AI Governance

Name the outcome and its boundary

Connect the intended use to affected people, systems, data and an accountable decision. An inventory becomes useful when it helps someone decide what may proceed, what needs review and what should stop.

Keep provider claims separate from the organisation’s deployment and controls. A named standard or collected document does not itself establish certification or compliance.

Compare different causal paths

Compare process redesign, changes to an existing system, bounded rules, AI assistance and automation. Each option needs a reason it could change this piece of work, and assumptions that can be tested.

Keep the workload, acceptable quality, human review, integration and ongoing costs visible. A modelled capacity gain becomes financial value only under explicit conversion conditions.

Design a test that can change the decision

• Record the evidence needed, its owner and the decision it can change.
• Define human approval, escalation and the trigger for another review.

Agree the metric, baseline, threshold, observation source and person responsible for the next decision. A result may support a bounded pilot, more evidence, redesign or stopping.

Keep a testable decision record

Metric and baseline

Record the evidence needed, its owner and the decision it can change.

Threshold and failure signal

Agree what would change the decision before observing results. Include unacceptable quality, rights or operating cost, and the evidence that would disprove the hypothesis.

Measurement and responsibility

Name the observation source, period, reviewer and person who accepts the outcome. Preserve assumptions separately from observed results.

Next action and review

Decide whether to test, redesign, expand or stop. A further operating step requires its own permissions and evidence; revisit it when the context changes.

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