Author ORCID Identifier
Nura Jabagi: https://orcid.org/0000-0003-2218-1802
Jennifer Jewer: https://orcid.org/0000-0003-4744-6598
Constanze Riedinger: https://orcid.org/0000-0003-0226-4114
Anne-Marie Croteau: https://orcid.org/0000-0002-0552-2882
Josianne Marsan: https://orcid.org/0000-0002-3991-0269
Abstract
The rapid proliferation of artificial intelligence (AI) and generative AI (GenAI) is intensifying demands on corporate boards to provide effective oversight and responsible governance. Yet scholarly engagement with board-level AI governance remains nascent and fragmented, and many boards struggle to translate high-level principles into actionable strategies. In this panel report, we advance the concept of AI-Intensified Governance and introduce the 7Ps of AI-Intensified Governance—a conceptual lens capturing the new conditions AI imposes on board governance: Pace, Perpetual motion, Proliferation, Possibilities, Power, Pressure, and Polarization. Next, we synthesize our AMCIS 2025 panel “Guiding the Future: Boardroom Governance in the Age of Artificial Intelligence”, which brought together scholars and board members with expertise in Information Systems (IS) and digital transformation. Building on this analysis, as well as a review of IS and cross-disciplinary governance literature, we reposition board-level AI oversight as socio-technical stewardship and examine how boards build readiness across cognitive, institutional, relational, evaluative, and normative domains. We further develop a structured research agenda that identifies key challenges and opportunities for advancing board-level AI governance under AI-intensified conditions. This report contributes to emerging AI governance scholarship in corporate settings and supports boards in adopting a more proactive, socio-technical approach to oversight.
Recommended Citation
Jabagi, N., Jewer, J., Riedinger, C., Croteau, A., & Marsan, J. (In press). AI-Intensified Governance: Preparing Corporate Boards for the Future of Oversight. Communications of the Association for Information Systems, 58, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol58/iss1/87
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