Most companies are not blocked on models. They are blocked on data quality, ownership and governance nobody wants to own. The unglamorous assessment comes first — then deployment where it pays.
WHO THIS IS FOR
Boards and management teams under pressure to have an AI answer, and companies whose sponsors, customers or auditors have started asking governance questions they cannot yet answer.
THE PROBLEM
AI spending tends to start with a tool and work backward to a use case. The data is not ready, nobody owns the policy, and the first diligence question about model governance has no answer. The pilot works; nothing after it does.
A written read on data, governance and where automation would actually pay.
Policy, controls and audit trail put in place and documented to survive diligence.
Phase one delivered into production, with the teams trained to run it.
Engagements run on Genexis ARC, our four-stage method from readiness to run-rate. The six dimensions we score, the twelve questions we ask and how the result is stated are published in full on the AI readiness assessment page. Governance work is built against the NIST AI Risk Management Framework and ISO/IEC 42001 rather than a proprietary checklist, so the result holds up in diligence.
Scope and fees are set on a fit call, once the shape of the problem is clear.
AI readiness, governance and training implemented in large enterprise companies — the framework built, the teams trained, and the first phase put into production rather than handed over as a recommendation.
That is usually a sign ‘the problem’ is worth an hour of conversation before anyone scopes anything.
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