The common procurement mistake is to treat AI governance as a policy-writing exercise that can be completed after the platform decision. The stronger sequence is to define the operating evidence before comparing vendors.
For one representative workflow, name the human actor, the AI Worker if one is involved, the permitted source, the approved model route, the action class, the approval point, and the record that must remain. A system that cannot answer at that level is not ready for production authority, however fluent its demo may be.
US organizations face a distributed evidence environment: HIPAA enforcement in healthcare, SEC cybersecurity disclosure requirements for public companies, FTC scrutiny of AI claims and data practices, state AI laws, and cyber-insurance underwriting. EU organizations face a different legal structure. Both routes lead a buyer to the same practical question: can the operating record be retrieved and reproduced?
Ask the vendor to run one control in the evaluation tenant, not in a prepared video. Then retrieve the corresponding policy decision, approval event, actor attribution, model route, and outcome. Repeat the test with a different user or source. The result should follow the configured authority, not the wording of the prompt.
A static framework map can help a committee organize its review, but it is not organizational certification and it is not proof that a control operated. The useful evidence is time-bounded, deployment-specific, and linked to the workflow being evaluated.
The decision does not need a fear narrative or a penalty figure. It needs a reproducible test, a named reviewer, and a clear rejection rule. That is enough to separate a documented claim from a demonstrated capability.