Boards in the Age of AI: Antecedents, Decisions, and Outcomes of Board-Level Artificial Intelligence Governance—A Systematic Review

The article deepens understanding of artificial intelligence on corporate boards by systematically reviewing the literature using the ADO (Antecedents, Decisions, Outcomes) and TCM (Theories, Contexts, Methods) frameworks, following established systematic review guidelines and PRISMA. The findings integrate theoretical, contextual, and methodological perspectives to identify key antecedents, decisions, and outcomes. Organizational, structural, individual, and external antecedents are crucial in shaping board-level decisions on structural adjustments, technology integration, human capital and education, strategic communication, and operational adaptation. These decisions influence the quality of supervision, the effectiveness of decision-making, as well as resilience, legitimacy, accountability, and behavioral outcomes. The research provides three distinct contributions. First, a synthesis contribution: a systematic taxonomic mapping of the construct space represented in the included corpus, comprising 30 antecedent, 15 decision, and 27 outcome constructs specific to board-level AI governance. Second, a conceptual contribution: the first integrative application of the combined ADO-TCM framework to the board-AI intersection. Third, a theoretical contribution: the identification of an emerging pattern in the reviewed literature favoring engaged augmentation over full automation as an approach to human–algorithm collaboration in board oversight.