Four Corporate AI Governance Gaps Partnership on AI Says Organizations Must Close Now

The Partnership on AI published Corporate AI Governance Matters Now More Than Ever on May 30, 2025, outlining four structural deficiencies the organization argues are present in most corporate AI governance programs today. The piece identifies supply chain responsibility, end-user terms and conditions, AI assurance ecosystems, and real-time monitoring of AI agents as the domains where organizational controls are most frequently absent or underdeveloped. The guidance is global in scope and directed explicitly at enterprises deploying AI at scale, including those that develop, procure, or integrate third-party AI components. While the publication does not impose legal obligations, it draws on incident data and emerging regulatory convergence to make the case that these gaps are both common and consequential. The framing is notably practical: each governance domain is connected to a category of harm that has already materialized in commercial deployments.

The publication addresses a widening control gap that has become visible as AI systems move from experimental pilots into production operations and increasingly agentic configurations. Traditional IT vendor risk management and software procurement frameworks were not designed to handle AI supply chains, where a single deployed model may incorporate components from multiple upstream providers, each with distinct training data provenance, performance characteristics, and update cadences. The Partnership on AI's emphasis on assurance ecosystems reflects a broader regulatory trend: the EU AI Act, ISO 42001, and the NIST AI RMF Playbook all require organizations to demonstrate ongoing conformity rather than point-in-time certification, but few enterprises have operationalized the continuous assurance functions those frameworks contemplate. The call for real-time agent monitoring is particularly timely given the rapid commercial adoption of agentic AI systems that take autonomous sequences of actions, execute code, access external data sources, and interact with downstream systems in ways that exceed the oversight capacity of existing human-in-the-loop controls. Compliance programs built around static model documentation and periodic audits are structurally ill-equipped to govern these systems, a gap the guidance explicitly names.

Compliance teams should use this publication as a diagnostic against their existing AI governance program rather than treating it as background reading. Teams that have completed an AI system inventory using a control like ai-system-inventory-and-risk-classification should re-examine whether that inventory captures agentic systems and AI components embedded in third-party software, since those are the categories most likely to be missing. Third-party AI vendor due diligence programs should be assessed against the supply chain responsibility criteria the Partnership on AI identifies, specifically whether vendor contracts require disclosure of upstream model components, training data sourcing practices, and incident notification obligations. The governing-agentic-ai playbook control applies directly to the real-time monitoring gap and should be reviewed for implementation completeness, particularly for any AI agents that have been granted tool access, API permissions, or the ability to initiate transactions without per-action human approval. No standard control yet covers the construction of an AI assurance ecosystem as a continuous organizational function rather than a project-level activity; teams should begin scoping what a standing assurance function would require in terms of tooling, staffing, and reporting cadence. Organizations in regulated industries, including financial services, healthcare, and critical infrastructure, should treat the agent monitoring and supply chain gaps as near-term priorities given the convergence of regulatory expectations from DORA, the EU AI Act, and sector-specific guidance from bodies such as the Financial Stability Board.