An AI system fails a compliance review long before a regulator, auditor or customer asks for evidence. It fails when the organisation cannot answer a basic question: who owns AI compliance? If Legal assumes IT has assessed the tool, IT assumes the business sponsor approved its use, and Compliance only learns about it after deployment, there is no governance. There is an undocumented risk acceptance.
For organisations operating under the EU AI Act, or building an AI management system aligned to ISO/IEC 42001, AI compliance cannot sit with one department in isolation. It requires a named accountable owner, defined operational responsibilities, and a record showing that decisions, controls and approvals were made at the right time.
The answer is not to create another committee with no authority. It is to establish a clear ownership model that follows the AI system from procurement or development through classification, deployment, monitoring and retirement.
Who owns AI compliance in practice?
The executive accountable for AI compliance should normally be the person with authority over the organisation’s risk posture. Depending on the organisation, that may be the Chief Risk Officer, General Counsel, Chief Compliance Officer or a formally appointed AI Governance Lead reporting into one of those functions.
That individual is accountable for ensuring an AI governance framework exists, is resourced and is effective. They should not personally classify every AI tool, test every model output or maintain every control record. Their role is to ensure the organisation has a defensible system for doing so, with escalation routes for material risks and decisions that need senior approval.
This distinction matters. Accountability cannot be delegated away, but responsibilities can and should be distributed. A business unit may own the intended use of a recruitment screening tool. IT may own its technical integration and access controls. Data Protection may lead on personal data risks. Information Security may assess suppliers and resilience. Legal and Compliance may interpret regulatory duties. Yet one senior owner must be able to state, with evidence, that the organisation knows what AI it uses and has governed it proportionately.
For high-risk AI systems, this is particularly pressing. The EU AI Act assigns specific obligations according to whether an organisation is a provider, deployer, importer, distributor or authorised representative. A company can occupy more than one role across its AI estate. Ownership therefore needs to be assessed system by system, not declared once in a policy document.
The ownership model that stands up to scrutiny
A workable model separates four layers: executive accountability, system ownership, control ownership and independent oversight. Confusing these layers is a common cause of weak governance.
Executive accountability
The accountable executive approves the AI governance policy, receives material risk reporting and ensures resources are available. Under ISO/IEC 42001, leadership must demonstrate commitment to the AI management system, establish policy and assign relevant roles and authorities. This is not ceremonial board sign-off. It means management can explain how AI risk is integrated into business governance.
The board does not need to approve every low-risk generative AI assistant. It does need visibility of the overall AI risk profile, high-risk systems, significant incidents, overdue remediation and strategic decisions such as whether to develop or deploy AI in regulated processes.
AI system ownership
Every registered system needs a named business owner. This person owns the purpose, scope and continued business justification for that use case. They should be able to answer: what decision or process does this system affect, who is affected, what happens when it fails, and why is AI necessary here?
The system owner is not necessarily the person who bought the software. Procurement may source the supplier, and IT may administer the account, but the business owner is responsible for ensuring the use remains within its approved purpose. If an HR team repurposes a general-purpose AI tool to rank candidates, that is a governance event, not a routine configuration change.
Control ownership
Controls need owners who can operate and evidence them. For example, the security team may own identity and access management controls; Data Protection may own data protection impact assessment processes; the model or product team may own performance testing and human oversight procedures; Compliance may own classification records and regulatory monitoring.
A control without an owner is merely a statement of intent. A control owner should know the required frequency, evidence standard, escalation threshold and dependency on other teams. This is where spreadsheets usually start to fail: they can list controls, but they rarely show whether the evidence is current, who approved an exception, or what has changed since the last review.
Independent oversight
Independent review prevents a system owner from marking their own homework. The second line, typically Risk, Compliance, Data Protection or a combined governance function, should challenge classifications, assess residual risk and track remediation. Internal Audit may then test whether the framework is operating as described.
Independence should be proportionate. A mid-market organisation does not need a large enterprise AI ethics office to create credible oversight. It does need a reviewer with sufficient authority and separation from the delivery team to challenge unsafe or non-compliant deployment.
Legal, risk and technology all have a role
Assigning AI compliance to Legal alone is tempting because the EU AI Act is a legal instrument. It is also incomplete. Legal can interpret obligations, define contractual requirements and advise on liability, but it cannot validate model performance, configure logging or monitor operational drift.
Giving ownership entirely to IT is equally weak. Technical teams can manage architecture, access, integration and supplier assurance, but they should not decide alone whether an AI use is lawful, fair, proportionate or appropriate for a sensitive decision.
Risk and Compliance provide the connective structure. They translate obligations into a repeatable workflow, ensure the organisation applies a consistent risk appetite, and maintain the evidence needed for assurance. But they need active input from system owners and technical teams. Compliance cannot govern an AI system it does not know exists.
This is why the AI inventory is the starting point. Before assigning ownership, an organisation must identify AI systems, intended uses, suppliers, data categories, affected people, deployment status and relevant regulatory role. An inventory is not an administrative catalogue. It is the control point that makes ownership visible.
Match responsibility to the AI lifecycle
Ownership should not end at approval. AI systems change through new data, model updates, altered prompts, additional integrations, expanded user groups and changed business purposes. A system classified as low risk at procurement can become materially different once deployed.
A disciplined lifecycle includes intake, classification, assessment, approval, implementation, monitoring, incident handling, periodic review and retirement. At each stage, the organisation should record who acts, who approves and what evidence is required.
For example, a business owner may initiate an intake record. Compliance and Legal may determine whether the proposed use falls within a prohibited practice, requires additional assessment or is likely to be high risk. Security, Data Protection and technical owners then provide specialist assessments. The accountable executive or delegated governance forum approves higher-impact deployments. After release, the system owner confirms ongoing use, while control owners monitor performance, incidents and changes.
The exact workflow depends on scale and risk. A small internal drafting assistant should not face the same process as an AI system influencing creditworthiness, employment decisions or access to essential services. Proportionate governance is not light-touch governance. It means applying controls according to potential harm, regulatory exposure and business criticality.
Make responsibility auditable, not aspirational
A RACI chart can be useful, but it is not sufficient on its own. Auditors and regulators will look for operational evidence: the named owner, the classification rationale, risk assessment, approvals, training records, control tests, change history and incident decisions.
For EU AI Act readiness, organisations should also ensure responsibilities reflect their role in the value chain. A deployer cannot assume that a supplier’s marketing claims amount to compliance evidence. Providers may need to supply documentation and instructions, but deployers still have duties around using the system according to instructions, human oversight, input data where relevant, monitoring and record-keeping. Contractual allocation of responsibilities matters, yet it does not remove statutory obligations.
ISO/IEC 42001 adds a management-system discipline. It expects organisations to define roles, manage documented information, assess risks and opportunities, monitor performance and improve over time. The useful question is not whether a policy says AI compliance is owned. The useful question is whether the organisation can produce the evidence within hours, not weeks.
A single system of record makes this practical. It links an AI inventory entry to classification decisions, assigned owners, control requirements, assessment findings, approvals, review dates and board reporting. Endaxi AIG is designed for this operating model: governance evidence is structured around the system being governed, rather than scattered across procurement folders, legal emails and disconnected spreadsheets.
Common ownership failures to avoid
The first failure is appointing a nominal AI owner with no budget, authority or access to the AI inventory. Accountability without decision rights is performative.
The second is treating third-party AI as the supplier’s problem. Most organisations are deploying externally built models and tools, but their own use cases, users, data and decisions still create compliance exposure.
The third is leaving ownership at programme level only. An enterprise-wide AI policy is necessary, but each system requires a named business owner and a current assessment. A central team cannot know whether a local team has changed a tool’s purpose unless the workflow requires disclosure and review.
The fourth is reporting activity rather than risk. Boards do not need a count of completed forms. They need to know which systems are high risk, where evidence is missing, which exceptions remain open, and whether controls are operating.
The most useful test is simple: select any AI system in use and ask who owns its purpose, its technical controls, its legal classification, its ongoing monitoring and its final risk acceptance. If the answers are immediate, named and evidenced, AI compliance has an owner. If they depend on a chain of assumptions, the governance gap already exists.