From Boardroom to Build Pipeline: What AI Governance Actually Looks Like in Practice

Sixty-two percent of public company boards now dedicate agenda time to AI. Fewer than 10 percent have approved budgets for AI projects or adopted meaningful metrics for reporting on them. That gap :between awareness and action :is where AI initiatives go to die.

As a board director and former senior P&L executive who has reviewed dozens of AI initiatives across multiple companies, I’ve seen the full spectrum: structured projects with clear ROI thresholds, and aspirational efforts with no owner, no baseline metrics, and no path to integration. The pattern is consistent :the projects that fail aren’t the ones with bad models. They’re the ones with no governance framework around execution.

In this talk, I’ll share practical frameworks that engineering leaders and product teams can adopt to make their AI projects board-ready and business-accountable. I’ll walk through the Technology ROI Matrix :a method for evaluating AI initiatives across strategic alignment, investment efficiency, and execution readiness :and the Stage-Gate model adapted for AI, which establishes clear success criteria at each phase from discovery through scale. I’ll cover the critical questions every AI initiative should be able to answer: What baseline are we measuring against? Who owns the outcome? How do we detect when the AI system deviates from expected behavior? What’s the full cost of building, operating, monitoring, and refreshing this system?

This isn’t a talk about compliance checklists. It’s about the discipline that separates AI projects that transform businesses from ones that quietly drain resources. Whether you’re a developer advocating for an AI initiative, an engineering leader allocating budget, or a CTO reporting to a board, you’ll leave with actionable evaluation frameworks you can apply immediately.