Where Are the AI Governance Roles? An Early-Stage Empirical Mapping of Presence, Absence, and Structure in Organisational AI Oversight

As AI technologies increasingly play a crucial role in organisational decision-making, ethical frameworks and governance guidelines have been developed to ensure accountability, transparency, and responsible use. However, these governance structures primarily assume that organisations have the formal capacity to oversee AI, without examining whether such capacity is actually present. Empirical evidence on how organisations truly govern AI—and where responsibility is fundamentally lacking—remains scarce. This paper offers an initial empirical delineation of formal AI governance responsibilities across diverse sectors and regions. It employs survey data from 351 organisations to investigate the existence of positions such as Chief Artificial Intelligence Officer (CAIO), AI Ethics Officer, Responsible AI Lead, Algorithmic Auditor, and AI Governance Committees. Furthermore, it analyses variations in these jobs across industries and geographies, as well as their structural characteristics, such as seniority, reporting relationships, authority, and available resources. The research reveals prevalent profiles of governance maturity. The findings indicate that formal roles for AI governance are not consistently implemented and, when they do exist, often lack the necessary authority, resources, and integration at a senior institutional level. Executive-level leadership roles and specialised audit functions are rare, and many organisations operate without any formal AI governance roles despite using AI technologies. The study outlines four profiles of governance maturity: Governance Absence, Symbolic Governance, Operational Governance, and Institutionalised Governance, highlighting that mature governance is often more the exception than the norm. By empirically assessing the presence or absence of AI governance, this research presents an absence-based viewpoint on AI ethics. It indicates that ethical concerns often arise from inadequacies in governance design rather than from flaws in existing frameworks. These results establish a foundational empirical baseline for subsequent studies on how various AI governance models influence compliance, trust, and ethical risks.