meets the criteria, such as internal search with citations. Keep features with unresolved risks disabled until they pass their retests.
  3. Hold release. Document reproducible critical findings, with an owner, a fix and a retest for each.

Agree in advance which findings block release. A high average quality score can hide a critical failure, such as a test account receiving restricted material. The company owns the final release decision.

Release decision for a reviewed workflow

  1. 01

    Does the workflow meet the agreed release criteria?

    • Yes: release

      Release the agreed scope.

    • No: narrow the scope or hold

      Release only a smaller scope that passes. Retest blocking findings.

Request an independent review

Describe what already runs, who will use it and the two failure patterns that concern you most. In the first conversation we can define the workflow and identify the test access and evidence needed for a decision.

Discuss an AI production readiness review

Next step

Sounds relevant for your company?

In a no-obligation initial call, we clarify within 30 minutes whether and where getting started is worthwhile for you — honestly and without sales pressure.

Request an initial call