Eliminate Accountability Gaps in Your AI Lifecycle.

In complex AI deployments, the question isn’t just what needs to be done, but who owns the risk when things go sideways. Built on two decades of leadership at Google and Tesla, The Enterprise AI Governance RACI is the definitive mapping tool for Technical Program Managers and AI Leaders.

This isn't a generic template. It is a practitioner-grade matrix specifically designed to bridge the gap between Engineering stability, Product ethics, and Legal compliance.

What’s Inside:

• The Governance RACI Matrix (PDF): A comprehensive breakdown of roles (Product, Engineering, AI Program Management, IT/Security, and Legal) across the most critical AI milestones.

• Agentic Risk Mapping: Specific ownership assignments for modern AI challenges, including "Kill Switch" authorization, Agentic Persona permissions, and Recursive Loop monitoring.

• The "Process Architect" Logic: Clear definitions of the AI Program Manager’s role in orchestrating cross-functional sign-offs without becoming a bottleneck.

  
  

Critical Ownership Domains Covered:

• The "Soul" vs. "Engine": Distinguishing accountability between Product Managers (Moral North Stars) and Engineering (Technical Bias Audits).

• Model Stability & Safety: Defining who is accountable for "Infinite Token Consumption" and "Model Drift" once the system is live.

• The Infrastructure Vault: Assigned ownership for API security, prompt injection defense, and data poisoning prevention.

• Executive Safety Nets: Strategic placement of the Executive Sponsor as the "Shield" for high-stakes "Go/No-Go" decisions.

  
  

Who is this for?

• Lead Program Managers who need to standardize cross-functional (XFN) workflows for 0-to-1 AI products.

• AI Governance Professionals looking for a validated framework to present to Ethics Boards or Executive Leadership.

• Engineering Managers who want to protect their teams by clearly defining where technical responsibility ends and business accountability begins.