Artificial Intelligence Governance Mechanisms—The Chief Data Officer Perspective with a Focus on Agentic AI Governance

Abstract

Artificial intelligence (AI) governance is becoming increasingly important due to technological development, widespread adoption, and the lack of boundaries. This triggers many opportunities for innovation but also increases risks. By conducting 23 subject-matter expert interviews with global Chief Data Officers and performing a workshop with 31 European Chief Data Officers, we explored five AI governance mechanisms identified in the literature, and we discussed the impact and developments regarding future legislation in a global context and the impact of data and operational sovereignty on (agentic) AI governance. Our data suggests that installing an AI governance steering committee is currently the most important mechanism and that stakeholder management and model ownership are prerequisite mechanisms; moreover, audit and impact assessments, as well as staff training, are hygiene mechanisms. In the context of agentic AI governance, we found that the mechanisms identified for AI governance must be applied with greater scrutiny, rigor, and consistency; in addition, in this second round of our research, we find that stronger AI tooling is required to effectively support governance practices. Our research resulted in an AI governance framework with six governance mechanisms and six AI governance good practices that provide a starting point for organizations to implement AI governance.

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