​Why AI Governance Is Now A PE Diligence Issue

​Jay Hawkinson is a board-certified (NACD.DC) data and digital leader turning data into margin through AI, analytics and strategy.

I’ve sat through a lot of PE diligence conversations over the years, and lately, I’ve noticed a pattern: Buyers are asking about AI systems as part of quality-of-earnings and operational review. Who is responsible for that system? What happens if it makes a wrong call?​

The problem is that many organizations can’t answer those questions. The company being acquired often uses AI to do things like set prices, forecast revenue or control product quality. These are systems that sit inside the numbers. But no one ever formally documented who owns the system or how decisions it makes get reviewed.

To a buyer, an undocumented authority is a liability. If something goes wrong after the acquisition, they’ve inherited a system with no accountable owner and no audit trail. So they either discount the purchase price or flag it as a risk. That’s why organizations need to have a clear understanding of what’s going on and how to avoid it.​

Why This Is Happening Now

Ongoing regulatory developments and lawsuits are driving the shift in expectations. The EU AI Act, Fannie Mae lender letters and a growing body of state-level laws all place the documentation and ownership burden on the company running the system, not the company that built it. Look at what happened with UnitedHealthcare: An AI system was making decisions about which claims should and shouldn’t be approved, leading to the critical question of whether people had any authority to override them.​

PE firms are getting smarter about all of this because they’re the ones taking on the liability. They don’t want to buy a company whose entire forecast is built on an AI projection that nobody truly understands. Once a system influences pricing, forecasting or batch disposition, it falls under the quality-of-earnings review.​