The Accountability Gap
Ask most organizations who is responsible for AI compliance and the answer reveals an uncomfortable truth: nobody knows, or everybody thinks someone else is handling it. The CTO assumes the General Counsel is monitoring regulatory developments. The General Counsel assumes the CTO is managing technical compliance. The CHRO assumes AI-assisted hiring tools are the vendor’s compliance responsibility. And the board assumes management has it covered.
This diffusion of assumed responsibility is the most common and most dangerous governance failure in AI compliance. It persists because AI compliance is new, because the regulatory landscape is still evolving, and because no traditional role was designed to own it.
What the Regulations Actually Require
The EU AI Act, which began phased enforcement in 2025 with high-risk system requirements taking effect in August 2026, is the most comprehensive AI regulatory framework in the world. For organizations that deploy high-risk AI systems — including AI used in employment decisions, credit scoring, educational assessment, law enforcement, and critical infrastructure — the Act requires specific accountability measures.
Organizations must establish a quality management system for AI, conduct conformity assessments for high-risk systems, maintain technical documentation that includes training data descriptions, model architecture, performance metrics, and risk assessments, implement post-market monitoring to detect model drift and emergent failures, and report serious incidents to regulatory authorities.
These are not aspirational guidelines. They are legal obligations with enforcement mechanisms, including fines of up to 35 million euros or 7 percent of global annual turnover for the most serious violations. Someone at the organization must own compliance with these requirements. If that person has not been designated, the organization is exposed.
Why the Existing Structure Does Not Work
The three most common candidates for AI compliance ownership each have significant limitations. The General Counsel understands legal obligations but typically lacks the technical fluency to evaluate AI systems, assess model risk, or oversee technical documentation. The CTO understands the technology but typically views compliance as a legal function and lacks regulatory expertise. The Chief Compliance Officer understands compliance frameworks but may not have the AI-specific knowledge needed to adapt those frameworks to AI systems.
None of these leaders is wrong for the role. But none is fully right, either. AI compliance sits at the intersection of technology, law, and risk management in a way that does not map cleanly onto any existing executive portfolio.
Three Models for AI Compliance Ownership
Organizations have adopted three models. The first is a dedicated Head of AI Governance or AI Compliance Officer who reports to the General Counsel, Chief Risk Officer, or CAIO. This model provides the clearest accountability and the deepest expertise, but requires a new hire and budget commitment.
The second is a cross-functional AI compliance committee with a designated chair. The committee includes representatives from legal, technology, risk, and relevant business units. The chair is accountable for the committee’s output and serves as the organization’s point of contact for AI compliance matters. This model works for organizations that are not yet ready for a dedicated hire but need structured accountability.
The third is to expand an existing role — typically the Chief Compliance Officer or Deputy General Counsel — to include explicit AI compliance responsibilities, supported by external advisory resources for technical governance questions. This model is the most budget-efficient but risks being under-resourced if AI compliance demands grow faster than anticipated.
Closing the Gap
Regardless of which model an organization chooses, the critical first step is designation: naming a specific individual who is accountable for AI compliance. Until that designation is made, the organization has a governance gap that grows more consequential as enforcement deadlines approach. Organizations that need to hire AI governance leadership to fill this gap can start the conversation about what the role should look like and how to find the right person.