AI Governance Operating Model
This example is generic and illustrative. It does not describe a real organization.
1. Operating Model Summary
| Field |
Value |
| Organization / team |
Example Enterprise AI Office |
| Scope |
Enterprise AI portfolio |
| Effective date |
2026-04-26 |
| Owner |
Example AI Governance Lead |
| Review cadence |
Quarterly |
| Primary frameworks referenced |
NIST AI RMF, ISO 42001, internal AI policy |
2. Governance Principles
- Accountability: every AI system must have a named business owner and technical owner.
- Traceability: release decisions must be linked to intake, risk assessment, validation evidence, and monitoring plans.
- Risk proportionality: controls scale by risk tier, with high-risk systems requiring formal sign-off and stronger oversight.
- Human oversight: human review is mandatory for high-risk decisions, sensitive-user impacts, and unresolved model uncertainty.
- Continuous improvement: incidents, drift findings, and post-release reviews feed back into governance templates and release gates.
3. Scope of AI Systems
In scope
- internal AI assistants used by employees
- customer-facing AI features
- AI-enabled routing, summarization, classification, or recommendation systems
- vendor-provided AI embedded into business workflows
Out of scope
- ordinary rules-based automation without model behavior
- personal experimentation not connected to business data or production workflows
System inventory expectations
Every AI system must record:
- named business owner
- named technical owner
- use-case description
- risk tier
- release status
- model or vendor dependency
- monitoring owner
- next review date
4. Governance Forums and Decision Rights
| Forum / role |
Decision rights |
Inputs required |
Outputs |
| AI Intake Forum |
accept, reject, request more information |
intake form, owner, use-case summary |
triage decision |
| Prioritization Forum |
rank, defer, escalate |
impact score, risk score, effort estimate |
prioritized AI portfolio |
| Release Gate Review |
approve, conditional, hold, reject |
validation evidence, risk assessment, release plan |
release decision and conditions |
| Monitoring Review |
continue, adjust, rollback, retire |
metrics, incident log, drift report |
improvement actions |
5. Lifecycle Controls
| Stage |
Minimum controls |
Required artifacts |
| Intake |
use-case definition, ownership, preliminary risk screen |
intake form |
| Prioritization |
value, risk, feasibility, dependency scoring |
prioritization matrix |
| Delivery governance |
milestone reviews, risk tracking, data governance |
project RAID, model card draft |
| Release readiness |
validation, legal/compliance review, monitoring readiness |
release gate review, checklist report |
| Post-release monitoring |
performance, drift, incidents, user feedback |
dashboard, incident log |
| Improvement / retirement |
periodic review, remediation, decommissioning decision |
improvement review or retirement note |
6. Risk Tiering
| Tier |
Definition |
Example controls |
| Low |
limited impact, internal use, reversible |
technical review, basic monitoring |
| Medium |
customer-facing or operationally significant |
governance review, model card, rollback plan |
| High |
regulated, safety-adjacent, hard to reverse, or high-impact |
legal review, human oversight, red-team testing, formal sign-off |
7. Metrics and CTQs
| Metric |
Purpose |
Owner |
Review cadence |
| AI system inventory completeness |
confirms traceability |
AI Governance Lead |
monthly |
| release gate pass rate |
tracks release quality |
Release Manager |
quarterly |
| incident recurrence rate |
tracks control effectiveness |
Operations Owner |
monthly |
| open high-risk actions |
tracks unresolved exposure |
Risk Owner |
weekly |
| time from intake to decision |
tracks governance flow efficiency |
Portfolio Lead |
monthly |
8. Escalation Rules
Escalate when:
- high-risk system lacks named owner
- release evidence is missing at a required gate
- a severity-1 or severity-2 AI incident occurs
- model behavior materially deviates from validated behavior
- privacy, legal, or safety concerns are unresolved
9. Continuous Improvement Loop
At each quarterly review, capture:
- which gate criteria were unclear or excessive
- which evidence was repeatedly missing
- which incidents indicate weak controls
- which templates should be standardized or simplified
- which systems should be re-tiered, remediated, or retired
10. Open Decisions
| Decision |
Owner |
Due date |
Status |
| Define escalation threshold for high-risk model drift |
Risk Owner |
2026-05-15 |
open |
| Select standard model-card format |
AI Governance Lead |
2026-05-30 |
open |
| Confirm quarterly monitoring review calendar |
Portfolio Lead |
2026-05-10 |
open |