In the latest episode of the AI Innovators™ Interview Series, we sat down with Glenn Hopper, Managing Director and Head of AI at VAI Consulting, to explore how artificial intelligence is transforming the finance and accounting landscape. Glenn brings a unique perspective shaped by his experience as a former CFO, author, educator, and advisor. He is the author of the Amazon best-seller Deep Finance: Corporate Finance in the Information Age and the new book AI Mastery for Finance Professionals, and he teaches in the executive education CFO program at Duke University.
Our conversation covered a wide range of topics, including the future of digital transformation in finance, why so many AI projects fail, how organizations can measure success, and why the coming wave of agentic AI could change how businesses operate.
From CFO to AI Innovator
Glenn’s path to becoming a leading voice in AI for finance was anything but traditional. He started his career as a public affairs officer in the U.S. Navy before moving into telecom, product management, and eventually a series of CFO roles. In each chapter of his career, technology played a central role.
“It was a circuitous path towards AI,” he said. “When I was in the Navy, I edited a monthly news magazine at a Naval Technical Training Center in Pensacola, Florida in 1995. And this was the dawn of the internet. We were moving from print to online and from film to digital for photos. At every point in my career, I’ve been an early adopter, whether it was those days in the Navy or when I was in telecom, managing products before I got into finance and making use of technology.”
That early adoption mindset carried into his CFO roles. “I had to lean into technology early,” he explained. “Because I had private equity invested in these companies and you know how they are with the reports they want, I had to be a great report writer and storyteller about the financials. Everything just seemed natural merging with technology for me.”
During the pandemic, Glenn wrote Deep Finance to explore how machine learning could be used in finance. “Back then, it was a very niche book,” he said. “But that was 2021. And lo and behold, a couple of years later, generative AI comes along, and everybody in the world is talking about it.”
Digital Evolution, Not Transformation
One of Glenn’s most compelling points is that “digital transformation” is no longer the right way to describe how organizations should think about technology adoption. Instead, he prefers the term digital evolution.
“I used to say digital transformation, but we’ve been talking about digital transformation for 30 years,” he explained. “If you say transformation, people assume it’s a one and done. So I’m calling it now digital evolution instead of transformation. It’s an ongoing process. It never stops. It’s increasingly difficult and increasingly more imperative.”
At VAI Consulting, that evolution includes both generative AI and what Glenn calls “classic AI.” “Generative AI right now is like the shiny lure that draws people in,” he said. “But a lot of times, especially in finance and accounting, there’s a part for generative AI, but really deterministic, old-fashioned machine learning — we’ll call it classic AI — is more of a solution. We just put a generative AI layer so that it makes the data easier to work with for people who aren’t writing Python or SQL queries.”
Why So Many AI Pilots Fail
A recurring theme throughout the interview was the high failure rate of AI projects, especially in finance. Glenn believes the reasons have little to do with the technology itself.
“I think when you hear about a lot of these projects that are failing and not going anywhere, it’s not a fault of the technology,” he said. “Someone has to come in and help translate the technology into the business domain language and vice versa.”
He pointed to the widely cited MIT statistic that 95 percent of generative AI pilots fail. “The pilots aren’t failing because the technology is bad or not working,” he said. “Pilots are failing because there’s a lack of understanding of how it works, where to use it, what its strengths are and what its limitations are.”
He likened the current stage of AI adoption to the Gartner Hype Cycle. “I think right now we’re over the peak of inflated expectations and plummeting down towards the trough of disillusionment,” Glenn explained. “It’s not because of the technology. It’s because there’s so much noise and so many false promises and so much of a lack of understanding around how to use it that people are just getting frustrated.”
Top-Down and Bottom-Up Adoption
Glenn advocates for a two-pronged approach to AI adoption: top-down investment and bottom-up empowerment.
“The first step is to think of generative AI rolling out in two ways,” he explained. “One is the top down. This is where your big capital investments are — integrating into your systems and making it part of the company’s overall workflow. The other is the bottom up, where you give your employees access to the tools, you give them training, you give them guardrails, you give them understanding of how they work and what their strengths and limitations are.”
This bottom-up approach, he said, often leads to “micro wins” that can add up over time. “If employees are figuring out on their own ways to become more productive, then they can shift their time from those mindless tasks to doing more mindful and more valuable work,” he said. “Nobody goes and gets a master’s in accounting because they love data entry.”
Measuring Success Beyond ROI
Glenn also emphasized the importance of thinking about AI return on investment differently than traditional software projects.
“If you ask me for ROI on this bottom-up version, I’d say, well, we’re talking about a software expense first off,” he said. “What’s your ROI on investing in any SaaS tool you use, whether it’s your ERP, CRM, whatever — it’s part of the work.”
While some automation will replace certain tasks, the bigger impact is in freeing people to do higher-value work and transforming how finance teams contribute strategically. “This is an evolutionary process,” he explained. “Not investing in it now would be like saying we’re not going to invest in going online or we’re not going to invest in cloud computing.”
A Framework for AI Project Success
Glenn shared a practical framework for companies starting their AI journey. “It has to start at the executive level,” he said. “Leadership has to have a thorough understanding of the technology — what it is, what it does, what its limitations are. Then leadership comes up with the guardrails. What are they comfortable with? How are we going to use this? Where are we not allowed to use it?”
From there, organizations should train employees, develop a group of internal evangelists, and focus first on small, low-risk pilot projects. “Find those quick wins, find small projects,” he advised. “Don’t try to eat the elephant all at once.”
The Future: Agentic AI and Dashboarding 2.0
Looking ahead, Glenn sees major changes on the horizon, particularly around agentic workflows and intelligent automation.
“I predicted early this year that 2025 was going to be the year of the agent,” he said. “Truthfully, for a true agent, we’re not there yet. But agentic workflows and truly agentic software are coming. We’re moving ever closer to real-time close, real-time planning, and dashboarding 2.0 — where drivers, scenarios, and narrative reporting are linked and delivered automatically.”
He believes this evolution will fundamentally change how finance teams work with data. “With generative AI, you actually have democratization of data science where people can not just see the data, but they can talk to it, ask it questions, and get more insights out of it,” Glenn said.
Watch the Full Interview
To hear Glenn Hopper’s full conversation on AI strategy, bottom-up adoption, failed pilots, and the future of finance, watch the complete interview below.
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