The AI Governance RACI Matrix

Summary

Forrester's AI Governance RACI Matrix cuts through one of the biggest challenges in AI implementation: figuring out who does what. This practical template maps organizational roles against AI governance activities using the proven RACI framework (Responsible, Accountable, Consulted, Informed), all structured around the NIST AI Risk Management Framework's four core functions. Instead of leaving AI governance responsibilities unclear or duplicated, this matrix provides a concrete way to assign decision-making authority and stakeholder involvement across your organization's AI initiatives.

Who this resource is for

• AI program managers establishing governance structures for the first time
• Chief AI Officers and Chief Data Officers building cross-functional AI teams
• Risk management professionals extending existing frameworks to cover AI systems
• IT leaders implementing AI governance within broader technology governance structures
• Compliance teams ensuring AI initiatives meet regulatory requirements
• Executive stakeholders who need clarity on their role in AI oversight

What makes this different

Unlike generic RACI templates, this matrix is purpose-built for AI governance challenges. It addresses the unique complexity of AI systems that often span multiple departments, require specialized expertise, and carry distinct risk profiles. The matrix aligns with the widely-adopted NIST AI RMF, making it compatible with existing risk management approaches while providing the granular role clarity that AI governance demands.

The template doesn't just list roles—it provides context for why certain stakeholders should be responsible versus consulted for specific AI governance activities, helping organizations avoid the common pitfall of over-consulting that slows down AI initiatives.

Core governance functions mapped

Govern: Establishes who sets AI strategy, policies, and oversight mechanisms. Clarifies executive accountability versus operational responsibility for AI program direction.

Map: Defines roles for AI system documentation, risk identification, and stakeholder analysis. Separates technical mapping responsibilities from business impact assessment.

Measure: Assigns accountability for AI system monitoring, performance measurement, and risk assessment activities. Distinguishes between technical measurement and business impact evaluation roles.

Manage: Clarifies responsibilities for ongoing AI system management, incident response, and risk mitigation. Separates day-to-day operational management from strategic oversight.

Getting started with the matrix

Begin by identifying your organization's key AI stakeholders across business units, IT, legal, risk, and compliance functions. Map these actual roles to the template's generic role categories—your "AI Product Owner" might fill the matrix's "Business Lead" function.

Start with a single AI use case or system rather than trying to map your entire AI portfolio at once. This allows you to test the matrix structure and refine role definitions before scaling across multiple initiatives.

Pay special attention to the "Accountable" assignments—ensure you have exactly one accountable party for each governance activity to avoid decision-making bottlenecks or finger-pointing when issues arise.

Watch out for

Role proliferation: Don't create more roles than necessary. The matrix should clarify responsibilities, not bureaucratize them.

Consulting everyone: Resist the urge to mark too many stakeholders as "Consulted"—this creates decision paralysis and slows AI implementation.

Static assignments: AI governance needs evolve as systems mature. Plan to revisit and update role assignments as your AI program grows.

Technical versus business splits: Ensure technical and business stakeholders have clear handoff points rather than unclear overlapping responsibilities.