How should boards evaluate consequential AI?
Boards should evaluate whether the institution knows where consequential AI is operating, who authorized each material use, what decisions the systems may influence, what evidence is preserved, how exceptions and incidents are handled, who owns the outcomes, and whether governance performance is visible through board-level measures.
Boards should evaluate whether the institution knows where consequential AI is operating, who authorized each material use, what decisions the systems may influence, what evidence is preserved, how exceptions and incidents are handled, who owns the outcomes, and whether governance performance is visible through board-level measures.
A list of AI tools or an annual policy review does not establish that the institution can control or defend consequential AI decisions in operation.
- A board-approved risk appetite and governance mandate.•
- Named executive and outcome owners.•
- Production-authorization, escalation, and stop authority.•
- Evidence coverage for consequential decisions.•
- Measures for safety, security, performance, adoption, value, and unresolved exposure.•
Board oversight is not model selection. It is assurance that the enterprise has decision rights, operational controls, evidence, and accountable ownership appropriate to the risk.
Accountable authority: The board establishes oversight expectations; management owns execution; named clinical and functional leaders retain delegated authority.
See TTIC's Finance Leadership and Investor Diligence resources, and TTIC's board-level standards of learning. Review the public evidence basis. Review the practitioner and standards context. Return to the Decision Governance library.
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