Numerical scenarios are illustrative unless a source is linked. Validate operational, legal, financial, and regulatory decisions against current primary guidance and your own baseline.
Governance should enable speed
Weak governance blocks AI or drives it underground.
Good governance lets teams move faster because the rules are clear:
which tools are approved
which data can be used
which outputs require review
what evidence must be shown
who approves risky actions
how incidents are handled
how performance is measured
NIST's AI Risk Management Framework is a useful reference because it treats AI risk management as an ongoing process, not a one-time checklist.
For companies operating in or selling into the EU, the AI Act timeline also matters. The European Commission notes that AI literacy obligations applied from February 2025, GPAI rules became applicable in August 2025, and transparency rules come into effect in August 2026. High-risk timelines continue to evolve under the AI omnibus process, so companies should monitor scope and obligations carefully.
Generated vs accepted work
The most practical governance rule:
Generated work is not accepted work.
Generated work is what AI produces. Accepted work is what the company is willing to use, send, publish, file, or commit to a system of record.
Every AI-native workflow needs an acceptance step. Acceptance can be automatic for low-risk cases if deterministic checks pass. It can require human approval for customer-visible or regulated work. It can require multi-party review for high-risk decisions.
The point is to make acceptance explicit.
Eval design
An eval is a repeatable way to judge AI output quality.
Start simple:
Output type
Useful eval
Summary
Completeness, source faithfulness, no unsupported claims
Extraction
Schema validity, field accuracy, missing data flags
