I lead the AI practice at VAi Consulting, where I guide CFOs and finance executives on GenAI strategy, automation, data analytics, and the full spectrum of AI implementation. My work spans AI readiness assessments, workflow automation, and driving change across FP&A, audit, and risk functions. I help shape our service offerings and collaborate with a national network of consultants to deliver impactful results for clients in healthcare, manufacturing, technology, and business services.

Prior to consulting, I spent 20 years as a CFO in private equity-backed companies, along with interim and advisory roles supporting public companies. I’ve built finance functions from the ground up, led M&A transactions on both the buy and sell side, and navigated the audit and controls complexities of institutional ownership. This hands-on experience shapes how I approach AI in finance. CFOs need more than just the potential of new technology. They need clear answers on how it will impact their control environment and the messaging to audit committees.

I’m currently working on my third book on AI in Finance, The AI-Ready CFO, which is due out in 2026 from Wiley Finance. My previous books include Deep Finance and AI Mastery for Finance Professionals.

I teach in Duke University’s CFO Program and for the AICPA and Corporate Finance Institute. I also sit on advisory boards for Preql, an AI data platform, the AI Leaders Council, and the Crews School of Accountancy at the University of Memphis. 

I’m the host of the FP&A Today podcast and a U.S. Navy veteran.

I’m teaching Section’s AI for Corporate Finance workshop on January 27. 

We’ll focus on practical workflows for CFOs, FP&A, controllers, and finance ops, and the guardrails that make them defensible month after month.

Section also recently interviewed me for a short piece on the four AI investments finance leaders should make in 2026. Links in the comments.

What are you prioritizing first this year: data consistency, process clarity, governance, or capability building?

Glenn Hopper

Building Practical AI Solutions for Finance | Head of AI at VAi

I just released a new DataCamp course: AI for Finance.

It’s a practical, finance-first walkthrough of how to use AI to speed up real work products: summaries from filings and transcripts, analysis from structured data, and outputs that are clean enough to review and share.

The course is about control as much as speed. Prompt templates, consistency, and workflow design so AI helps during close week instead of creating rework.

If you’re building AI capability in a finance team (or you’re the one everyone asks to “figure out AI”), this will give you a solid starting point.

https://lnkd.in/gP2URQWH

Claude Cowork is the first AI product release in a while that made me think more about actual finance workflows than model specs.

This is a product preview mode now available only in the Claude macOS desktop app (for now) that can take a scoped workspace and produce real deliverables. This is important to finance professionals because in many ways finance is still an "artifact factory" with all of our Excel models, decks, memos, binders, and trackers. We spend an embarrassing amount of time turning messy inputs into something reviewable. Claude Cowork can help with that.

In the free post, I break down three workflows where this is plausibly useful:
- Variance commentary packs that tie back to the numbers
- KPI pack refreshes that actually update the workbook and deck
- Audit and PBC support that reduces the coordination tax

The caveat is the same one finance always has: the closer a tool gets to your reporting outputs, the more it becomes part of your control environment. Faster throughput is great. Review gates are non-negotiable.

Free article here: https://lnkd.in/gbWgwsCV

For pro subscribers I go deeper on how to use Cowork to build an actual finance workflow, where it fits in the stack, an end-to-end variance pack build with review gates, six month-end use cases, a controls-first operating model, plus a 10-business-day pilot plan and prompt pack.

🎙️ New FP&A Today episode is live!

In this episode stephen hedlund, Head of Finance at Rillet, and I discuss the future of the AI-native ERP

Stephen has one of the more interesting seats in finance right now:

He is building the finance function at a company rebuilding the finance function ~ with a clear thesis on what “AI-native” actually changes in close, controls, and day-to-day operating cadence.

We talk about the real gap between “AI layered on top” vs a system rebuilt for AI, why integration depth and metadata matter, and where automation is genuinely delivering ROI. We also get into the operator reality of scaling from enterprise to startups, the kind of people who thrive in early-stage environments, and what it looks like when finance is expected to influence growth, not just report it.

Stephen’s origin story is one of the better ones I’ve heard. He traces his path into finance back to Isaac Asimov’s Foundation and the idea of “psychohistory,” where enough variables can make the future feel predictable. That instinct, the pull toward forecasting and decision support, is basically corporate finance in its purest form ... and I think made us instant BFFs!

Open article: The Future of the AI-Native ERP Stephen Hedlund, Rillet  - Datarails by datarails.com

The Future of the AI-Native ERP Stephen Hedlund, Rillet 

The Future of the AI-Native ERP Stephen Hedlund, Rillet  - Datarails

Every AI business case eventually collapses into three questions: payback, IRR, and “how sure are we?”

The first two are easy to calculate. The third is where most AI ROI models quietly fall apart. AI doesn’t behave like a deterministic IT investment. Value depends on adoption, data quality, and whether performance holds as conditions change. If you model it like traditional software, you either overpromise or miss the real economics.

This week’s Deep Finance Dispatch walks through how CFOs should think about AI ROI differently. Probability-weighted returns. Time-to-value. Benefit durability. And why treating AI as a portfolio rather than a single project leads to more defensible capital decisions and better board conversations.

Free version is out now. The Pro edition goes deeper into the mechanics.

https://lnkd.in/eCqDEnYd

New FP&A Today episode is live: Troy Anderson, CFO at Kelly, on 35 years in finance and why the job is basically constant change. Troy’s path runs through Xerox, Conduent, and Universal Technical Institute, Inc., with a lot of transformation work in messy, real operating environments.

A few takeaways I’m still thinking about:
▪️ How an investor relations rotation at Xerox shifted his lens from inside-out FP&A to outside-in “what matters to the market,” and accelerated his CFO trajectory
▪️ Why integration is where value gets won or lost after M&A, and how an integration management office prevents functional silos from quietly killing synergies
▪️ What a multi-year enterprise tech shift (including Workday) looks like when “bringing people along” is the real constraint, not the software
▪️ How he’s framing AI’s impact on outsourced services and the labor market, including the “low hire, low fire” dynamic and the gap between exec optimism and worker skepticism

If you lead FP&A, finance transformation, or any function that has to modernize while still reporting clean numbers, you’ll get something out of this one. Full episode and transcript are available now.

https://lnkd.in/g8kgMqDj

Open video: 35 Years in Finance and constant change: Troy Anderson CFO at Kelly Services on youtu.be

35 Years in Finance and constant change: Troy Anderson CFO at Kelly Services

January was a busy month for AI in finance and accounting, so I swapped the usual deep dive for a roundup.

This week’s Deep Finance Dispatch covers what’s actually been announced and shipped so far in 2026: Intuit bringing Quickbooks and TurboTax workflows into ChatGPT, Microsoft pushing Copilot closer to approved actions and execution, Thomson Reuters automating sales and use tax prep with exception-based review, SAP’s Joule updates around traceability and drill-down, and BlackLine’s WiseLayer AI (acquired by BlackLine, NASDAQ: BL) acquisition for close automation. 

I also included early governance signals, including Texas’ Responsible AI Governance Act effective January 1, 2026, plus recent PCAOB commentary on audit documentation and evidence.

It’s a practical scan of the landscape for CFOs, controllers, FP&A leaders, and anyone responsible for close, tax, or audit readiness.

Link in comments.

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