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There is a lot of AI activity inside portfolios right now: process automation, field-service scheduling and dispatch, demand planning, conversational customer support, document and case processing, marketing attribution.
The harder question, the one that matters at exit, is whether any of it changes what a buyer will actually pay.
Value at exit comes from two places: growing earnings, and expanding the multiple those earnings are valued at.
Most AI work in portcos improves the first a little and the second not at all. That's not a failure, but it is a ceiling, and it's worth being honest about which kind of value a given initiative is actually creating.
Automating a back-office process, deflecting support tickets, speeding up a manual workflow: these produce genuine efficiency, and efficiency flows through to EBITDA. That's worth doing. But two things limit it.
First, cost savings improve earnings without expanding the multiple those earnings trade at, so the value is bounded by the lever, not multiplied by it.
Second, and more important, most of these gains are replicable. If the advantage came from deploying the same off-the-shelf tools every competitor can license, a buyer knows the next owner can do the same, and prices accordingly.
An efficiency anyone can buy won't set a portco apart. Every competitor can license the same tools, so by exit the market treats it as table stakes and prices it that way.
Cost-out sets a floor. It keeps a portco competitive, but it rarely makes one more valuable than its peers.
The AI that moves the multiple is the AI that shows up where the customer can feel it, and that a competitor can't reproduce by writing the same check. Two tests matter:
Back-office savings live in the P&L. A premium lives in something the customer experiences:
If the gain never reaches the customer, it lowers costs without lifting value.
A model on its own won't protect you; the underlying tools are available to everyone. Defensibility comes from what surrounds the model:
That second combination is what a buyer will underwrite a premium against, because it's what they can't simply purchase after close.
Buyers don't pay a premium for potential. They underwrite it against evidence, and evidence is what holds up in diligence. By the time you're at the table, that means three things:
Get those right and the AI story becomes something a buyer can put in their own model with confidence. Get them wrong and even real capability reads as risk, which discounts rather than adds.
For a firm that plans ahead, the opportunity is an arbitrage on the multiple itself. A portco can be acquired at a conventional multiple, have genuine AI capability built during the hold, and exit at a premium the market hadn't priced in at entry.
That delta is real, but only for firms that start early enough to build something defensible and seasoned by the time they sell.
That's the throughline of this series: the value only lands if AI is in the plan from the beginning, prioritized against where the portco sits in its hold, and engineered to survive the scrutiny of a buyer. Efficiency you can bolt on late. A premium you have to build.
We help private equity firms separate the AI work that trims costs from the AI work that moves the multiple, then build the latter and prove it.
That means identifying the defensible, market-facing opportunities in a portco, engineering them from concept to implementation on data foundations and governance that hold up in diligence, and standing up the evidence a buyer will underwrite. Exit-aligned from the first workshop, so the value shows up in the multiple, not just the margin.
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We're offering $50K in funding for PE-backed businesses investing in AI innovation. Here's how it works:
This is the second post in a series on AI value creation across the private equity portfolio.
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