Last Updated on 10/02/2026 by John Vins

AI business cases usually count hours saved and tasks automated. Yet many organizations still see little in the P&L. In this one-hour session, Alex Hilton uses throughput accounting and the Theory of Constraints to show why. He explains how a big local productivity gain can leave overall throughput almost unchanged, and how to test whether an AI initiative creates value at your constraint or just adds activity. You’ll leave with a practical lens for judging AI investments in terms of ROI and cash flow.

About Alex:

Alex Hilton has spent more than 12 years helping organizations connect delivery, product, and finance. At National Grid, he leads Agile and digital transformation across more than ten enterprise portfolios. There he built an AI automation pipeline with a 240:1 productivity gain, applied it at a late project’s capacity constraint, and brought the project in on its committed date. Before that, he was a Release Train Engineer and Scrum Master at Ascend Learning, and he spent seven years using Lean and the Theory of Constraints to improve small and mid-sized businesses. He has written on AI accountability for California Management Review Insights and CFO.com, and he has contributed to national AI policy through the IEEE-USA AI & Autonomous Systems Policy Committee. He holds an MBA in Finance and is a Scrum Alliance Certified Team Coach. He also serves as Treasurer of Agile New England and Program Chair of the Agile Games & Software Teaming Conference.