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A company generating a billion dollars in revenue today can still be worth far less than its valuation suggests. If AI is about to automate 40 to 60 percent of that company’s value chain, the number on this year’s income statement may not survive contact with next year’s competitive landscape.
That’s the premise behind “Due Diligence Reimagined,” a paper from Frank Kurre, Managing Director and Global Board Governance Program Leader at Protiviti. Kurre has spent over 40 years advising boards, and today he leads Protiviti’s work across three of the world’s largest private equity firms. That work puts him inside many conversations every year with directors, C-suite executives, and PE investors trying to answer the same question: is this business built for what AI is about to do to its industry?
We sat down with Kurre to unpack his framework, and the conversation kept circling back to a theme that outlasts any single deal: boards that get good at evaluating AI risk in an acquisition target are the same boards that need to evaluate it in themselves.
Why Today's Revenue Doesn't Guarantee Tomorrow's Value
Traditional due diligence checks the financials, the operations, the technology stack. Kurre’s argument is that none of it answers the question that matters most in an AI-driven market: what happens to this company’s revenue when AI changes what customers expect, what competitors can build, and what it costs to deliver the same product?
“This focus on really understanding a company, whether we're on the buy side, the sell side, thinking about an IPO, really understanding the impact potentially that AI will have on that company's revenue streams, operations, the way they engage with their clients or customers. These are going to be critical factors as we go forward.”
He points to a historical parallel every board has lived through in some form: the shift from mainframe computing to the cloud. Established companies had sunk hundreds of millions into infrastructure they couldn’t easily write off, while smaller competitors built on cheaper, newer technology and overtook them. AI is repeating that pattern, except faster, and the boards that don’t see it coming risk becoming, in Kurre’s words, “the next Blockbuster Video.”
For directors, that reframes the diligence question entirely: what happens to this company’s performance once AI resets the cost of doing what it does.
The stakes show up clearly whenever a board restructures how a company is owned. When a business separates, merges, or goes public, the board that governs it needs a composition built for what’s coming, not what came before. Our look at corporate spinoffs covers the same instinct from a different angle: getting board buildout right at the moment of separation is what determines whether the new company thrives or struggles.
Seven Dimensions, Three That Should Keep You Up at Night
Kurre’s framework breaks AI maturity into seven dimensions: strategy, adoption, governance, risk and compliance, people, products and services, and partners and platforms. Asked which ones deserve a board’s limited attention first, he didn’t hesitate.
“If I could only focus on three: it would be strategy, it would be governance, and it would be the people.”
The urgency behind that answer comes from how fast the ground is shifting underneath boards that assume they have time. “There have been many executives out there, many board members who thought, ‘Okay, we’ve got plenty of time before AI gets fully implemented.’ But what’s happening is the implementation of AI is rapidly accelerating,” he said, citing one client whose AI usage quadrupled in the second half of 2025 compared to the first half of the same year.
The people dimension matters just as much as strategy, and Kurre draws a distinction that’s easy to miss: AI fluency can’t stay theoretical. Employees need to actually build with the tools, not attend a training session and move on. He recounted a Wharton graduate course where the professor left students with three rules that apply just as well in the boardroom: don’t let AI erode your critical thinking, keep building the interpersonal skills a machine can’t replicate, and understand exactly which AI capabilities your organization has actually deployed. Miss any of those, and an individual’s or a company’s competitive edge erodes fast.
What Good AI Governance Looks Like (And Why Most Boards Fall Short)
Every dimension in Kurre’s framework connects back to governance, and this is where he sees the widest gap between what boards think they’re doing and what’s actually in place.
“Good governance from an AI perspective, first of all, includes maintaining clear decision rights,” Kurre said. “Also, modeling in advance of implementation and ensuring that the models are working well, I think that’s really critical.” Companies without that discipline, he added, “typically cannot deploy AI quickly enough, efficiently enough, and safely enough,” which opens gaps that translate directly into future risk.
That governance gap doesn’t just show up in a target company’s board materials. It shows up in how a company understands its competitive exposure. Kurre described asking a simple question in nearly every diligence conversation: what does your competitive intelligence function actually tell you about how rivals are deploying AI into their products? Most companies, he said, don’t have a good answer. Some have a traditional competitive intelligence function that tracks pricing and marketing, but nothing that tracks how competitors are embedding AI into the customer experience itself.
That blind spot is exactly the kind of gap our recent piece on why AI strategy is already behind explored from a different angle: most boards still don’t have a formal AI strategy, and the ones without one are the ones least equipped to see disruption coming.
Turning the Framework Inward
Kurre’s model was built to evaluate acquisition targets, but we asked whether a board could point the same framework at its own organization.
“Definitely, our framework could be used by a board to better assess their AI, their governance, their strategy, the capability, the agility,” Kurre said. He drew a comparison to how boards learned to handle cybersecurity oversight: naming a single Chief Information Security Officer doesn’t absolve the rest of the C-suite of responsibility. “Each of you needs to be the head of cyber for the firm,” he recalled one CEO telling an executive team. “I would say the same thing applies to AI… the CEO of every firm should be that chief AI transformation officer, the CFO, the COO, the CMO.”
That kind of distributed ownership is what turns AI from a defensive checkbox into a real competitive advantage, because the board isn’t waiting on one person to catch everyone else up.
His broader point is that AI oversight can’t live in one office. It has to be distributed across every function that touches the business, because the alternative is a board that thinks it has covered AI governance when only one person in the room actually understands it. Our breakdown of the governance questions boards can’t answer points to the same structural problem: the hardest governance insights are the ones that require connecting information across time and across functions, not the ones visible in a single meeting.
The Guardrails Boards Should Demand From Their Own AI Tools
The conversation’s sharpest turn came when we asked about a layer of risk that gets far less attention than it deserves: directors themselves using AI to prepare for meetings.
“There have been quite a few instances already where board members and C-suite were not properly educated and used the public GPTs that are out there,” Kurre said. Directors put board materials into public tools “where it wasn’t secured,” a habit that risks exposing information “which may be high risk information,” including details that haven’t been disclosed in public filings.
His prescription was direct: boards need to understand exactly which internal systems handle AI work, confirm that data stays inside the organization rather than training a public model, and treat “what tools are available” as a governance question with the same weight as any other oversight responsibility. “No one can never guarantee you 100% security on anything,” he said, “but I think that’s really critical for the future.”
That guidance lines up closely with what we’ve found digging into AI security risks more broadly: the exposure isn’t usually a headline-grabbing hack. It’s a director pasting sensitive material into an unsecured chat window because no governed alternative was ever put in front of them.
And it’s the same logic behind how boards are building AI oversight into their own governance practices. The tools a board uses for its own work need to meet the same bar as the systems it expects a portfolio company to have in place.
One Step Before Your Next Board Meeting
Asked for a single practical action a director could take before their next meeting, Kurre pointed to something more personal than a policy checklist: continuous, hands-on AI fluency.
“One of the significant topics that boards are looking for are board members who have real hands-on AI current experience,” he said, drawing on years spent helping board candidates land their first or next seat. His advice: take stock of how you’re actually using AI today, and look for a way to go deeper, whether that’s a university certificate program or training through an organization like the National Association of Corporate Directors.
He offered a caution alongside the advice. “When somebody says my company is at the cutting edge, is transformational in how we’re implementing AI, I get a little nervous when I hear that,” Kurre said, “because we honestly don’t know what’s going to come down the pike a week from now, a month from now, a year from now.” Confidence without continued learning is its own kind of exposure.
The Thread Running Through All of It
Kurre’s framework was built for deal teams evaluating acquisitions, but the questions underneath it apply just as directly to the board doing the evaluating. What’s our AI strategy? Who actually governs how AI gets used here? Are the people in this organization building with these tools or just talking about them? And when directors themselves reach for AI to prepare for a meeting, is that tool held to the same standard as everything else in the governance record?
That last question is the one we think about most. Directors want the benefits of AI: faster prep, sharper context, less time lost to information they’ve already seen before. What they can’t get around is that the tool doing that work is handling the same sensitive material as everything else on the governance record, and it deserves the same scrutiny.
A board that knows how to size up AI risk in a target company should be just as comfortable asking whether its own directors are prepping for meetings inside the security perimeter the governance team already trusts, or somewhere outside it.
That’s a question worth answering honestly, not assuming away. The boards that get it right are the ones treating director AI use as a governance decision, not a workaround directors quietly figure out on their own.
See how OnBoard AI gives directors governance-grade AI built to run inside the portal, so your board can move up the AI maturity ladder without sending sensitive material outside it.
Read Frank Kurre’s full paper, “Due Diligence Reimagined: AI’s Impact on Valuations,” for the complete seven-dimension framework.
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About The Author
- Ben Blanc is the Brand Narrative Manager at OnBoard, where he shapes the company's public voice across social media, live programming, and external communications. With 18+ years of experience spanning media, operations, and marketing, he brings a blend of storytelling instinct and editorial discipline to B2B SaaS. Ben has spent his career turning complex ideas into clear, accessible, and actionable narratives. At OnBoard, his focus is on thought leadership grounded in real customer proof, credible perspective, and content worth paying attention to.
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