AI operating evidence
AI value measurement is useful when it answers a service or operating question and changes a decision.
A count of prompts, runs, or outputs is not a business result by itself. The measure needs a definition, source, owner, context, and review action.
State the work or decision AI is expected to support, the intended user or service outcome, the boundary of the claim, and the condition that would make the workflow worth continuing or changing.
The AI operating model design route provides an internal path when value evidence needs ownership and review.
Name the source, definition, time period, reviewer, decision authority, and follow-up action. Record where evidence is incomplete.
Activity can show use or operation. Outcome requires a documented relationship to the service question and a defensible interpretation. Avoid converting a convenient proxy into a claim without support.
Review corrections, exceptions, support questions, user workarounds, access events, human review, unresolved cases, and service-owner decisions where those signals fit the workflow.
Revisit the measure when the model, data, prompt, tool, user group, workflow, consequence, or support boundary changes. Retire measures that no longer answer a useful question.
No. The useful measure depends on the service question and may include quality, control, user effort, decision clarity, risk handling, or supportability.
When its source, definition, context, or relationship to the service question no longer holds.
Connect the question, source, owner, operating decision, review cycle, and change record.
Bring the outcome, measure, or evidence question that needs a clearer operating model.
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