AI governance isn’t just about regulation—it’s about anticipating what can go wrong, and making sure the right people are accountable when it does. In this TFiR clip, Jesse McCrosky, Principal Architect – GenAI at Egen, lays out a pragmatic, risk-centered approach to AI governance that extends far beyond legal compliance.

Risk Comes in Many Forms

“We need a broad conception of risk,” says McCrosky. Beyond regulatory risk, businesses need to account for:

• Operational risk (misaligned AI outcomes)
• Reputational risk (AI conflicting with company values)
• Social/environmental risk
• Competitive risk (falling behind)

Each of these requires a distinct lens—and collectively, they inform a stronger governance posture.

From Checklists to Culture

McCrosky emphasizes that governance can’t be performative. Effective frameworks must:

• Define who is accountable and for what
• Maintain model lifecycle oversight post-deployment
• Include transparent documentation and decision-making
• Prioritize upskilling and AI literacy across teams

“Without a culture of actually caring about getting this right,” he says, “it’s very difficult to end up with good outcomes.”

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