In this episode of "Full Tech Ahead," Amanda Razani interviews Mery Zadeh, SVP of AI Governance and Risk Consulting at Lumenova AI. The conversation centers on the complexities of building AI governance teams and navigating the "three-language problem" where technical, regulatory, and business teams struggle to communicate.
Zadeh advises companies to stop searching for "unicorn" candidates who possess all these skills and instead focus on cross-training and rotation programs. Furthermore, as AI evolves from Generative AI to Agentic AI (systems that take independent actions), she emphasizes the critical need to shift from annual audits to real-time, continuous monitoring.
Ultimately, she notes that the most successful AI governance frameworks are practical ones that developers actually use, rather than theoretically perfect models.
Key Quotes
- "We're turning AI risk to possibilities."
- "We should stop looking for a Unicorn... start on career paths, start on education."
- "You can't govern what you can't see."
Takeaways
- Solve the "Three-Language Problem": Effective AI governance requires fluency in business, technology, and compliance. Instead of hunting for an impossible candidate who knows it all, organizations should implement rotation programs (e.g., auditors spending time with tech teams) to cross-train their current workforce.
- Integrate Teams Across the AI Lifecycle: From the initial business intake and legal risk assessment to the developer build phase, cross-functional teams must collaborate continuously to ensure the AI system is technically sound, compliant, and fit for its intended use.
- Shift to Continuous Monitoring: As the industry moves toward Agentic AI—where AI agents execute tasks like booking or canceling meetings—traditional annual audits are obsolete. Organizations must implement real-time monitoring and strict permission controls to catch and correct issues immediately.
- Prioritize Practical Governance: The most effective AI governance isn't a flawless, overly complex policy; it's a practical framework with clear controls and evaluations that developers understand, see value in, and actually follow.
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