Private equity faces AI-readiness gap after $1.2T 2025 deals

US private equity firms completed more than $1.2 trillion of deals across over 9,000 transactions in 2025, yet many lack standard ways to assess how artificial intelligence will affect asset value and risk. PitchBook data show the volume of activity, while investors and advisers report varying approaches to measuring AI capability.

At acquisition, management teams often present AI roadmaps and demonstrations while core systems, data and governance remain unchanged. Weak data foundations, fragmented systems or small engineering teams can limit the ability to turn AI experiments into sustained margin improvement. After buyouts, those technical and organisational gaps can delay or reduce expected operational gains.

At exit, potential buyers seek evidence that AI-linked margin gains are real, repeatable and defensible against competitors and changing costs. Industry practitioners say due diligence should focus on whether data are accurate, accessible and governed; whether the technology architecture can deploy and monitor models; whether specific use cases link to revenue, cost, working capital or risk metrics; whether pilots have moved into workflows used by employees or customers; and whether privacy, security, intellectual property and vendor dependence have been addressed.

RSM’s guidance on AI due diligence sets out one model that private equity teams are adopting. The recommended output is a documented baseline of current capabilities, the most material gaps, the cost of remediation and a prioritised list of use cases with named owners, required investment and expected financial impact. That approach aims to give AI readiness a financial shape that can be compared across potential deals.

Valuation practice remains unsettled. There is no widely accepted method to convert model risk, data quality or AI maturity into a precise multiple adjustment across sectors. A company with strong current EBITDA but poor data foundations may struggle to automate or launch AI-enabled products. Conversely, a company with polished demonstrations may rely on a single vendor or a tiny technical team and lack evidence of customer adoption.

Recent surveys show mixed expectations. Bain research indicates general use of AI across sourcing, diligence and value creation, while a joint outlook with StepStone found 39% of general partners did not expect AI to deliver a material financial impact on portfolio companies in 2026. Alvarez & Marsal reports that more than 80% of European respondents were using or experimenting with AI in due diligence and building it into investment theses. In North America, 73% of respondents expected AI to increase portfolio value over the following 12 months, but only 8% described their firm as leading in the area.

Advisers recommend several practical steps for sponsors. Make AI assessment a defined workstream within commercial and technology diligence and require evidence on data accessibility, architecture, production use cases, talent, governance and third-party dependencies. Score capability across consistent dimensions-data quality, process maturity, production deployment, leadership ownership, workforce capability and governance-rather than counting licences or pilot counts. Translate findings into the value-creation plan by assigning each priority use case an owner, a baseline, an investment requirement and measurable outcomes. Track adoption, productivity, margin and revenue impacts to build an evidence trail for exit.

Measurement remains a gap. The market has not produced a sector-wide model for pricing risks such as hallucinations, vendor dependence or the longevity of AI-driven margin improvements. Firms continue to develop internal standards and evidence frameworks to make AI a quantifiable part of diligence and value-creation work.