Why assessing AI should not stop at the output
When evaluating an AI adoption, most enterprises look only at the quality of the output. But a single output cannot tell you whether the system can be trusted inside your company.
A good result with an unexplainable process causes problems in internal approval and external reporting. Your organization must be able to answer "how was this document produced?" — which materials it relied on and what review it passed — so accountability can ultimately be established.
Evaluation should be split into three lanes. First, consistency: if the same instruction produces wildly different results, the system cannot be embedded into a real workflow. Second, provenance: verify that stated claims actually match their cited sources. Third, control: confirm that a user outside their scope is denied on unauthorized requests, and that the attempt is recorded.
AI evaluation should be designed as a repeating measurement of these three lanes — not a one-off pilot. Only then is it verified as work rather than a demo.