BOTTOM LINE UPFRONT
Private equity–backed companies are adopting paid AI tools faster than the broader market (59% vs. 41%), but still lag venture-backed firms (77%). Despite strong pressure from sponsors, fewer than one-third of CFOs have implemented AI meaningfully, largely due to uncertainty about how to begin.
Adoption gap
The study shows AI use in private equity-backed companies is stronger than in the broader business base, but still constrained by operating realities. Venture-backed businesses are more likely to have built AI into their operations from an earlier stage, while private equity-owned companies often have older systems, established finance processes and more layers of governance.
That difference matters because many buyout-backed companies are under pressure to improve performance within a defined investment period. The report argues that AI is becoming part of that effort, particularly in finance functions, where businesses are trying to improve forecasting, working capital management and reporting speed.
AI can reduce month-end close cycles by up to 30% and lower days sales outstanding by as much as 15%, according to the report. It also links AI use to stronger forecasting and analysis, which can improve earnings before interest, taxes, depreciation and amortisation.
Sector trends
Adoption remains uneven across sectors. Technology, media and telecommunications lead among private equity-backed companies, with a 76% adoption rate, while retail and consumer stands at 39%.
Some of the sharpest increases came in sectors that have historically moved more slowly on digital tools. Manufacturing adoption rose from 27% to 52% over the past year, while healthcare and life sciences increased from 23% to 48%.
Those gains reflect pressure on companies to manage costs, improve efficiency and sharpen planning. In manufacturing, the report points to increased use of AI in forecasting, procurement and financial planning.
One case study involved a private equity-backed industrial company that adopted AI-based financial planning and analysis workflows. The project improved forecast accuracy by 35%, reduced working capital by 8% and halved reporting time within one quarter, according to the research.
Data Problems
Many companies still face basic obstacles that limit the usefulness of AI tools. These include fragmented enterprise resource planning systems, inconsistent data definitions and weak governance over data and reporting processes.
Those shortcomings are particularly relevant for finance teams, which need reliable data for AI systems to produce useful output. The research argues that adoption is not only a matter of buying software, but also of changing operating processes and assigning clear ownership.
That places CFOs at the center of the issue. The study describes the finance function as an increasingly important source of AI-related value creation, as companies use automation and data analysis to monitor cash flow, margins and risk more closely.
The report is based on transaction-level data from Ramp’s corporate spend platform, alongside survey responses from 400 private equity sponsors and CFOs. It concludes that AI is moving from a discretionary experiment to a standard operating requirement in many businesses, even as execution remains uneven across the private equity market.
The full dataset suggests the immediate challenge for many portfolio companies is no longer whether to adopt AI, but how to move from paid subscriptions and pilot projects to broader use in core finance and operating workflows.