Paul BarnhurstFP&A Expert Empowering Finance Professionals to Excel Everday

Most finance leaders misunderstand AI.

3 Jul 2026 · 1 min

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1:02

In a conversational excerpt from a YouTube video, Paul Barnhurst challenges the common belief among finance leaders that modern AI tools are purely generative. He notes that many software vendors describe their FP&A solutions as deterministic—identical inputs always yield identical outputs—similar to traditional statistical models. Barnhurst argues that labeling these tools as "AI" creates unnecessary fear and confusion about the role of agents. Speaker B expands on this by characterizing today’s AI as a probabilistic engine rooted in massive multivariable regression and matrix mathematics, likening it to high‑school level math performed at enormous scale. Both speakers highlight a gap in understanding: finance leaders often conflate deterministic automation with generative AI, while the underlying technology is fundamentally statistical and probabilistic, leading to misperceptions and anxiety about AI adoption in finance.

Chapters

people sometimes have the idea that, okay, it's all being done by generative AI. I've talked to a lot of software vendors and almost every one of them says, no, the numbers— we're using a deterministic process here. If you give me the same inputs, you're going to get the same output every time. It's no different than— I think different in a little ways, but very similar to almost every FP&A tool that has an algorithm that will do a statistical model for you. There's a lot of that behind the scenes. You're just now using AI, and so that different term, a little bit of different technology and automation, creates a real fear. What would you say to that? That's kind of how I think about it. Is that a fair assessment here of how to think about a lot of this technology with agents?
AI as it stands today is a probabilistic engine. I think part of it is like no one really understands how AI works. I've actually had a chance to speak all day with Frontier Labs. They're like, this is really great. If you think about AI at its really simplest core, it's, it's like grade 12 math. It's multivariable regression analysis at like monstrous scale. It's matrix math. That's really what it is. The researchers that are building these capabilities even understand why this math produces the output and outcomes that it produces, but it's good.