Most organisations do not have a complete absence of AI ownership. They have the opposite problem: a great many people own a part of it.
IT owns the technology. The data organisation owns the data. Information security assesses the security. Legal and compliance examine the regulation. The business develops use cases. HR handles the workforce implications. The executive team approves the investment.
Everyone can perform their own task well, and the whole can still have no owner. Responsibility for an individual task is not the same thing as responsibility for the outcome.
The title is not the point
Organisations sometimes try to solve this by naming a person. CIO, CTO, CDO, Chief AI Officer, head of data, head of digital.
This can be organisationally useful, but a title does not resolve governance. The mandate does.
If the person responsible for AI cannot influence investment decisions, prioritisation, shared principles or risk limits, they do not in fact own the whole. They coordinate it. Real ownership shows up in decision rights: who gets to choose, prioritise, approve, set boundaries, and — when necessary — say no.
So the useful test is not “who is responsible for AI?” but whether the organisation can answer, clearly and without hedging: what are we trying to achieve with AI, where are investments directed, how are use cases prioritised, which risks are accepted, what the organisation will not do, who is accountable for the business benefit, who can stop a use case, and when a matter escalates to a higher decision level.
If those answers are unclear, the organisation may have a lot of AI activity and very little AI management.
Governance is a decision system, not a rulebook
Governance is sometimes read as administration, which brings to mind policies, committees, approval processes and documentation. Those can be part of governance, but they are not what it is.
Governance exists to make sure the right decisions are made at the right level, on the basis of the right information, in a way that keeps accountability identifiable.
Compressed, it is five questions. Who decides? Where do they get the authority to decide? On what basis is the decision made? When must a decision be escalated? How do we know afterwards what was decided and what happened?
Note what is not on that list: whether the regulation requires it. A company needs governance for AI use cases that carry no specific obligations under the EU AI Act either — because investment, risk and decision rights have to be managed whether or not the law demands a particular control.
Do not build a parallel system
The instinct, once this is recognised, is to build an AI governance structure alongside the existing one. That is usually a mistake.
The company already has a strategy process, investment decision-making, risk management, internal control, information security, procurement and technology management. AI governance should be integrated into those wherever possible.
AI does raise questions that conventional technology management does not always catch: uncertainty in outputs, models and services that change underneath you, the effect of data and environment on performance, bias, the human tendency to trust an automated recommendation, the limits of more autonomous operation, third-party dependencies, and the traceability of decisions.
These need deliberate controls. They rarely need a separate hierarchy.
Integrate first. Separate only where the existing decision and control structures genuinely fail to cover AI’s specific risk well enough.
This piece draws on Strateginen tekoäly — hallituksen ja johdon kysymykset (Aamu Editions, 2026).