AI adoption needs more than a usage policy. It needs clear accountability, data rules, human oversight, risk classification, procurement controls and monitoring.
A practical AI governance model should define who may approve use cases, what information may be used, how outputs are verified, when humans must intervene, and how incidents are handled. Start by classifying use cases by consequence and data sensitivity. Low-risk productivity uses can move quickly; high-impact decisions require stronger review, evidence and auditability. Governance should enable safe adoption rather than block it.
Control 21 perspective
The practical question is not whether the concept is fashionable, but whether it improves decisions, delivery or operations. Control 21 approaches the topic through governance, implementation and measurable operating outcomes.
Next step
What should your organisation do next?
Describe the problem to AVA or speak directly with Control 21 about a scoped engagement.