Why that matters for AI review
I know how to tell the difference between a plausible answer and a dependable one.
Audit and senior accountancy work are built around challenge. Does the evidence support the conclusion? What has been omitted? Are the assumptions reasonable? Is the explanation internally consistent? Would I be comfortable relying on this output in the real world?
Those are fundamentally useful questions when reviewing AI systems, particularly in finance and professional-services use cases where reliability, oversight and evidence matter.
Evidence & challengeReview before reliance.
Used to testing claims against evidence, reconciling inconsistencies and probing what sits behind a conclusion.
Controls & riskThink about failure modes.
Comfortable identifying where a process can go wrong, what needs oversight and where human review still matters.
CommunicationClear feedback, not technical noise.
Experienced in explaining complex findings to clients, teams and decision-makers in straightforward language.
Commercial judgementUnderstand the user and objective.
I care whether a system is useful, trusted and practical — not simply whether it can produce an impressive demo.