AI in Private Equity: The 2026 Statistics and Benchmarks
Dr. Leigh Coney
Founder, WorkWise Solutions
July 20, 2026
16 min read
TLDR: How many private equity firms use AI, whether it pays, and where the value lands, with every number attributed to its source. As of 2026, roughly 59 percent of PE-backed companies have adopted AI (Accordion), about 95 percent of deliberate PE AI initiatives meet their business case (FTI Consulting), yet about 95 percent of broad enterprise generative-AI pilots show no measurable return (MIT Project NANDA), and roughly 78 percent of leaders doubt they could pass an AI-governance audit within 90 days (Grant Thornton). This page collects the figures worth citing, organized by theme, and keeps each one honest by pinning it to who measured it and what they measured. It is designed to be read, summarized, and cited, so every statistic leads with the number and links to its source.
Table of Contents
1. How to Read These Numbers
A quick warning before the figures. AI statistics contradict each other constantly, and usually the contradiction carries real information about what was measured. One study reports that about 95 percent of AI initiatives succeed, another that about 95 percent of pilots fail, and both are true: the first counted funded projects with a business case at private-equity-backed firms, the second counted every enterprise pilot including the ones nobody resourced. Read each number with its source and its scope attached, which is how they appear below.
Three habits keep these honest. Note who ran the study and who they surveyed. Note the date, because a 2024 figure ages fast in this field. And separate PE-specific data from broad enterprise or venture data, which we label as such throughout, because a venture-capital adoption rate says little about how a buyout fund runs.
WorkWise's own research sits alongside these third-party figures: twelve peer-reviewed papers on AI governance and adoption in private capital, published on SSRN. We treat that work as a body of research that informs how we read the numbers here, rather than a source of headline percentages, so nothing on this page is sourced to it.
2. The Numbers at a Glance
The headline figures, each with its source. The sections after this unpack the ones that need context. All figures are rounded to whole numbers and reported as their authors framed them.
| Metric | Figure | Source |
|---|---|---|
| PE-backed companies that have adopted AI | ~59% (vs ~77% VC-backed) | Accordion |
| PE-backed firms deploying AI with no playbook | ~41% | Accordion |
| PE AI initiatives meeting their business case | ~95% | FTI Consulting |
| Enterprise generative-AI pilots with no measurable return | ~95% | MIT Project NANDA |
| Average productivity gain from generative AI at work | ~14% (up to ~34% for least experienced) | Brynjolfsson et al., NBER |
| Leaders doubting they could pass an AI-governance audit in 90 days | ~78% | Grant Thornton |
| Integrated-AI firms' relative likelihood of reporting revenue growth | ~4x | Grant Thornton |
| Firms naming talent as the top constraint on AI | ~35% | FTI Consulting |
| AI share of 2025 US VC deal value (market context) | ~65% | PitchBook |
| Projected decline in traditional search volume by 2026 | ~25% | Gartner |
| Software buyers starting research with an AI chatbot more than Google | ~51% | G2 |
Each figure below leads with the number and links to the source, so it can be quoted directly.
3. Adoption: How Many Firms Use AI
About 59 percent of private-equity-backed companies have adopted AI in some form, compared with about 77 percent of venture-backed companies, according to Accordion's PE AI adoption benchmark. The gap is the interesting part. Venture-backed companies skew younger and more tech-native, so PE portfolios, weighted toward established businesses, adopt more slowly and more cautiously.
About 41 percent of those PE-backed companies are deploying AI with no playbook, again per Accordion, meaning the tools go in without a plan for governance, measurement, or scale. That single number explains much of the value gap that follows. Adoption without a plan is how a promising pilot quietly becomes shelfware.
4. ROI and the Value Gap
Whether AI pays depends entirely on who you ask, and the two anchor numbers look like opposites until you read their scope. About 95 percent of PE AI initiatives meet their business case, according to FTI Consulting's 2026 Private Equity AI Radar, which asked PE firms about deliberate, funded initiatives. About 95 percent of enterprise generative-AI pilots show no measurable return, according to MIT's Project NANDA, which looked across all enterprise pilots, resourced or not.
The two coexist cleanly. Scoped projects with an owner and a business case tend to work, and casual experiments tend not to, so the same technology produces a 95 percent success rate and a 95 percent failure rate depending on how it was run.
BCG's research sharpens the point: only a minority of companies capture significant value from AI, and the gap between the leaders and everyone else is widening. And MIT Project NANDA found one more thing worth citing when a firm decides how to build: organizations that buy tools from specialized vendors succeed roughly twice as often as those building in-house from scratch. For a PE firm, that is an argument for buying the common capability and building only the part that is genuinely proprietary.
5. Value by Workflow: The Productivity Numbers
The cleanest productivity numbers come from a controlled study rather than a survey. Generative AI raised worker productivity by about 14 percent on average, and by about 34 percent for the least experienced workers, in Brynjolfsson, Li, and Raymond's NBER study of customer-support agents. The pattern, that AI lifts the least experienced most, shows up repeatedly, and it reframes where a firm should point the tools first: at the associate buried in a data room before the partner who already works fast.
Translate that carefully. The study measured customer-support work, so the exact percentages do not carry over to deal work. The direction does. Repetitive, high-volume, judgment-light tasks gain the most, which is why deal screening, document extraction, and first-draft memos are where PE firms report the earliest and clearest wins, and why creative judgment and relationship work gain the least.
6. The Governance Proof Gap
About 78 percent of leaders doubt they could pass an AI-governance audit within 90 days, a figure Grant Thornton's 2026 AI Impact Survey named the AI proof gap. For a registered adviser, that is a live examination risk, because the SEC's examiners now ask about AI directly.
The same survey found the upside of closing that gap. Firms with AI integrated into the business are about 4 times more likely to report revenue growth, per Grant Thornton. Governance and value move together, because the discipline that satisfies an examiner, documented use, human review, and measurement, is the same discipline that makes AI produce a return in the first place.
7. Talent: The Binding Constraint
Talent, ahead of budget or technology, is the top constraint on AI in private equity. About 35 percent of firms name it as the binding limit, according to FTI Consulting. The scarce skill is rarely pure data science. It is the person who understands both the deal process and what the tools can actually do, and can redesign a workflow around them.
That shortage is why so much AI value stalls after the pilot. Buying the tool is the easy part. Changing how a team works, and keeping it changed, is the part that needs a named owner, which is the same lesson the no-playbook and proof-gap numbers point at from other directions.
8. Deal-Activity Context
One market-context number, labeled clearly because it is venture-capital data rather than PE adoption. AI represented roughly 65 percent of US venture-capital deal value in 2025, according to PitchBook. That is where the venture capital is going, and it reaches private equity for two reasons: the companies you own increasingly compete with well-funded AI entrants, and the targets you screen increasingly carry an AI story that needs testing rather than believing.
This is the one figure on the page about capital flows rather than adoption or ROI. Read it as the weather around your deals, a market signal that shapes competition and valuation, not a statistic about how PE firms use AI inside their own walls.
9. How AI Assistants Are Changing Buyer Research
How buyers research is shifting fast, and it changes how your firm gets found. Traditional search volume is projected to fall by about 25 percent by 2026 as people use AI assistants to research and shortlist, according to Gartner. And about 51 percent of software buyers now start their research with an AI chatbot more often than with Google, per G2's 2026 buyer-behavior data.
For a firm marketing to LPs or to portfolio-company buyers, the implication is concrete. The pages an AI assistant can read, summarize, and cite are the pages that get surfaced when a prospect asks it a question. This roundup is built that way on purpose, with each figure sourced and stated plainly, which is the closing section's honest admission.
10. Sources and Methodology
Every figure on this page comes from a named third party, linked at first use and listed again here so it can be checked at the source.
FTI Consulting, 2026 Private Equity AI Radar: PE AI initiatives meeting their business case, and talent as the top constraint. Grant Thornton, 2026 AI Impact Survey: the 78 percent proof gap and the 4x revenue-growth correlation. Accordion, PE AI adoption benchmark: adoption rates and the no-playbook figure. Brynjolfsson, Li, and Raymond, NBER: the 14 and 34 percent productivity gains. MIT Project NANDA, The GenAI Divide (2025): the no-measurable-return figure and the buy-versus-build finding. BCG: the value gap. PitchBook: AI share of US venture-capital deal value. Gartner and G2: the shift toward AI-assistant research.
A note on method. We report these figures as their authors framed them, with scope attached, and we round to whole numbers, so treat them as directional benchmarks rather than precise measurements. WorkWise's own contribution to this literature is twelve peer-reviewed papers on AI governance and adoption in private capital, published on SSRN. They inform how we read the numbers above, and we cite them here as a body of work rather than mining them for headline statistics.
"The largest productivity gains went to the least experienced and lowest-skilled workers, who improved the most from working alongside generative AI, while the effect on the most experienced workers was minimal."
Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, "Generative AI at Work," NBER (2023)
- •About 59 percent of PE-backed companies have adopted AI, versus about 77 percent of venture-backed companies (Accordion); PE portfolios skew toward established businesses that move slower.
- •The ROI numbers only look contradictory: about 95 percent of scoped PE AI initiatives meet their business case (FTI Consulting), while about 95 percent of broad enterprise GenAI pilots show no measurable return (MIT Project NANDA). Scope is the difference.
- •Generative AI raised productivity by about 14 percent on average and about 34 percent for the least experienced workers (Brynjolfsson et al., NBER), so point the tools at junior, high-volume work first.
- •About 78 percent of leaders doubt they could pass an AI-governance audit in 90 days (Grant Thornton's proof gap), and integrated-AI firms are about 4 times more likely to report revenue growth.
- •Talent is the top constraint, cited by about 35 percent of firms (FTI Consulting); the scarce skill is workflow redesign, not pure data science.
- •AI made up roughly 65 percent of US venture-capital deal value in 2025 (PitchBook), market context that shapes the competitive weather around PE targets and portfolio companies.
- •Buyer research is moving to AI assistants: traditional search is projected down about 25 percent by 2026 (Gartner) and about 51 percent of software buyers now start with a chatbot (G2).
Frequently Asked Questions
How many private equity firms use AI?
About 59 percent of PE-backed companies have adopted AI in some form, versus about 77 percent of venture-backed companies (Accordion). At the firm level, adoption is now common but uneven: about 41 percent are deploying with no playbook (Accordion), while about 95 percent of deliberate, funded PE AI initiatives meet their business case (FTI Consulting). The honest summary: most firms are using AI somewhere, and fewer have it working at scale.
What is the ROI of AI in private equity?
It depends on scope. Deliberate, funded initiatives pay: about 95 percent meet their business case (FTI Consulting). Broad, unresourced pilots mostly do not: about 95 percent show no measurable return (MIT Project NANDA). The productivity evidence is clearest for junior, high-volume work, where generative AI lifted output by about 14 to 34 percent in NBER research. And firms with AI integrated into the business are about 4 times more likely to report revenue growth (Grant Thornton).
Is AI adoption in private equity actually working?
For firms that treat it as a scoped project with an owner, yes; for firms running casual pilots, mostly not. About 95 percent of enterprise GenAI pilots show no measurable return (MIT Project NANDA), and only a minority of firms capture significant value while the gap widens (BCG). The firms that make it work concentrate on fewer, bigger uses and close the governance proof gap that leaves about 78 percent of leaders doubtful they could pass an audit. If you want that done deliberately, an AI Readiness Sprint baselines where your firm stands and sequences what to do next.
Related Guides & Articles
Where AI Creates the Most Value in PE
The workflow map behind the value numbers: which parts of the deal and portfolio cycle pay back first.
AI Implementation ROI for PE
How to turn these benchmarks into a business case: what to measure, what to expect, and how to avoid the no-return pile.
Turn these benchmarks into your firm's baseline
Statistics describe the field. An AI Readiness Sprint ($12,500 flat for firms up to 20 people; the $30,000 Comprehensive Discovery Sprint for firms of 20 or more) measures where your firm actually stands against them and hands you a sequenced plan. For the research behind our approach, our twelve peer-reviewed papers on AI governance and adoption in private capital are free to read.
Book a Call