Artificial intelligence has moved beyond pilot projects and productivity tools. It now acts autonomously, processing claims, executing trades, and taking thousands of actions per hour with minimal human oversight. Yet research shows that while 80 percent of organizations have an AI policy on paper, fewer than 20 percent have an operational governance model with clear decision rights, escalation paths, and enforcement. Meanwhile, two-thirds of directors report limited to no AI knowledge, and one-third of boards have yet to place AI on their agenda. The gap between policy and practice has become a board-level risk.
In this webinar, two enterprise technologists who have led AI governance discussions and worked with boards across industries around the globe share what they've learned: the patterns that separate leaders from laggards, the governance models that actually work, and the questions directors should be asking management right now. Drawing on insights from hundreds of enterprise AI engagements, they'll cover four critical areas: how to structure AI oversight without creating bottlenecks, what changes when AI shifts from suggesting to acting autonomously, how to measure AI ROI beyond headcount savings, and why most organizations stall between pilot and production, including what boards can do about it.
This program is complimentary to NACD members and nonmembers.
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Learning Objectives
- Evaluate and select an AI governance operating model by understanding the three emerging archetypes (centralized, federated with guardrails, and embedded), including how to define actionable AI risk appetite and establish decision authority tiers for autonomous AI agents
- Apply a multi-dimensional framework for AI investment oversight by moving beyond FTE-savings metrics to assess revenue impact, risk reduction, speed-to-market, strategic optionality, and the quantifiable cost of inaction ($87 million average annual impact for lagging organizations)
- Ask the right board-level questions about data readiness by identifying the "pilot-to-production gap," where 60–70 percent of AI effort is spent on data engineering, and recognizing the signals that an organization's data foundation can (or cannot) support AI at scale
Who Should Attend
- Corporate directors and board chairs
- Directors serving on technology, risk, and audit committees
- Senior executives involved in AI strategy and governance
- NACD members and nonmembers
Governance Models That Work
Compare the three emerging AI governance archetypes, centralized, federated with guardrails, and embedded, to find the right fit for your organization
Beyond FTE Savings
Learn a multi-dimensional framework for measuring AI ROI, including revenue impact, risk reduction, and the cost of inaction
Closing the Pilot-to-Production Gap
Identify the board-level questions that reveal whether your organization's data foundation can support AI at scale
