Short answer. AI governance is how an organisation decides who may use AI for what, what must be checked before an AI output is used, and who is accountable when it goes wrong. For a large company that means policies, committees and tooling. For a small team it can be a one-page rule set.
The four parts
- Policy: which tools are allowed, what data may be pasted into them, which uses are off limits.
- Roles: who approves new uses, who owns each AI-assisted process, who handles incidents.
- Controls: testing and review before outputs are relied on, including checks for errors and bias.
- Records: what was used, what was checked, what was decided.
Common models
Organisations tend to pick one of three shapes: a central committee that approves everything, a federated model where each team owns its own rules inside shared limits, or a lightweight approach based on risk tiers, where only high-impact uses get a formal review. Small teams usually do best with risk tiers.
Where a second-opinion step fits
Controls need a practical way to test an AI output. A second opinion from independent models is one such check: if models disagree on a fact, figure or conclusion, that points a reviewer to what to verify. It supports the control layer. It is not a policy, a role or a record system, and agreement between models does not prove an answer is true.
A minimal starting point
- Write down the tools allowed and the data that must never be pasted.
- Define which outputs are high impact and need a second opinion plus a human check.
- Name one person accountable for AI use.
- Keep a short log of high-impact checks.
What this article is not
It is not legal advice or a compliance guide. Regulations differ by country and sector; consult the relevant specialist.