Move generative AI governance into the workflow by defining source permissions, evaluation evidence, human review, change ownership, and operating telemetry.
Govern the workflow, not the model in isolation
A model is only one part of an enterprise generative AI service. The real control boundary includes the user task, source information, retrieval path, tools, permissions, interface, human review, and the action taken from an answer.
Leaders should begin with a bounded workflow and name its owner, users, information sensitivity, unacceptable failures, and point of human authority. Model selection becomes one design decision inside that operating context.
Build evaluation evidence before broad access
Plausible output is not the same as useful output. A representative evaluation set should include ordinary questions, ambiguous requests, missing evidence, outdated sources, permission boundaries, adversarial prompts, and cases that require escalation.
The evaluation should test retrieval and citation quality as well as final wording. Results need owners, thresholds, and a release decision so prompt or model changes cannot bypass the evidence expected from other production services.
Operate cost, quality, access, and change together
Once released, the service needs telemetry for usage, latency, cost, retrieval failures, unsupported answers, user challenges, and source changes. These signals show whether the workflow remains useful and where review effort is accumulating.
A practical operating model assigns responsibility for source ingestion, permissions, evaluation, prompt and model changes, incident response, retention, and user feedback. Governance then becomes a repeatable service discipline rather than a launch review.