AI Governance RACI Matrix Template

Summary

This template from ISACA tackles one of the most overlooked challenges in AI governance: who actually does what when things go wrong. While organizations rush to implement AI ethics committees and oversight boards, many fail to clearly define accountability when an AI system makes a biased decision or causes harm. This RACI matrix template cuts through the confusion by mapping specific responsibilities across the entire AI lifecycle, from initial development through deployment and monitoring. Unlike generic RACI templates, this tool is specifically designed for AI governance complexities, addressing unique challenges like model drift monitoring, algorithmic bias assessment, and cross-functional AI risk management.

The accountability gap this solves

Traditional organizational structures weren't built for AI oversight. When an AI system discriminates against loan applicants or a chatbot provides harmful medical advice, organizations often discover they have overlapping responsibilities, gaps in oversight, or unclear escalation paths. This template addresses that chaos by clearly defining:

• Who owns AI risk decisions at each stage of development and deployment
• Which roles have input versus final authority on critical AI governance activities
• How information flows between technical teams, legal, compliance, and business units
• Where accountability sits when AI systems cause harm or fail to meet performance standards

The matrix covers 15+ core AI governance activities, from data quality assurance to incident response, ensuring nothing falls through organizational cracks.

Who this resource is for

Primary audience: Chief Data Officers, Chief AI Officers, and IT governance professionals tasked with implementing formal AI oversight structures. This is particularly valuable for organizations that have moved beyond ad-hoc AI projects and need systematic governance as they scale AI across business units.

Also valuable for: Compliance teams preparing for AI regulations, risk managers establishing AI risk frameworks, and project managers leading cross-functional AI initiatives who need clear role definitions to avoid scope creep and accountability gaps.

Best fit: Mid-to-large organizations (500+ employees) with multiple AI use cases and complex stakeholder environments. Smaller organizations may find this overly structured, though the concepts remain relevant.

How to customize this for your organization

The template provides a foundation, but effective implementation requires thoughtful adaptation to your specific context:

Map to your existing roles: The template uses generic titles like "AI Ethics Officer" and "Model Owner." Replace these with your actual job titles and organizational structure. If you don't have dedicated AI roles, assign responsibilities to existing positions while noting potential resource constraints.

Adjust for your AI maturity level: Early-stage AI adopters should focus on the core governance activities (model validation, bias testing, deployment approval) before implementing the full matrix. Mature AI organizations may need to add specialized activities like federated learning governance or AI supply chain risk management.

Consider your regulatory environment: Organizations in heavily regulated industries (healthcare, financial services) may need additional "Consulted" roles for compliance and legal review. Those preparing for the EU AI Act should ensure high-risk AI system activities have clearly defined accountability chains.

Implementation roadmap

Phase 1 (Weeks 1-2): Map current AI initiatives to the template activities. Identify where you have unclear or missing accountability. Don't try to fill every gap immediately—document them for prioritization.

Phase 2 (Weeks 3-4): Socialize the draft matrix with key stakeholders. Pay special attention to activities marked as "Accountable"—these roles need explicit buy-in and may require job description updates or resource allocation.

Phase 3 (Month 2): Pilot the matrix with 1-2 AI projects to test role clarity and identify friction points. Common issues include over-consultation (too many "C" designations slowing decisions) and accountability conflicts between technical and business owners.

Phase 4 (Month 3+): Roll out organization-wide with regular reviews. AI governance needs evolve as technology and regulations change—plan quarterly matrix reviews to ensure continued relevance.