What leaders need to decide
Governance should make responsible delivery faster by clarifying decisions before a team is under pressure. The goal is a workable operating model, not a shelf of policies that nobody can apply during an exception or incident.
Practical AI governance operating model
| Control area | Decision to define | Operational artifact |
|---|---|---|
| Decision rights | Who approves scope, risk, and release? | Named accountable owner and escalation path. |
| Data boundaries | Which inputs are allowed, restricted, or prohibited? | Data classification and retention rules. |
| Evaluation | How will quality and safety be tested? | Acceptance criteria, test set, and review cadence. |
| Human oversight | Which actions require review or override? | Approval thresholds, fallback behavior, and audit trail. |
| Vendor management | What model or platform commitments are acceptable? | Security, portability, and contract decision record. |
Governance requirements should be proportionate to the workflow impact and the consequence of a wrong answer.
A practical sequence
- 01
Start from consequences
Classify what happens when the system is wrong, unavailable, biased, insecure, or used outside its intended workflow.
- 02
Design human review deliberately
Specify the conditions for review, the reviewer’s authority, and what information they need to make a useful decision.
- 03
Operate the controls
Review outcomes, incidents, exceptions, and model or workflow changes on a regular cadence with named owners.
Move from analysis to a 90-day plan
When your team needs a prioritized portfolio, architecture direction, governance boundaries, and a first pilot scope, the AI Opportunity & Execution Roadmap turns this framework into an implementation-linked engagement.
Explore the AI Opportunity & Execution RoadmapFrequently asked questions
Does every AI initiative need the same governance?
No. Governance should match the impact of the workflow. A low-risk internal drafting aid and an automated decision affecting customers require very different controls.
Who owns AI governance?
The accountable business owner owns the outcome. Technical, security, privacy, legal, and risk partners contribute controls, but governance fails when ownership is treated as someone else’s function.