Operational controls that turn AI governance intent into accountable practice.
Openness can improve inspectability, control and portability, but only when an organisation can own the supply chain, operation and evidence it inherits.
Review should follow material changes in purpose, authority, data, users, dependencies, evidence or incidents—not an arbitrary calendar alone.
An AI inventory becomes useful when it identifies deployed systems, their context, owners, dependencies and review triggers—not merely a list of model vendors.
A centre can concentrate scarce expertise and reusable controls, or become an approval queue detached from real work. Choose the mandate before the structure.
Boards do not need to review prompts or model scores. They need a clear account of what each agent may do, whose mandate it carries and how control is recovered.
Buy evidence, controllability and lifecycle obligations—not a confident demo or an unrestricted promise of autonomy.
Reviewable agent decisions depend on a small chain of artefacts connecting workflow purpose, authority, configuration, evidence, findings, controls and ownership.
Effective oversight gives a prepared person the signal, authority, time and interface needed to prevent or limit a defined AI failure.
Design an AI workshop backwards from a named decision, then capture evidence, assumptions, disagreements, owners and triggers in an artefact that survives the meeting.
Finance agents can prepare, reconcile and investigate, but posting, paying and attesting require deliberately separated authority.
Enterprise readiness scores hide the conditions that make one workflow viable and another reckless. Assess readiness where data, authority, people and operations meet.
A technical review can trace data and test controls, but it cannot alone determine lawful basis, resolve policy choices or certify compliance.
KEEP EXPLORING
Read the method, assumptions and questions to ask.