The impact of AI on data governance practices: a comparative framework

This paper develops a comparative framework to examine how artificial intelligence (AI) impacts data governance (DG) practices across organisational contexts. As organisations increasingly rely on AI in distributed data environments, understanding how DG practices adapt to such varying conditions becomes critical. Building on foundational DG theory, the study analyses how core DG domains, including data principles, quality, access, metadata, and lifecycle are applied under different organisational scopes and AI types. The proposed framework distinguishes between intra- and inter-organisational settings and between deterministic (rule-based) and probabilistic (learning-based) AI, generating four governance quadrants. It identifies distinct governance tensions across these quadrants, particularly around control, accountability, and coordination. This research shows that even if DG domains remain conceptually stable, their application varies in practice depending on authority distribution and algorithmic characteristics. The study provides a structured lens for understanding AI’s role in shaping DG practices and provides a foundation for future empirical research.