Conceptualizing Enterprise AI Governance Functions as Institutional Standards for Auditable and Defensible Risk Management
Abstract
As enterprises move artificial intelligence from pilot projects into core business processes, the informal governance arrangements that sufficed during experimentation are proving inadequate for regulatory, contractual, and reputational scrutiny. This paper argues that enterprise AI governance functions, the organizational structures and processes through which firms decide, monitor, and account for AI use, are best understood not as internal management preferences but as institutional standards: patterned, transferable practices that gain legitimacy through convergence with recognized external frameworks. Drawing on institutional theory and the current landscape of AI governance instruments, including ISO/IEC 42001, the NIST AI Risk Management Framework, and the phased obligations of the EU AI Act, the paper develops a conceptual model in which governance functions are organized around five interlocking capabilities: direction setting, risk classification, measurement and testing, control and response, and independent assurance. The model is examined through two properties increasingly demanded by regulators, courts, and boards: auditability, the capacity to reconstruct and evidence a decision after the fact, and defensibility, the capacity to justify that decision against a documented standard of care. The paper concludes that treating governance functions as institutional standards, rather than bespoke internal controls, offers enterprises a more durable basis for managing AI risk amid regulatory uncertainty, including recent revisions to the EU AI Act's high-risk implementation timeline.
Keywords
AI governanceinstitutional theoryauditabilitydefensibilityISO 42001NIST AI RMFEU AI Actenterprise risk managementthree lines model
References
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