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Create an AI Council That Makes Decisions Instead of Holding Meetings
By Mason · Published · 2 min read
Editorial standards and correctionsAI governanceAI councilAI risk managementresponsible AIdecision frameworkAI accountabilitygenerative AI governance
Design an AI governance council with a narrow mandate, decision calendar, evidence packet, and measurable service levels.
An AI council should decide—not merely discuss. Its value comes from resolving portfolio, risk, ownership, and scale questions faster and more consistently.
This decision connects to Build an AI Strategy That Starts With Business Value and Turn Company Priorities Into an AI Opportunity Portfolio, which provide the strategic direction and portfolio context.
Use this four-part leadership framework
Mandate — Limit the council to portfolio choices, shared standards, high-risk approvals, and unresolved cross-functional issues.
Evidence packet — Require the same concise information for every decision.
Decision rules — Publish quorum, conflict, escalation, conditional approval, and emergency stop rules.
Service level — Measure decision time, rework, aging requests, and recurring blockers.
A practical decision example
A council meets twice monthly. Routine low-risk work follows published guardrails; only material exceptions arrive. Each agenda item ends with approve, approve with conditions, return for evidence, decline, or escalate. This is a hypothetical example; use your own baseline, constraints, and evidence.
Evidence, governance, and responsible use
Use the NIST AI Risk Management Framework to connect the initiative to governance, context, measurement, and ongoing management. The companion NIST AI RMF Playbook turns those functions into questions leaders can assign and review.
The GAO AI Accountability Framework is useful for examining governance, data, performance, and monitoring across the system life cycle. Compare the plan with the OECD AI Principles, particularly transparency, robustness, accountability, and respect for people affected by the system.
For generative AI, review the NIST Generative AI Profile and test representative cases using OpenAI’s evaluation guidance. Use the OWASP Top 10 for LLM Applications to discuss application threats before a model can access sensitive information or take actions.
Check performance statements against the FTC’s guidance on AI claims, and review information handling with the FTC’s privacy and security resources. For a broader organizational management approach, study the ISO/IEC 42001 overview.
Take this to the next leadership meeting
Write a one-page charter and a standard decision packet covering value, data, AI role, affected people, tests, controls, owner, cost, and requested decision.
Record the owner, evidence source, decision date, and what would cause the company to stop. The goal is not to make the document look complete. The goal is to make the next decision explicit, measurable, and accountable.
Continue with Give Every AI Initiative an Executive Sponsor and an Operating Owner. Use Run a Quarterly AI Portfolio Review: Keep, Fix, Scale, or Stop to revisit the decision with current evidence.
About the author
Mason
I help businesses replace manual processes with practical AI systems—and show what changed, what it cost, and what results improved.
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