What is your private company worth now that buyers are running the numbers through AI? Probably not what your last valuation said. The old comps-and-multiples ritual, three peers, a spreadsheet, a defended EBITDA, still happens on paper. Underneath it, buyers are letting models chew through thousands of transactions, benchmark your margins against invisible peers, and score how AI-native your business really is.
The result isn’t a single number moving up or down. Different kinds of companies are being repriced in different directions, sometimes on the same deal team, on the same afternoon.
Here’s how that’s shaking out across the categories that matter.
AI-Native Software Companies Are Getting a Premium
Companies that genuinely build AI into the product, not a chatbot bolted on the side, are pricing at multiples the rest of the market can’t touch. FE International’s 2026 breakdown puts top private AI deals in the 15 to 30x revenue range, with public comparables running even higher. Traditional SaaS at 5 to 8x looks like a different asset class.
The catch: the models buyers use to justify those prices are unforgiving about proof. Retention curves, gross margin on inference costs, how much of the roadmap depends on a single model provider. A company that describes itself as AI-first without the telemetry to back it up gets marked down fast.
Traditional Businesses Face a Widening Split
Owners of long-running services businesses often assume the AI story doesn’t apply to them. It does, in both directions. As EisnerAmper has noted, buyers now feed a target’s financials, contracts, and headcount into models that estimate how much of the workforce is exposed to automation over the next three to five years. Two businesses with identical EBITDA can come out priced very differently:
- Automation-resistant operators. Trades, physical services, regulated work with real switching costs. These hold their multiples and sometimes gain, because buyers see the cash flows as durable in a world where a lot of white-collar work isn’t.
- Commoditized knowledge shops. Bookkeeping, basic content, tier-one support, routine legal drafting. Models discount these on the assumption that a competitor running on AI will underprice them within a couple of budget cycles.
- Owner-dependent specialists. High-margin firms whose value lives in one person’s head. AI diligence tools flag this concentration risk faster than a human reviewer ever did, and the price reflects it.
Owners Should Move Before Testing the Market
You can’t opt out of how buyers price you, but you can shape what they find. A few things worth doing this year, not the quarter before you sign an engagement letter:
- Get an honest read early. A broker or advisor who works with private company sales can pressure-test what a modern buyer’s model will spit out on your numbers, not just what a rule-of-thumb multiple suggests.
- Clean your data room before diligence. Automated tools flag inconsistencies in customer counts, revenue recognition, and margin trends within hours. Fix those before they become discount points.
- Document where AI already helps you. Even modest, verifiable uses (support deflection, forecasting, quoting) shift how buyers score defensibility.
- Reduce single points of failure. One rainmaker, one platform, one anchor customer. Models penalize concentration harder than human buyers used to.
The price your business commands next year will be set partly by you and partly by software you’ll never log into. Knowing which levers still belong to you is the difference between reacting to a number and shaping one.
