Enterprise AI Governance: Who Owns What in 2026

A proposed 2025 class action against Sharp HealthCare over alleged undisclosed use of an AI medical scribe illustrates the central argument that AI governance requires more than written policies: it needs clear decision ownership, documented evidence, risk-based approval workflows, and controls that can enforce requirements in practice. The discussion compares centralized, federated, and hub-and-spoke governance structures, favoring centralized standards with domain-level execution for large enterprises, and identifies decision rights, named RACI assignments, escalation paths, enforcement mechanisms, and recurring review as essential components. It emphasizes that low-risk and high-risk systems should follow proportionate review processes, while governance programs should generate audit evidence aligned with the EU AI Act, NIST AI RMF, and ISO 42001 during normal operations. Agentic AI presents additional challenges because autonomous tool calls can act before human review, requiring explicit tool allow lists, sequence-level monitoring, and detailed reconstructable audit trails. The piece distinguishes documentation, logging, and alerting from active controls that block, redact, or escalate harmful outputs, and presents Openlayer as a platform intended to connect intake, risk scoring, evaluation, monitoring, runtime enforcement, and compliance evidence.

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