AI Governance for Executives and Boards

AI is moving from experimentation into decisions that can affect revenue, risk, customers, operations, and compliance. This session explores what happens when AI becomes part of the decision-making layer, and how executives and boards can establish accountability before failures become business crises.

Title: AI Governance for Executives and Boards
Date & Time: Wednesday, September 30, 2026 | 07:00 PM - 08:00 PM IST | 8:30 AM - 9:30 AM CDT
Location: Online | Register Now

### What You’ll Learn

• From Adoption to Accountability: Identifying where AI materially influences business outcomes and decisions.
• The Governance Framework: Applying VALUE → PROOF → SCALE → GOVERN to move AI from experimentation to accountable deployment.
• When AI Goes Wrong: What real-world failures reveal about ownership, oversight, recovery, and escalation.
• Board-Level Questions: What leaders should ask about assumptions, error rates, human overrides, monitoring, and accountability.
• Governance at Scale: Building practical controls that protect the enterprise without blocking legitimate AI innovation.

### Who Should Attend

• Board Members, CEOs, CXOs, CTOs, CIOs, and Technology Leaders.
• AI, Data, Risk, Compliance, Legal, and Governance Professionals.
• Business Leaders responsible for AI strategy, transformation, and enterprise decision-making.

### Speaker

Dr. Munmun Das | Director - GCC (Data, AI & Advanced Analytics), Huge
Dr. Munmun Das brings 20+ years of global experience building AI, Data, and Automation-led transformation programs across BFSI, Healthcare, Media, Smart Cities, and Digital Commerce.
She currently leads AI, Data & Advanced Analytics for a global GCC, driving enterprise AI/GenAI strategy and agentic platform architecture, with P&L accountability for $25M+ delivery portfolios.
She is an MCA-Certified Independent Director, deep-tech researcher, author of seven books, and published researcher in AI, IoT, Blockchain, and risk frameworks.

Don’t Miss Out
Discover how boards and executives can turn AI governance from a policy exercise into a practical system of accountability, evidence, oversight, and responsible scale.

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