AI governance often breaks down for a simple reason:

Too many people are involved, and nobody is completely sure who owns the decision.

Legal may think Risk is responsible.

Risk may expect the AI Governance team to make the call.

Technology may assume the business owner has final accountability.

And the business may be waiting for everyone else to approve the system.

The result?

⏳ Slower decisions
🔁 Duplicated reviews
📄 Unnecessary documentation
👥 Senior leaders pulled into routine questions
🚧 AI initiatives sitting in approval queues

The problem is not always a lack of governance.

Often, it is a lack of clear decision rights.

That is where an AI Governance RACI becomes valuable.

A traditional RACI identifies who is:

Responsible — performs the work
Accountable — owns the outcome or final decision
Consulted — provides expertise or challenge
Informed — needs visibility into the decision

Straightforward in theory.

Much harder in practice.

AI governance cuts across functions that look at the same system through very different lenses.

💼 The business cares about value and operational use.

💻 Technology understands how the system works.

⚖️ Legal interprets regulatory obligations.

🛡️ Risk evaluates exposure.

📋 Compliance assesses whether required controls are being followed.

🔐 Privacy and Cybersecurity evaluate data and technical risks.

🏛️ AI Governance creates the enterprise framework that connects those perspectives.

Without clearly defined roles, the default often becomes consensus.

And consensus is not the same thing as accountability.

There is no single RACI that will work for every company.

Industry, regulatory exposure, organizational structure, and AI maturity all matter.

But a scalable operating model should generally distinguish between:

Business ownership
Governance coordination
Technical responsibility
Independent challenge
Executive escalation

Here is one practical way to structure it.

Primary role: Accountable for the business use of the AI system

The business owner should typically own:

This matters because AI governance cannot become something the business simply hands to Legal, Risk, or Technology.

The people deploying the system should remain accountable for how it is used and what outcome it produces.

Primary role: Responsible for technical implementation

Technology teams should typically own:

They provide much of the evidence needed to govern the system.

But they generally should not be the sole decision-maker on whether a particular use of AI is acceptable.

Primary role: Responsible for the governance process

A central AI Governance function can coordinate:

Its job is to make governance consistent, repeatable, and scalable.

That does not necessarily mean it should approve every AI system.

If the AI Governance Office becomes the approval authority for everything, it can quickly become the bottleneck it was created to prevent.

Primary role: Consulted on legal interpretation

Legal should provide guidance on issues such as:

Legal expertise is essential.

But requiring Legal to approve every AI use case is rarely scalable.

Organizations should define which questions actually require legal judgment and which can be handled through established policy, standards, and precedent.

Primary role: Independent challenge and oversight

Risk and Compliance may:

The distinction matters.

The first line should generally own the risk.

The second line should provide independent challenge.

When those roles blur, accountability can quietly shift away from the business.

Primary role: Specialist review when specific risks are triggered

Not every AI system should require the same Privacy or Cybersecurity review.

Their involvement should depend on characteristics of the system.

For example:

🔐 Does it process personal or sensitive data?

🌍 Does data cross jurisdictions?

🛡️ Does the system create new attack surfaces or third-party dependencies?

📊 Is sensitive information being used for training, retrieval, or inference?

The RACI should therefore be trigger-based, not simply committee-based.

Primary role: Accountable for enterprise-level governance decisions and material exceptions

An AI Governance Council should not become a weekly queue for approving individual AI systems.

Its highest-value role is making decisions such as:

The Council should own decisions that shape the governance system, not decisions the governance system should already be capable of handling.

One of the easiest ways to weaken a governance model is to assign multiple parties as Accountable.

It feels safer.

Usually, it creates the opposite effect.

If four functions are accountable for a decision, nobody really is.

An organization should be able to answer one question:

If this decision turns out to be wrong, who was ultimately responsible for making it?

If the answer is unclear, the operating model probably is too.

Another common mistake is building one governance process for every AI system.

A low-impact internal productivity tool should not necessarily go through the same decision structure as an AI system influencing:

The RACI should change based on the characteristics and potential impact of the system.

Factors might include:

📊 regulatory classification
👥 potential impact on individuals
🔐 data sensitivity
🤖 level of decision autonomy
💰 financial exposure
🌐 external deployment
🧠 model complexity
↩️ reversibility of harm

Lower-risk systems should move through a lighter process.

Higher-risk systems should trigger additional expertise, oversight, and escalation.

That is how organizations create proportionate governance instead of simply more governance.

Good governance should not eliminate friction.

Some friction is intentional.

Organizations should slow down when an AI system could materially affect people, create significant financial exposure, or introduce substantial regulatory risk.

But governance should eliminate unnecessary friction.

Teams should know:

✅ who owns the use case
✅ who performs the assessment
✅ who reviews specific risks
✅ who can make the decision
✅ when escalation is required
✅ who needs to be informed afterward

The objective is not to involve fewer people.

It is to involve the right people, at the right point, with clear authority.

That is the difference between having an AI governance committee and having an AI governance operating model.

For every material AI decision:

Who decides, who challenges, and who ultimately owns the outcome?

If answering that question requires scheduling another meeting, the RACI probably needs work.

If you are building or revisiting your AI governance operating model, start by mapping your highest-frequency AI decisions against the functions that currently review them.

Look for three warning signs:

Those are usually the first places where governance friction can be reduced without weakening oversight.

If this framework was useful, subscribe to AI Governance Briefing for frameworks on AI governance, operating models, classification, and enterprise implementation.

And if someone on your Legal, Risk, Technology, Compliance, or AI team is wrestling with ownership questions, forward this issue to them.

Boards do not need another 40-page AI governance report.

They need a small number of metrics that tell them whether AI risk is actually being identified, governed, and escalated effectively.

In the next issue, I will break down:

📊 the AI governance metrics that belong at board level
🚨 which indicators should trigger escalation
📈 how to balance risk reporting with AI value and adoption
🧭 what executives should be able to understand at a glance

Next: What Every Board Should See on an AI Governance Dashboard.

Disclaimer: This newsletter is provided for general informational and educational purposes only and does not constitute legal, regulatory, compliance, risk, or professional advice. Organizations should evaluate their specific circumstances and consult appropriate legal, compliance, risk, and other professional advisors when designing or implementing AI governance frameworks.