Let's start with a confession: most AI governance models I've seen don't actually govern anything.
They exist as documents—impressive-looking charters with RACI matrices and approval workflows—that nobody follows in practice. Meanwhile, teams build AI applications however they want, and governance becomes something to retrofit when audit asks questions.
This isn't governance. It's documentation theater.
Real AI governance is an operating model: a set of structures, processes, and decision rights that shape how AI applications get built and deployed every day. When governance works, it accelerates development by providing clear guardrails. When it doesn't work, it either blocks everything or permits everything—neither of which is acceptable.
Here's how to build one that actually works.
The Three Failure Modes
Before designing the solution, let's understand what goes wrong:
Failure Mode 1: Governance as Gatekeeping
The committee meets monthly. Every AI initiative requires a 20-slide deck and executive approval. Decision timelines stretch to 6+ months. Teams either give up or work around the process.
Root cause: Governance is designed as risk avoidance, not risk management. The answer to everything is "wait" or "no."
Failure Mode 2: Governance as Checkbox
A form gets filled out. Someone signs it. The initiative proceeds. Nobody reads the form after it's submitted. When something goes wrong, the governance paper trail provides legal cover but no actual control.
Root cause: Governance exists to satisfy compliance, not to improve outcomes. It's a CYA mechanism.
Failure Mode 3: Governance as Aspiration
The governance charter describes a beautiful future state with AI ethics committees and real-time monitoring. None of it is implemented. Teams have no idea what they're supposed to do because the governance model is theoretical.
Root cause: Governance was designed without operationalization. It's a vision document, not an operating model.
The Operating Model Framework
A working AI governance operating model has four components:
1. Clear Decision Rights
Who can approve what, under what circumstances?
The fundamental question any governance model must answer is: who makes decisions? Most governance failures trace back to ambiguity here—either everyone thinks someone else is responsible, or multiple parties think they have authority and conflict.
Effective decision rights follow a tiered model:
Tier 1: Team-Level Authority
- Low-risk use cases with established patterns
- Internal tools without external exposure
- Experiments in isolated environments
- Approval: Use Case Owner + Technical Lead
Tier 2: Domain Authority
- Medium-risk use cases with some novelty
- Internal applications with sensitive data
- Customer-affecting features behind feature flags
- Approval: Domain leads (Data, Security, Legal)
Tier 3: Committee Authority
- High-risk use cases affecting decisions about individuals
- Customer-facing applications with real-time interaction
- Use cases in regulated contexts (healthcare, financial advice)
- Approval: Governance Committee
Tier 4: Executive Authority
- Strategic AI initiatives requiring significant investment
- Use cases with novel liability exposure
- Regulatory questions without clear precedent
- Approval: Executive Leadership + potentially Board
The key insight: 70-80% of use cases should flow through Tier 1 or 2. If everything requires committee review, you've built gatekeeping, not governance.
2. The RACI Nobody Ignores
Most RACI matrices are exercises in wishful thinking—everybody gets assigned to everything, and nobody is actually accountable.
Here's a RACI that works because it's specific about what each role actually does:
| Activity | Use Case Owner | Technical Lead | Data/Privacy | Security | Legal | Governance Committee | |----------|---------------|----------------|--------------|----------|-------|---------------------| | Use case definition | A/R | C | I | I | I | I | | Data requirements | R | A | R | C | I | I | | Privacy assessment | C | C | A/R | C | C | I | | Security review | C | R | C | A/R | I | I | | Legal risk assessment | C | I | C | C | A/R | I | | Go-live approval (Tier 1-2) | A | R | C | C | C | I | | Go-live approval (Tier 3-4) | R | R | R | R | R | A | | Post-deployment monitoring | A/R | R | C | R | I | I | | Incident response | R | A/R | R | R | R | C |
A = Accountable (one per activity—the decision maker) R = Responsible (does the work) C = Consulted (provides input) I = Informed (notified of outcome)
This RACI works because:
- Every activity has exactly one Accountable party
- Consulted and Informed are distinguished (reduces meeting load)
- Authority scales with risk tier
- Post-deployment accountability is explicit
3. Use Case Intake That Doesn't Suck
The use case intake process is where most governance models fall apart. It's either too onerous (30-page questionnaires that nobody completes honestly) or too superficial (a form that asks nothing useful).
Effective intake balances signal and burden:
Stage 1: Initial Screening (5 minutes) Answer three questions:
- What business outcome does this use case enable?
- Does this use case involve decisions affecting individuals (employment, credit, healthcare)?
- Is there customer-facing interaction?
If #2 and #3 are both "no," proceed to Tier 1 fast-track. If either is "yes," proceed to detailed assessment.
Stage 2: Detailed Assessment (30-60 minutes) For use cases requiring more scrutiny:
| Category | Questions | |----------|-----------| | Data | What data sources are used? Any PII? Any external API calls? | | Model | Is this a fine-tuned model or API-based? What's the hallucination risk? | | Human Loop | What human oversight exists? Who reviews outputs? | | Blast Radius | If the model fails, what's the worst case? Who's affected? | | Compliance | Any regulatory requirements? Audit trail needs? | | Exit | What's the rollback plan? How do we turn this off? |
Stage 3: Classification Based on assessment, assign risk tier and required approvals.
The entire intake process—from initial screening through classification—should take less than one week for 80% of use cases. If it routinely takes longer, the process is the problem.
4. Metrics That Drive Behavior
What gets measured gets managed. Most governance models measure the wrong things (number of reviews conducted, forms completed) rather than outcomes that matter.
Metrics that actually improve governance:
| Metric | Target | Why It Matters | |--------|--------|----------------| | Intake-to-approval cycle time | <2 weeks (Tier 1-2), <4 weeks (Tier 3-4) | Governance should enable, not block | | Use case rejection rate | 5-15% | Too low = rubber stamp; too high = gatekeeping | | Post-approval issue rate | <10% | Approved use cases should actually be ready | | Shadow AI detection rate | Decreasing | If shadow AI is increasing, governance is failing | | Governance process NPS | >0 (positive) | Teams should find governance helpful, not painful |
Track these monthly. Share them transparently. When metrics trend wrong, diagnose root cause before assuming teams are non-compliant.
Building the Governance Committee
The governance committee is either the engine or the bottleneck of AI governance. Here's how to build an effective one:
Composition
- Chair: CTO or VP Engineering (someone with technical credibility and organizational authority)
- Members:
- Chief Data Officer or Data/Analytics Lead
- Chief Information Security Officer or Security Lead
- General Counsel or Legal representative
- Compliance/Risk representative
- Business unit representative (rotating)
- AI/ML Lead (technical advisor)
Not included: Everyone who wants to be included. The committee should be 5-8 members max. Larger committees don't make better decisions—they make slower decisions.
Cadence
- Standing meeting: Bi-weekly, 60 minutes
- Async approval path: For urgent items between meetings
- Emergency convene: Same-day for significant incidents
Rule: If the committee can't address an item within two weeks, the committee is the bottleneck. Fix the process.
Authority
The committee should have:
- Final approval authority for Tier 3-4 use cases
- Policy-setting authority for governance standards
- Escalation resolution authority for cross-functional disputes
- Exception-granting authority (with documented rationale)
The committee should NOT have:
- Operational authority over implementation
- Individual accountability for use case outcomes (that stays with owners)
- Unilateral authority to change strategic AI direction (that's executive domain)
Operationalizing the Model
A governance model exists on paper. An operating model exists in practice. Here's how to bridge the gap:
Phase 1: Pilot (Weeks 1-4)
- Select 3-5 use cases across different risk tiers
- Run each through the intake and approval process
- Time every step; identify friction points
- Collect feedback from participants
- Adjust processes based on learning
Phase 2: Expand (Weeks 5-8)
- Open governance process to all new AI initiatives
- Publish intake process, decision criteria, and RACI
- Train domain leads on their approval authority
- Begin measuring governance metrics
- Handle first escalations and exceptions
Phase 3: Optimize (Weeks 9-12)
- Analyze metrics; identify systematic issues
- Refine decision criteria based on case patterns
- Automate intake steps where possible
- Document precedents for common scenarios
- Establish continuous improvement rhythm
Phase 4: Sustain (Ongoing)
- Monthly committee review of metrics and exceptions
- Quarterly process review and optimization
- Annual policy refresh based on regulatory/industry evolution
- Continuous training as personnel change
Handling the Hard Cases
Every governance model will face cases that don't fit neatly:
The Urgent Business Need
"We need this in production by Friday for the board meeting."
Response: Urgency doesn't eliminate risk. Governance can expedite review but can't skip assessment. If expedited review isn't possible, the business decision is whether to proceed without governance sign-off (with explicit executive acceptance of risk) or delay.
The Executive Override
"The CEO wants this; just approve it."
Response: Executive sponsorship doesn't change risk profile. Document the executive sponsor, ensure they understand the risks, and proceed with appropriate controls. The governance record should reflect both the approval and the risk acknowledgment.
The Innovation Pilot
"It's just an experiment; we don't need full governance."
Response: Experiments need scope limits, not governance exemptions. Define what "experiment" means (no production data, no external users, time-boxed, specific success criteria) and create an expedited path that maintains control.
The Third-Party Tool
"We're just using Vendor X's AI feature; we didn't build anything."
Response: Third-party tools introduce AI risk regardless of who built them. Governance should cover AI capabilities in procurement and integration, not just internal development.
The Cultural Dimension
Operating models are made of people, not processes. The governance model will fail if:
- Teams see governance as punishment. Governance should help teams navigate complexity, not create obstacles.
- Governance becomes turf protection. Each function should enable decisions, not veto to preserve authority.
- Speed and control are framed as tradeoffs. Good governance improves both; bad governance sacrifices both.
Building a governance culture requires:
- Leadership consistently reinforcing that governance enables speed
- Governance team helping teams succeed, not catching them failing
- Transparent decision-making that builds trust
- Celebrating use cases that navigated governance successfully
What Success Looks Like
When AI governance works:
- Teams proactively engage governance because it helps them
- Decision cycle times are measured in days, not months
- Shadow AI decreases because the sanctioned path is faster
- Incidents are rare, and when they occur, accountability is clear
- Regulators see a coherent, documented governance program
- The organization can scale AI deployment with confidence
This isn't utopia—it's achievable within 90-180 days of focused effort.
The alternative is governance theater: impressive documents that everyone ignores, until something goes wrong and everyone scrambles to explain why the governance model didn't prevent it.
Build the operating model. Make governance work.
Suleman Khalid is the founder of Fortera Labs, specializing in AI governance, modern delivery transformation, and secure automation for regulated industries. He previously led GenAI programs at Freddie Mac with 9+ years of enterprise technology experience.
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