Speaking & Media

For most U.S. banks, AI is no longer a question of pilots and proofs of concept — it's a question of how to turn isolated experiments into enterprise capability that moves the financials. This session maps the path from AI POC to enterprise AI: what has to change in the operating model, data foundation, and governance when intelligence moves from the edges of the organization into the core of banking, payments, risk, and customer engagement.

The discussion is organized around four shifts that separate leaders from the rest. First, moving from AI POC to enterprise AI — productionizing and scaling what already works. Second, moving beyond productivity to enterprise value — tying AI to revenue, margin, and return rather than activity. Third, secure and responsible AI — deploying in ways that hold up to regulatory, risk, and model-governance scrutiny. And fourth, tokenomics and AI financial optimization — managing the economics of compute so AI spend is metered, concentrated on the highest-leverage use cases, and run like a disciplined investment.

This session focused on AI assurance amid the ever-increasing evolution of AI capabilities — agentic and autonomous software, AI assistants for executives, and a reduced human-in-the-loop.

Key topics included a framework for governance — human controls, velocity and tempo, and exponential complexity — and the drive to build a culture of learning and challenge that drives outcomes.