AI RACI Matrix — Accountability Framework for AI Governance

Definition

An AI RACI Matrix is a structured accountability tool that assigns, for each AI governance activity, the roles that are Responsible (who does the work), Accountable (who is ultimately answerable), Consulted (who provides input), and Informed (who needs to know the outcome). Adapted from project management and IT governance practice, the AI RACI Matrix operationalizes the accountability dimension of AI governance by making explicit who owns each governance action — from policy development through risk assessment, model deployment, monitoring, and incident response.

The NSW Government AI RACI Responsibilities Template (2025) provides a government-validated reference framework for assigning AI governance responsibilities across organizational roles. It covers the full AI lifecycle: strategic policy, risk assessment, data governance, model development, deployment approval, human oversight designation, post-market monitoring, incident reporting, and audit preparation. The template is structured to be directly auditable against ISO/IEC 42001 and EU AI Act requirements.

A well-designed AI RACI Matrix resolves the most common AI governance failure: diffuse accountability. When no specific role is Accountable for an AI governance activity, that activity tends not to happen consistently. The RACI structure makes governance gaps visible before they become incidents.

Why it matters operationally

The AI RACI Matrix matters because AI governance accountability is structurally different from most organizational accountability frameworks. AI decisions and their consequences are distributed across roles that did not previously interact: data scientists build models, business units deploy them, IT operates them, legal advises on them, and compliance monitors them. Without an explicit RACI, each function assumes another is accountable for governance actions that fall between traditional role boundaries.

The NSW Government RACI template documents a pattern that has been validated across multiple governance programs: organizations that formalize AI accountability through RACI consistently identify accountability gaps that existing role descriptions leave uncovered. The most common gaps are in the deployment approval and post-market monitoring phases, where the transition from development to operations creates unclear ownership.

Regulatory framework

Framework	RACI accountability requirements
ISO/IEC 42001 — Clauses 5, 6.2	Requires the organization to establish roles and responsibilities for AI governance, assign accountability for AIMS performance, and ensure responsibilities are communicated. The RACI Matrix is the operational mechanism for satisfying these requirements.
EU AI Act	Provider and deployer roles have differentiated obligations. A RACI matrix mapping those obligations to internal organizational roles is the implementation tool that converts regulatory requirements into operational accountability.
NIST AI RMF — Govern	The Govern function requires establishment of policies, roles, responsibilities, and risk culture. The RACI Matrix operationalizes the role assignments that Govern requires.

How Zertia evaluates it

Zertia evaluates AI RACI accountability as part of ISO/IEC 42001 certification. The audit assesses whether governance roles and responsibilities are explicitly defined, whether accountability (not just responsibility) is assigned for key AI governance activities, and whether the RACI structure covers the full AI lifecycle including post-market monitoring and incident response. The NSW Government RACI template is a reference model that Zertia uses when advising organizations on governance structure design during pre-certification assessments.

[ISO 42001 Certification] · Pre-Certification Assessment