Companion videos

AI ROI and maturity: how to measure adoption that actually works — companion videos

The article rejects vanity metrics and measures AI adoption at the workflow level: baseline, time, quality, risk, sustained use and maturity. This companion delivers the CFO case study the topic needs: a finance chief measuring agentic AI the way the article says adoption should be measured — per workflow, with quality gates and stop decisions.

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55:43
AI Agents in Finance with HPE's Chief Financial Officer (CFO)

CXOTalk

HPE CFO Marie Myers describes rolling agentic AI (their "Alfred" platform) across a 3,600-person finance organization with the discipline the article argues for: a direct-versus-indirect ROI framework, stage gates that decide whether each use case continues or stops, mandatory human-in-the-loop quality controls, and workflow-level metrics such as a weekly review process losing roughly 90% of its manual effort. Adoption is measured as changed work, not tool logins.

What you should get from this: Build an ROI case for one AI workflow with a baseline, direct and indirect benefits, quality gates and an explicit stop decision.

Watch or know first: Experience with team-level AI pilots, business metrics or finance/operations processes large enough to measure.

AI Expert note: A vendor-neutral interview but still one company's success story told by its CFO, so expect the wins to be foregrounded. Borrow the measurement framework and the stage-gate discipline; validate the percentages against your own baselines.

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