AI in Private Equity: 50 Key Statistics for 2026
Dr. Leigh Coney
Founder, WorkWise Solutions
February 24, 2026
14 min read
This is a curated collection of 50 verified AI statistics relevant to private equity, family offices, private credit, and independent sponsors. Every number comes from a named source: Stanford HAI, a16z's Big Ideas series, Bain, BCG, McKinsey, Andrew Ng, Kai-Fu Lee, academic researchers, and SEC filings. Organized into six categories covering adoption, ROI, investment, PE industry data, governance, and technology trends.
Numbers without context are noise. Context without numbers is opinion. This page gives you both.
I wrote this because PE partners, family office principals, and private credit teams kept asking me the same things. How fast is adoption really moving? What does the ROI data say? How big is the AI wave, and does it matter for my portfolio?
Every stat below is sourced. Every source is named. Nothing from press releases or vendor marketing made the list. Everything here comes from peer-reviewed research, institutional surveys, and named experts worth citing.
1. AI Adoption Rates
The numbers tell a clear story. AI is no longer an experiment. It is a baseline expectation, and firms still treating it as optional are already behind.
1. 72% of organizations reported adopting AI in 2024. This is from McKinsey's annual global survey, cited in the Stanford AI Index Report 2025. Two years ago the number was below 50%. The acceleration is not gradual. It is a step function.
Source: McKinsey Global Survey, cited in Stanford AI Index Report 2025
2. 78% of organizations have adopted AI when including generative AI, up from 55% in 2023. The jump from 55% to 78% in a single year is the fastest adoption increase Stanford HAI has ever recorded. GenAI did not just add a few percentage points. It pulled an entirely new cohort of organizations into the AI-adopter category.
Source: Stanford HAI, April 2025
3. 15% of US worker tasks could be completed significantly faster with LLM access alone. With software built around those models, that number rises to 47-56%. The gap between those two numbers is the entire case for AI consulting. Raw model access is not enough. The value is in the systems built around the models.
Source: a16z, 2025
4. The US ranks 20th on the AI Adoption Index. Singapore is 1st, UAE is 2nd. The country that builds most of the world's AI models is not leading in deploying them. For PE firms with global portfolios, the adoption opportunity at US portfolio companies may be larger than at some Asian and Middle Eastern ones.
Source: a16z, Early 2026
5. Shopify CEO Tobias Lutke declared that "reflexive AI usage is now a baseline expectation" for all employees. Not a suggestion. Not a pilot. A baseline expectation. When a public company CEO makes AI a condition of employment, adoption has moved from early to required.
Source: Tobias Lutke, CEO of Shopify, March 2025
6. 80% of corporate knowledge lives in unstructured formats. Emails, PDFs, Slack messages, meeting notes, spreadsheet comments. AI can now process all of it. Traditional software could not. For PE firms, the biggest gains are not in structured financial data your existing tools already handle. They are in the 80% of information a human still has to read, interpret, and act on.
Source: a16z, 2026
2. AI ROI and Productivity
ROI claims are everywhere. Most are vendor-funded. The numbers below come from academics and institutions with no product to sell.
7. BCG consultants with AI access completed 12.2% more tasks, worked 25.1% faster, and produced 40%+ higher quality output. This is from the Harvard/Wharton/BCG study that remains one of the most rigorous controlled experiments on AI productivity. The subjects were not students. They were professional consultants at a top-tier firm, performing real work tasks.
Source: Dell'Acqua, Mollick et al., 2023 (Harvard/Wharton/BCG study)
8. Bottom-half performers saw a 43% quality improvement with AI, compared to 17% for top performers. This is the most important finding in the AI productivity literature for PE firms. AI does not just make your best people slightly better. It makes your average people significantly better. For firms that struggle to recruit top talent (most firms), that changes the economics of team building.
Source: Dell'Acqua, Mollick et al., 2023
9. Customer support agents saw a 14% productivity increase with AI assistance. Measured across thousands of agents at a Fortune 500 company. The key finding: the improvement came from AI helping agents find the right information faster, not from AI replacing human judgment.
Source: Brynjolfsson, Li, and Raymond, 2023 (Stanford/MIT study)
10. Novice workers saw a 34% productivity improvement with AI. Experienced workers saw minimal impact. This matches the BCG findings. AI is an equalizer, not an amplifier. If your portfolio company's edge depends on having the most experienced team, AI does not widen that moat. If it competes on speed and volume, AI changes the math on who you need to hire.
Source: Brynjolfsson et al., 2023
11. AI programs should show concrete results within 6-12 months. Andrew Ng has said this repeatedly, and it is still the best benchmark for PE firms judging AI initiatives at portfolio companies. If a program cannot show measurable results within a year, AI is not the problem. The scope is.
Source: Andrew Ng, 2019 (AI Transformation Playbook)
12. AI is projected to create $13 trillion in GDP growth by 2030. This is the McKinsey/PwC number Andrew Ng frequently cites. Even if it lands at half that, it is the largest technology-driven economic shift since the internet.
Source: Andrew Ng, citing PwC projection
3. AI Investment and Market Size
Follow the money. Where capital flows tells you where the smart money sees returns. These numbers show the scale of the AI investment wave, and the collapsing cost curve that makes deployment accessible to firms of every size.
13. Global corporate investment in AI reached $67 billion in 2023, nearly 8x the 2020 level. This is not VC money or government grants. This is corporate spend on AI capabilities. The curve is steepening, not flattening.
Source: Stanford HAI, 2025
14. Training GPT-4 cost $78 million. Google's Gemini Ultra is estimated at $191 million. These numbers explain why only a handful of companies can build frontier models. Using those models is a completely different cost equation, and that cost is falling fast.
Source: Stanford HAI, 2025
15. AI model costs dropped 500x in 18 months while capabilities improved. Kai-Fu Lee calls this one of the most underappreciated dynamics in AI. The models get better while getting cheaper. In most industries, quality and cost move together. In AI, they move in opposite directions.
Source: Kai-Fu Lee, 2024
16. 80% year-over-year decrease in the cost of generative AI models. Andrew Ng puts it bluntly: the cost barrier that kept mid-market firms from deploying AI is gone. What remains is the knowledge barrier. Knowing what to build and how to build it.
Source: Andrew Ng, March 2025
17. The coding agent ecosystem generated $1 billion in new revenue in 2025 alone. That is a single slice of AI tooling. It shows how fast new market categories are forming. For PE firms evaluating software companies, the question is no longer "does this company use AI?" It is "can this company survive against competitors that do?"
Source: a16z, 2026
18. Claude Code reached $1 billion annualized revenue run rate in 6 months. For context, Slack took 5 years and Zoom took 4 years to hit the same milestone. AI products are reaching scale faster than any previous generation of enterprise software.
Source: a16z, Early 2026
4. PE Industry Data
The private equity industry's own numbers show why AI matters. A $10.5 trillion industry with median single-digit IRRs has obvious room to improve operations. The capital deployment data is especially striking.
19. Private capital fund AUM grew 15-fold (14% per year) over 25 years. The industry is much bigger now. That means more deals to screen, more portfolio companies to monitor, and more investor reports to produce. Manual processes that worked at $500 million do not scale to $5 billion.
Source: Phalippou, 2024
20. There are 12,306 private capital funds managing $10.5 trillion in total AUM. That many funds makes competition for deals intense. Any operational edge, in screening speed, due diligence thoroughness, or portfolio monitoring, compounds over hundreds of investment decisions.
Source: MSCI database, cited in Phalippou 2024
21. Median IRR across all private capital funds: 9.1%. Not the top-quartile number you see in marketing materials. The median. Half of all funds are below this. At these returns, small operational improvements, whether faster deal screening or better portfolio monitoring, move the needle.
Source: Phalippou, 2024
22. Carlyle Group manages nearly half a trillion dollars in client assets. David Rubenstein mentioned this while describing how the industry evolved from small, relationship-driven shops into institutional-scale asset managers. That shift demands institutional-scale tools.
Source: David Rubenstein, Carlyle Group, 2024
23. The Capital Deployment Factor of PE funds rarely exceeds 60% during a fund's lifetime. A big chunk of committed capital sits idle. AI-powered deal screening and sourcing can help firms find and evaluate opportunities faster, improving deployment and reducing the drag from uninvested capital.
Source: Pintado and Spichiger, 2025
24. For the median PE fund, only 28.2% of capital paid in was actually deployed. Read that again. Roughly 70% of capital called from LPs went to fees, expenses, and recycling rather than direct investment. That puts enormous pressure on GPs to maximize returns on what does get deployed, which makes better deal selection critical.
Source: Pintado and Spichiger, 2025
5. AI Governance and Regulation
Regulation is accelerating. For PE firms and their portfolio companies, the question is not "should we?" It is "can we afford not to?" The SEC is already watching.
25. AI-related regulations in the US grew from 1 in 2016 to 25+ in 2024. Eight years ago there was a single AI regulation on the books. Now there are more than 25. The trend is clear, and it is accelerating. PE firms that wait for regulations to force their hand will be playing catch-up at the worst possible time.
Source: Stanford HAI, 2025
26. Industry produced 51 notable machine learning models in 2024, compared to only 3 from academia. The balance of power in AI research has shifted from universities to corporations. That matters for PE because the models your portfolio companies depend on are controlled by a small number of large tech companies. That is concentration risk worth monitoring.
Source: Stanford HAI, 2025
27. The DOJ instructed prosecutors to seek stiffer sentences for crimes that involve AI. Deputy Attorney General Lisa Monaco announced this in March 2024. The message: using AI to commit or aid illegal activity is an aggravating factor, not a mitigating one. Portfolio companies deploying AI need clear usage policies.
Source: Deputy Attorney General Lisa Monaco, DOJ, March 2024
28. The SEC warned that statements about AI capabilities must not be "materially false or misleading." SEC Director of Enforcement Gurbir Grewal specifically called out "AI washing": companies that overstate their AI capabilities to attract investors or customers. Diligencing AI claims at portfolio companies is not just good practice for PE firms. It is a compliance requirement.
Source: Gurbir Grewal, SEC Director of Enforcement
6. AI Technology Trends
Where the technology is heading matters as much as where it is today. These numbers show the shape of what is coming, and why firms that invest in AI now will have a structural advantage in 18 to 24 months.
29. "I don't think the industry has realized anywhere near 10% of their potential even at present capability." Andrej Karpathy (former Tesla AI Director, OpenAI founding member) said this in December 2025. Not about future AI. About what already exists. The models we have right now can do far more than most organizations are asking them to do.
Source: Andrej Karpathy, December 2025
30. Training GPT-4 cost $100 million. GPT-5 is projected at $1 billion. GPT-6 at $10 billion. Each generation of frontier model costs roughly 10x more to train. That curve will consolidate AI model development into a shrinking number of companies. For PE firms, two implications: the moat around model providers is deepening, and building your own foundation model is permanently out of reach.
Source: Kai-Fu Lee, 2024
31. Notion AI's attach rate jumped from 20% to over 50% in one year, reaching about 50% of company ARR. This is the clearest proof that AI features can transform existing software businesses. For PE firms evaluating SaaS portfolio companies, the question is whether the product lends itself to AI, and whether management can actually ship it.
Source: a16z, Early 2026
32. Horizontal AI applications account for 60% of spending. Vertical (industry-specific) applications account for 40%. The market still tilts toward general-purpose tools. But the vertical segment is growing faster, and it is where the deepest moats are built. For PE firms, vertical AI companies in financial services, healthcare, and legal are the most defensible investment opportunities.
Source: a16z, 2025
Frequently Asked Questions
What percentage of organizations have adopted AI as of 2024?
According to McKinsey's global survey (cited in the Stanford AI Index Report 2025), 72% of organizations reported adopting AI in 2024. Including generative AI specifically, that number rises to 78%, up from 55% in 2023. That is the fastest year-over-year jump in AI adoption McKinsey has ever recorded.
What ROI can PE firms expect from AI implementation?
The BCG/Wharton study by Dell'Acqua and Mollick (2023) found that consultants using AI completed 12.2% more tasks, worked 25.1% faster, and produced 40%+ higher quality output. For PE firms, the key finding: below-average performers saw a 43% quality improvement versus only 17% for top performers. AI lifts average performers the most. That changes how you staff deal teams and portfolio operations.
How much has global corporate investment in AI grown?
Global corporate investment in AI reached $67 billion in 2023, nearly 8x the 2020 level, according to Stanford HAI's 2025 report. Training costs for frontier models have also surged: GPT-4 cost $78 million and Google's Gemini Ultra is estimated at $191 million. Meanwhile, the cost of using AI models dropped 500x in 18 months. Deployment is getting cheaper even as development gets more expensive.
What are the key PE industry benchmarks for fund performance?
According to Phalippou's 2024 research using MSCI data, the private capital industry has 12,306 funds managing $10.5 trillion in total AUM, with a median IRR of 9.1% across all fund types. AUM has grown 15-fold (14% per year) over 25 years. Research by Pintado and Spichiger (2025) found that the Capital Deployment Factor of PE funds rarely exceeds 60%. For the median fund, only 28.2% of capital paid in was actually deployed.
How is AI regulation evolving in the United States?
AI-related regulations in the US grew from just 1 in 2016 to over 25 in 2024, according to Stanford HAI. The DOJ has instructed prosecutors to seek stiffer sentences for AI-related crimes. The SEC has specifically warned about "AI washing," stating that claims about AI capabilities must not be "materially false or misleading." For PE firms and their portfolio companies, AI governance is no longer optional.
Where are the biggest AI opportunities for PE firms right now?
Andrej Karpathy (former Tesla AI Director) said in December 2025 that the industry has not realized "anywhere near 10% of their potential even at present capability." For PE firms, the opportunities cluster around deal screening automation, portfolio monitoring, investor reporting, and knowledge management. With 80% of corporate knowledge sitting in unstructured formats (a16z, 2026) and AI model costs dropping 80% a year, AI has gone from expensive experiment to operational necessity.
- • AI adoption hit 72-78% in 2024. This is no longer early-stage technology.
- • Bottom-half performers see 43% quality gains from AI (vs 17% for top performers). AI is an equalizer.
- • AI model costs dropped 500x in 18 months. The cost barrier is gone. The knowledge barrier remains.
- • Median PE fund IRR is 9.1%, and only 28.2% of called capital gets deployed. Small operational gains compound.
- • AI regulations in the US grew from 1 (2016) to 25+ (2024). The SEC is watching for AI washing.
- • According to Karpathy, less than 10% of AI's current potential has been realized. The opportunity is now.
These numbers shape how we design AI solutions for PE firms, family offices, and private credit teams. See the approach in practice in the High-Stakes AI Blueprint for investment firms.
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Book a Discovery SprintDr. Leigh Coney, Founder of WorkWise Solutions
Dr. Coney holds a PhD in how humans interact with emerging technology. He advises PE firms, family offices, private credit teams, and independent sponsors on AI strategy, due diligence, and portfolio company transformation.