How Private Equity and Venture Investors Should Evaluate AI-Enabled Operators

How to Evaluate AI-Enabled Operators Before You Invest

Every fund I talk to this year is chasing the same claim: an AI-enabled operator with an edge nobody in the room can actually verify. That is the wrong moment to find out the edge is not real. It should be found in diligence, before the term sheet, and most technical diligence still is not built to find it.

I have sat through enough management presentations to know the tell. A confident demo, a slide with a productivity multiple, and a data room that goes quiet the moment someone asks where the training data actually came from. The demo is usually real. The advantage behind it often is not, and a polished walkthrough under controlled conditions says very little about whether the underlying system is production grade or whether the economics management is claiming actually trace back to it.

Gartner's most recent research on agentic AI governance puts a number on what happens when that gap goes unchecked inside a portfolio company. By 2027, 40 percent of enterprises will demote or decommission autonomous AI agents because governance gaps surface only after a production incident forces the issue, not before one. That is not a technology problem a vendor patches. It is a valuation problem, because the enterprise value a fund underwrote at signing assumed the capability kept behaving the way the pitch deck said it would.

I return to the same four questions on every target where AI is part of the investment thesis, and I do not move past one without evidence, never a slide.

What I ask before a term sheet

1. Whose data is this. If the training and operating data is commodity, licensed under terms that do not survive a change of control, or simply undocumented, the advantage does not transfer at close no matter how good the current output looks.

2. Who maintains it. A model that performs well in a demo and a system that is monitored, retrained on a set cadence, and version controlled are two different assets. I ask who owns that cadence today and what happens to it if that person leaves.

3. Is the governance enforced or only described. A permission boundary that lives in a policy document nobody rereads after launch is not a control. I ask whether an agent's actions are checked at the point of execution or only reviewed after the fact, once the damage is already done.

4. Does the economics hold without the AI narrative. Strip out any revenue or cost claim that depends on an unverified capability and look at what is left standing. If the valuation case collapses without that assumption, the AI story is doing work the underlying business has not yet earned.

   
   

McKinsey's own guidance to private equity firms on generative AI makes the same point from the diligence side, and it is worth taking seriously precisely because it comes from a firm that also sells AI advisory work. Gen AI output is fluent, well organized, and confident, which is exactly why it needs a critical eye and human judgment before anyone trusts it, because a tool that produces something structured and convincing can still be entirely wrong underneath. I hold a target's own AI claims to that same standard, not just the tools my team uses to evaluate them. A polished capability slide has passed no such review, and neither has a claim of proprietary advantage that nobody in the room can trace back to a specific, owned data asset.

The part that should worry every investment committee is what gets inherited after close, not what gets pitched before it. IBM's 2026 Cost of a Data Breach Report puts a figure on that inheritance. Breaches involving a compromised AI model carry an average cost of six million dollars globally, well above the overall average, and the organizations hit hardest are overwhelmingly the ones that never put basic access controls around the system in the first place. A fund does not acquire a capability in isolation. It acquires whatever governance, or absence of it, was already sitting underneath that capability, and that liability transfers at close whether or not it showed up in the data room.

None of this argues against paying for genuine AI capability. It argues against pricing a story as if it were an asset. A target that can show data it actually owns with rights that survive acquisition, a system with a named owner and a maintenance cadence, governance that is enforced rather than merely written down, and economics that hold up once the AI narrative is stripped out is a fundamentally different asset than one that has all four bundled into a single confident pitch. The first is worth a premium. The second is a bet the fund has not actually priced, dressed up to look like diligence that already happened.

Firms serious about closing this gap tend to pair financial and legal diligence with a technical partner who can tell a governed system from a demo on sight. That is the lane BetterWorld Technology works in with its cybersecurity and technical assessment practice. For portfolio companies where the thesis depends on scaling a capability after close rather than just protecting one that already exists, Working Excellence's operational excellence work is what turns an immature system into one with the discipline institutional investors expect to see a year later. 

Put the standing review on the same calendar as the financial covenants, not in a folder that gets opened once at signing and never again. Ask the same four questions at the next board meeting. If the answers have not moved since the deal closed, that tells the committee something too.