Designing the AI Governance Operating Model & RACI

Summary

This practical guide from Elevate Consult tackles one of the most challenging aspects of AI governance: turning strategy into operational reality. Rather than offering theoretical frameworks, it provides concrete guidance for building governance operating models with clear RACI (Responsible, Accountable, Consulted, Informed) matrices that actually work in practice. The resource addresses the critical gap between high-level AI principles and day-to-day operational decisions, helping organizations move beyond governance theater to create systems that can scale AI initiatives while managing risk effectively.

The operational governance challenge

Most organizations struggle with AI governance because they focus on policies without considering how those policies translate into daily operations. This resource addresses three core operational challenges: role confusion (who decides what?), process bottlenecks (where do AI decisions get stuck?), and accountability gaps (who's ultimately responsible when things go wrong?). The guide recognizes that effective AI governance isn't just about having the right principles—it's about creating workflows that enable quick decision-making while maintaining appropriate oversight.

What you'll find inside

The resource centers on building RACI matrices specifically for AI governance scenarios, but goes beyond simple role assignments. It includes guidance on designing approval workflows that don't kill innovation, establishing controls that adapt to different AI risk levels, and creating governance structures that can evolve with your AI maturity. Key components include templates for common AI governance decisions, escalation pathways for edge cases, and integration points with existing IT and compliance processes.

Who this resource is for

• Primary audience : Operations leaders, compliance officers, and IT governance teams tasked with implementing AI governance in practice. This is particularly valuable for organizations that have established AI strategies or adopted AI frameworks but are struggling with the "how" of day-to-day governance.
• Also useful for : Chief Data Officers building AI Centers of Excellence, project managers overseeing AI implementations, and consultants helping clients operationalize AI governance.

The resource assumes some familiarity with RACI methodology and basic AI risk concepts.

Getting your operating model right

The guide emphasizes three critical success factors for AI governance operating models. First, right-sizing governance to match AI risk and business impact—avoiding the trap of applying enterprise-level governance to low-risk AI experiments. Second, designing for speed without sacrificing oversight by creating clear swim lanes for different types of AI decisions. Third, building in feedback loops that allow the operating model to learn and adapt as the organization's AI capabilities mature.

Watch out for common implementation traps

Several pitfalls can derail AI governance implementation. The resource warns against creating RACI matrices that are too complex for practical use, designing approval processes that become innovation bottlenecks, and failing to account for the interdisciplinary nature of AI decisions. It also addresses the challenge of maintaining governance effectiveness as AI initiatives scale, emphasizing the need for operating models that can handle increased volume without proportional increases in governance overhead.