Show Notes
Marie Myers, Chief Financial Officer of HPE, explains how she measures business value while deploying agentic AI across a 3,600-person finance organization. Her framework separates direct ROI from indirect value (speed, accuracy, fewer errors) and the operating requirements that make finance AI trustworthy at scale.
YOU'LL DISCOVER ✅ How Myers separates direct ROI from indirect value, including speed, accuracy, and lower error rates ✅ Why determinism was "foundational" for finance AI, and why HPE co-engineered with Nvidia NIMs to achieve consistent answers across half a million data elements ✅ What "human in the loop" means in practice, and why accountability stays with finance leaders ✅ How Alfred (built on Deloitte's Zora platform) moved from transactional workflows to core finance operating rhythms like HPE's weekly ops call ✅ Why clean, reconciled data and a strong data layer are prerequisites for enterprise AI ✅ How HPE redesigned FP&A workflows, centralized the team, and pushed "one source of truth" before layering in agents ✅ How Myers thinks about agile experimentation, stage gates, and when to stop AI investments that will not pay off ✅ Why change management and cultural adoption are often harder than the technology, and how training 3,000+ people was essential
⏱️ TIMESTAMPS 0:00 Measuring AI value beyond hard ROI 3:40 Stage gates, scorecards, and when to stop an AI investment 6:49 "This is a team sport": IT, business, compliance 7:20 Determinism vs probabilism in financial AI 9:38 Alfred, Deloitte Zora, and private cloud (on-premises) architecture 13:04 Human in the loop and limits on agent autonomy 14:31 Highest ROI AI use cases: engineering, marketing, IT 16:23 Where finance sees ROI first: transactional workflows 19:00 "AI slop" and maintaining quality standards 25:32 Data quality and trusted, reconciled financial data 33:49 Redesigning FP&A workflows, "one source of truth" 40:35 Change management is the hardest part of AI
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