Most AI governance failures are not caused by a missing policy. They happen because nobody knows who is allowed to approve an AI use case, who checks data risks, who owns the model or agent after launch, who can stop it, or who decides whether its benefits justify its cost and risk. A RACI matrix makes those decision rights explicit.
The goal is not to create a committee for every chatbot. It is to match governance to impact. A low-risk internal drafting assistant may need a simple approval and acceptable-use check. An AI agent with access to customer data or production systems needs clear business ownership, security review, data controls, human-approval thresholds, monitoring, and an incident plan. The matrix below is a starting point that organisations should tailor to their size, industry, and risk profile.
What RACI means
RACI is a simple responsibility model:
- Responsible — does the work.
- Accountable — owns the outcome and makes the final decision; there should usually be one.
- Consulted — provides input before the decision or action.
- Informed — needs to know that the decision or action occurred.
The model is useful for AI because a single project can involve business operations, technology, data, security, privacy, legal/compliance, HR, product, procurement, and finance. Without defined roles, work either stalls while teams wait for one another, or moves quickly with important risks left unowned.
Start with the AI use case, not the org chart
Before filling in a RACI, document the use case in plain language. What is the AI system intended to do? Who will use it? Which decisions, customers, employees, data sets, systems, and vendors are involved? Does it generate suggestions only, or can it take action? What could go wrong, and who would be affected?
A clear use case prevents generic governance. “Use AI to improve service” is too broad. “Use an internal AI agent to summarise support tickets and suggest knowledge-base articles; no customer communication, system changes, or access to personal information beyond ticket content” gives the organisation something it can assess and govern.
A model RACI for AI governance
The matrix below assumes a medium-sized organisation. In a smaller company, one individual may hold several roles. In a larger company, roles may be divided into specialist functions. The important discipline is to preserve accountability even when titles change.
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| Set AI strategy and risk appetite | |||||||||
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| Approve high-impact AI use case | |||||||||
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| Define business requirements and success measures | |||||||||
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| Assess technical architecture and integration | |||||||||
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| Classify data and approve data use | |||||||||
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| Review security controls and access | |||||||||
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| Review legal, regulatory, and contract obligations | |||||||||
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| Conduct supplier due diligence | |||||||||
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| Approve production release | |||||||||
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| Define human-approval thresholds | |||||||||
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| Monitor quality, cost, and business outcomes | |||||||||
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| Handle AI incident and disablement | |||||||||
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| Review material changes or retirement | |||||||||
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This is a guide, not a prescription. In regulated, high-risk, or customer-facing use cases, legal, compliance, privacy, and security roles should be engaged early. In low-risk internal use cases, an abbreviated model can keep the process efficient.
The roles that are most often missing
The accountable business owner
Technology teams often build or configure AI systems, but they should not be the only owners of the business outcome. The business owner defines the intended value, confirms the process fit, approves the acceptable level of risk, and is accountable for whether the system remains useful after launch.
Without this role, projects become technology experiments that cannot answer basic questions: Why does this agent exist? Which customer or operational problem does it solve? What performance is good enough? Who decides when to stop using it?
The data and privacy owner
AI projects often start with “what data can we connect?” The better question is “what data is necessary, appropriate, and authorised for this purpose?” A data or privacy owner helps classify information, assess sensitivity, set retention boundaries, review vendor terms, and identify when additional impact assessment is needed.
This role is not only about compliance. Data boundaries reduce the risk of agents returning confidential information to the wrong person or creating a new, uncontrolled data store through prompts, logs, and exported outputs.
The person who can say “stop”
Every significant AI system needs a defined disablement authority. If an agent leaks data, acts outside its scope, generates unsafe output, becomes compromised, or incurs unexpected costs, someone must be able to suspend it quickly. That person needs access, a documented path, and a decision framework.
Do not rely on a vague escalation instruction. Decide whether the technical lead, security lead, business owner, or incident commander can stop the system, and under what circumstances. Test that capability before the system is relied upon.
Tailor the RACI to risk
A useful way to scale governance is to classify use cases into three broad levels.
Level 1: low-impact assistance
Examples include internal drafting, meeting summaries, coding support without access to sensitive production data, or research assistance using approved public information. These typically need an approved tool, acceptable-use rules, basic training, data restrictions, and a business owner. Security and privacy may provide guardrails rather than review every use case individually.
Level 2: operational decision support
Examples include internal knowledge assistants, customer-support drafting, sales research, workflow recommendations, document classification, or automated triage. These need clearer requirements, data review, testing, human approval thresholds, quality monitoring, and documented ownership. The agent may influence a process, but people retain meaningful control.
Level 3: high-impact or autonomous action
Examples include agents that write to business systems, influence employment or financial decisions, interact directly with customers at scale, access sensitive data, control infrastructure, or provide outputs in regulated contexts. These need formal approvals, deeper impact and security assessments, strong access controls, logging, incident procedures, periodic reviews, and executive visibility.
The classification process should be repeatable. It should not depend on who makes the request or how persuasive the initial demo looks.
How to introduce the matrix without slowing down work
Create a one-page AI use-case intake form. It should capture the problem, owner, users, data, systems, vendor, output, action authority, affected people, risk level, and intended launch date. The form triggers the right review path rather than forcing every proposal through the same heavy process.
For each risk level, define a standard “golden path.” A low-risk assistant can use an approved platform, preconfigured data restrictions, a standard disclosure, and a basic owner approval. A higher-risk agent uses a stronger template with dedicated identity, least-privilege access, logging, testing, and change control. Teams move faster because controls are built into the starting point rather than added after a prototype has already spread.
Centralised governance does not require centralised development. Business units can still build and experiment within approved patterns. What must be centralised is the ability to know which systems exist, who owns them, what they can access, and whether they remain within policy.
Practitioner detail: the five decisions that should always be documented
For every production AI system, document five decisions:
- Purpose: What business outcome is approved, and what uses are prohibited?
- Authority: Who is accountable, who can approve release, and who can disable the system?
- Data: What data may be used, what is prohibited, and how are prompts, outputs, and logs handled?
- Action: What can the system do automatically, and what requires human approval?
- Assurance: How is quality monitored, how are incidents handled, and when is the system reviewed or retired?
These decisions make governance tangible. They also make it much easier to investigate a failure, complete a customer security questionnaire, respond to an audit, or hand over responsibility when staff change.
Keep the matrix alive
A RACI is not a one-time workshop slide. Review it when the AI strategy changes, when a significant incident occurs, when a new regulated use case is proposed, or when a role changes. Include it in onboarding for managers, builders, and reviewers. Link each responsibility to practical workflows, templates, and evidence locations.
The real test is whether people can answer, without hesitation: “Who owns this agent? Who approved it? What is it allowed to access? Who can stop it?” If they cannot, the organisation has an AI governance gap regardless of how advanced the technology may be.
Need help turning AI strategy into clear operating roles, decision rights, and governed delivery? Managed Company can help establish a proportionate AI governance model, define responsibility matrices, align systems and processes, and give leadership a repeatable way to oversee AI investment and risk. Explore Fractional CXO support.
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