AI is a value creation lever. Most firms don't treat it like one. It sits off to the side as an innovation project, a pilot a portco is exploring, a line item someone will get to. That gap, between how AI is treated and what it can actually do, is where returns are won and lost.
The firms producing the strongest returns don't run AI as a side initiative. They write an AI thesis into the value creation plan the same way they underwrite any other source of value, and they do it early enough for the results to matter at exit.
The problem with a bolt-on approach isn't that the pilots fail. It's timing.
AI-driven improvements take longer to surface than traditional levers, and agentic systems most of all. The technology deploys quickly, but the value depends on adoption, workflow redesign, and the trust to let a system act, and those take time to build. Add the foundational work most portcos need first, cleaning and consolidating data, standing up the infrastructure, and the runway compresses fast. Buyers want to see seasoned, run-rate results at exit, not an initiative that went live last quarter.
Work backward from the exit and the math is unforgiving. A firm that decides to "look at AI" halfway through the hold has already lost the time it needed to prove the results. The initiative may be sound, but it won't season before the window opens, and unseasoned improvements don't move the multiple.
Treating AI as an afterthought doesn't simply delay value. Past a certain point in the hold, it forecloses it.
An AI thesis isn't a list of tools or a mandate to "use more AI." It answers the same three questions the rest of the value creation plan answers.
Where the value comes from. Not every use case is worth pursuing. The thesis names the specific initiatives, many of them now agentic, expected to move EBITDA or defensibility, prioritized by impact and matched to where the portco sits in its hold period. A year-four play looks nothing like a year-one play, and the plan should reflect that.
How it's sequenced across the hold. Foundational work comes first and often produces no direct return on its own, which is precisely why it must be planned and funded early rather than discovered late. From there, initiatives are staged so results have time to step up and season before exit.
How it's measured. Every initiative carries a clear, credible metric from the outset, tracked consistently through implementation. That measurement is what lets you tell a forward-looking value story a buyer can underwrite at harvest.
Answer those three questions and AI stops being a series of disconnected experiments. It becomes a coordinated set of bets whose combined effect shifts the earnings trajectory in a way traditional levers alone can't.
The best time to build the AI thesis is before the deal closes.
Diligence is where you assess a target's real AI exposure and opportunity: where the business is vulnerable to AI-native competition, where its data and workflows create a credible basis for advantage, and what foundational work any of it will require. Bring that lens into diligence and the value creation plan is ready to run on day one, with the foundational investments scoped and the clock already accounted for. Skip it, and you spend the first year of the hold discovering what you should've priced at entry.
This is also where exit-aligned discipline matters most. AI for the sake of AI doesn't create value; it consumes it. The point of the thesis is to prioritize the initiatives that produce material value at the multiple, and to decline the ones that don't, from the moment you underwrite the deal.
This is the work we do with private equity firms. We prioritize the AI initiatives that produce the most material value at exit and take them from concept to production. We engage on strategy alongside portfolio operations and stay embedded to deliver, so the plan doesn't stall between a sound idea and a working system.
This is the first post in a series on AI value creation across the private equity portfolio. It starts here: put AI in the plan, and put it there early.
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