Buyout Firms Are Screening for AI Disruption Before They Bid. Tech Services Assets Have Already Repriced.

What's New

AI readiness has moved into entry diligence, and it now helps decide whether a deal proceeds at all rather than what happens after close. Rachel Barton, Global Lead for CEO Advisory and Private Equity at Accenture, argues this in an episode of Private Markets 360. Buyers are applying digital maturity and AI disruption assessments alongside standard diligence, testing data quality, technology architecture, and whether the business itself is a plausible casualty. The trigger was a live repricing. Systems integration, tech services, and software assets that were prized a few years ago have lost appeal because of AI, and firms are pulling the assessment forward so the next reset does not land inside a lengthening hold period.

Why It Matters

The comfortable view is that AI is a value creation lever applied after acquisition. Barton puts it earlier and treats it as a screen. That shifts who loses: sellers of assets with weak data foundations face a discount they cannot fix during a sale process, and sponsors who bought into vulnerable subsectors during the software boom carry it into a market where buyers now have a name for the risk. Accenture sells transformation services to both sides, so the argument aligns with its book.

Big Picture Drivers

• Cheap finance stopped doing the work: Higher financing costs push operational value creation ahead of financial re-engineering. Go to market, operational efficiency, AI, and talent are being applied as direct interventions rather than as support for multiple expansion.

• Longer holds raise the cost of a wrong entry call: Extended hold periods mean a disruption thesis has to survive years rather than quarters. That pushes exit planning to the acquisition date and drives interest in carve-outs and bolt-ons as corporates shed non-core businesses.

• Foresight is the first component of edge: Barton defines edge as knowing something before others and moving before a process starts. AI is being deployed to sense, spot, and identify targets faster.

• Craft is the second component: Firms are specializing into industries and subsectors, building repeatable formulas for specific value creation levers, particularly topline growth. Data and technology do not substitute for being better at something.

• Talent is the third component, and it has a new job title: The most requested search from private equity firms at one headhunting firm is the AI operating partner. Scrutiny has extended past the CEO and CFO to middle management and what Barton calls the next 100 people who hold the control points.

• Overlaying AI produces automation while redesigning around it produces value: The common failure is treating AI as a technology initiative. What gets skipped is the redesign of processes, decision rights, roles, governance, and business models.

By The Numbers

• 20 times: Growth in private markets over the past 25 years, with private equity AUM now reported in double-digit trillions.

• 1.5 times: How much more advanced private equity firms are than corporate buyers at embedding agentic AI into deal theses and value creation plans, from an Accenture survey of roughly 650 dealmakers this year.

• Over 80%: Share of C-suite leaders planning to increase AI investment over the next 12 months, from a separate Accenture survey of more than 3,000 executives.

• 1 in 10: Leaders in that survey who believe a significant AI bubble currently exists.

• Over 50%: Leaders confident that agentic AI initiatives specifically will deliver quantifiable outcomes.

Key Trends to Watch

• AI disruption indices become a standard diligence line item: Terminology will vary by firm, but the assessment is arriving early enough to kill deals. Watch for the first sponsors to publish their scoring approach.

• Sector migration from venture into buyout: Barton points to areas that matured through five or six years of venture activity, sport among them, now attracting private equity. Nordic salmon fishing, battery storage, and machine learning businesses have all seen diligence work.

• Operating partner economics get repriced: If the AI operating partner is the scarcest hire, compensation and equity terms for that seat will move before the broader operating bench does.

• Portfolio companies built entirely on agentic workforces come to market: Barton expects private equity to create categories that have not existed at scale. The first exits from that cohort will set the valuation reference.

Memorable Quotes

• “They are less attractive now because of AI.” On tech services and software assets that were sought after a few years ago, and the repricing that pushed AI assessment into entry diligence.

• “viewing AI as a technology initiative rather than a business transformation” Barton's description of the single biggest mistake firms make, private equity included.

• “the winners are those that are redesigning the business around AI” The test she sets for portfolio companies, against the more common approach of layering AI onto existing workflows.

• “Be really good at something so that you create that edge.” Her closing instruction on sector expertise, and the reason she treats craft as separate from technology.

The Wrap

The thesis holds if AI disruption scoring shows up in signed deals as pricing differences between comparable assets, and if sponsors who redesign portfolio companies around AI post measurably better realized returns than those who automate existing processes. It fails if the assessments become a diligence formality that changes nothing at the bid, or if the software repricing proves cyclical and those assets recover on earnings rather than on architecture. Hold periods are long enough that the evidence arrives slowly. The 2026 and 2027 exit cohorts, priced against the AI readiness of what they are selling, will be the first real test.