Most boards have now had the conversation about artificial intelligence in the abstract. Far fewer have turned it into something operational: a clear view of where AI is used in the business, who is accountable for it, and how the company will meet the obligations the EU AI Act imposes. AI governance is the bridge between a board’s good intentions and a company that can actually demonstrate compliance – and it is increasingly what regulators, customers and investors expect to see.
Why AI governance is a board issue
AI is no longer confined to a data science team. It sits in recruitment tools, customer service, credit and risk decisions, marketing, fraud detection and the everyday productivity software employees use without a second thought. That spread makes AI an enterprise risk – legal, reputational, operational and ethical – and enterprise risk is the board’s responsibility.
The EU AI Act sharpens this. It assigns obligations by risk level and by role, backs them with significant penalties, and, importantly, imposes an AI literacy duty that requires organisations to ensure staff dealing with AI systems have an adequate understanding of them. That duty has applied since February 2025 and it points directly at governance: a company cannot ensure literacy it has never organised.
The building blocks of an AI operating model
An AI inventory
Governance begins with knowing what you have. An AI inventory lists every system that uses AI, what it does, what data it processes, which business function owns it, and whether the company is a provider or a deployer of it. Most organisations that build one are surprised both by how many systems it contains and by how many arrived through ordinary software updates rather than deliberate procurement.
Risk classification
Each system should be mapped to the AI Act’s categories: prohibited practices, high-risk uses, limited-risk systems subject to transparency duties, and minimal-risk systems. High-risk uses – recruitment and worker management, access to essential services, credit and certain others – carry the heaviest obligations and deserve the closest attention. The classification drives everything that follows.
Clear accountability
Someone must own AI governance. Whether that is a dedicated function, a cross-functional committee or an existing risk owner, the point is that responsibility is named rather than diffused. High-risk systems need identified human oversight – a person with the authority and information to question and, if necessary, override the system’s output.
Policies and controls
A workable AI policy states which tools are approved, what may and may not be entered into them, when human review is required before an AI output is acted upon, and how confidential and personal data must be handled. It should connect to the company’s existing data protection, security and procurement processes rather than sit beside them.
Where the AI Act meets the GDPR
AI governance cannot be separated from data protection. Most AI systems process personal data, so the GDPR applies in parallel: a lawful basis is needed, transparency obligations arise, data protection impact assessments are often required, and the rules on automated decision-making constrain systems that make significant decisions about people without meaningful human involvement. A governance model that addresses the AI Act but ignores the GDPR is only half built.
Practical example: from ad hoc to accountable
A mid-sized company discovers, when a customer asks how a decision was made, that several departments have independently adopted AI tools – a recruitment screen in HR, a chatbot in support, a scoring model in finance – with no central oversight. No one can say confidently which are high-risk, what data they use, or whether staff understand them.
The board commissions an inventory, classifies each system, assigns ownership, and puts a short policy and a literacy programme in place. Nothing about the underlying tools changes, but the company moves from being unable to answer basic questions to being able to demonstrate control. When the high-risk obligations bite, it faces a review rather than a scramble – and it can give customers, regulators and investors a straight answer about how it uses AI.
Common mistakes companies make
Treating AI governance as a one-off compliance project rather than an ongoing operating capability that evolves as tools and rules change.
Leaving governance entirely to the IT or data science team, when the risks are legal, ethical and reputational and belong at board level.
Overlooking AI embedded in third-party software, which is where much of an organisation’s real exposure quietly sits.
Ignoring the AI literacy duty, which already applies and is among the simplest obligations to meet and to evidence.
Building AI governance in isolation from data protection, security and procurement, producing duplicated effort and gaps between them.
Recommended actions
Start with the inventory and the risk classification, because everything else depends on knowing what you have and how the AI Act treats it. Assign clear ownership and, for high-risk systems, identify genuine human oversight.
Put a concise, usable AI policy in place and connect it to existing governance rather than bolting on a parallel process. Deliver AI literacy training to the people who work with these systems, and record that you have done so. Then review the model regularly, because both the technology and the regulatory timeline continue to move.
Frequently asked questions
Does the AI Act apply to companies that only use AI, not build it?
Yes. Deployers of AI systems have their own obligations under the Act, particularly for high-risk uses, including human oversight, using systems according to instructions, and informing affected people. Governance is how a deployer meets those duties.
What is the AI literacy obligation?
It requires organisations to take measures to ensure that staff and others dealing with AI systems on their behalf have an adequate level of understanding of them. It has applied since early 2025 and is best met through targeted, role-based training that is documented.
Do we need a separate AI committee?
Not necessarily. What matters is that responsibility is clearly assigned and that AI risk reaches the board. In some organisations a dedicated committee makes sense; in others an existing risk or compliance function can absorb the role.
Conclusion
AI governance turns the board’s awareness of AI into something the company can actually operate and demonstrate: a known inventory, a clear risk view, named accountability, workable policies and staff who understand the tools they use. The organisations that build this now will meet the AI Act’s deadlines calmly and will be able to answer the questions customers, regulators and investors are beginning to ask. Those that wait will build the same thing later, under pressure and in public.
Lawgent helps boards and management build practical AI governance – inventories, risk classification, policies, human oversight and literacy programmes – aligned with both the EU AI Act and the GDPR. Get in touch to turn your AI ambitions into an operating model you can stand behind.
