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Home > CXOTalk > AI Agents in Finance with HPE's Chief Financial Officer (CFO) | CXOTalk 914
Podcast: CXOTalk
Episode:

AI Agents in Finance with HPE's Chief Financial Officer (CFO) | CXOTalk 914
Category: Technology
Duration: 00:55:43
Publish Date: 2026-04-10 22:24:45
Description:

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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