Paper Type
Complete
Abstract
This paper examines the distribution of responsibility and accountability for AI governance in large-scale organizations. Despite the growing importance of AI governance, existing literature lacks clear guidance for the incorporation into organizational structures and role assignments. We investigate core activities for AI governance with respective roles and responsibilities in practice, on organizational and on AI system layer. We apply design science research to develop a RACI matrix that maps responsibilities to core AI governance activities. This research is based on a literature review and evaluation of interviews with nine AI governance leaders from multinational organizations. Our findings demonstrate that effective AI governance requires a multidisciplinary approach with both centralized accountability through C-level executives and decentralized responsibility through specialized AI roles. The research provides practical guidance for companies establishing AI governance structures while addressing a significant gap in the literature.
Paper Number
1379
Recommended Citation
Engel, Sarah and Duske, Kira, "AI Governance in Large-Scale Organizations: Who is Responsible for What?" (2025). AMCIS 2025 Proceedings. 1.
https://aisel.aisnet.org/amcis2025/is_leader/is_leader/1
AI Governance in Large-Scale Organizations: Who is Responsible for What?
This paper examines the distribution of responsibility and accountability for AI governance in large-scale organizations. Despite the growing importance of AI governance, existing literature lacks clear guidance for the incorporation into organizational structures and role assignments. We investigate core activities for AI governance with respective roles and responsibilities in practice, on organizational and on AI system layer. We apply design science research to develop a RACI matrix that maps responsibilities to core AI governance activities. This research is based on a literature review and evaluation of interviews with nine AI governance leaders from multinational organizations. Our findings demonstrate that effective AI governance requires a multidisciplinary approach with both centralized accountability through C-level executives and decentralized responsibility through specialized AI roles. The research provides practical guidance for companies establishing AI governance structures while addressing a significant gap in the literature.
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