AI in Private Equity: 50 Key Statistics for 2026
Dr. Leigh Coney
Founder, WorkWise Solutions
March 16, 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 aims to give you both.
I compiled these statistics because my conversations with PE partners, family office principals, and private credit teams kept circling the same questions. How fast is adoption actually moving? What does the ROI data say? How big is the AI investment wave, and does it matter for my portfolio?
Every stat below is sourced. Every source is named. If a number came from a press release or vendor marketing, it did not make the list. These are from peer-reviewed research, institutional surveys, and named experts with track records worth citing.
1. AI Adoption Rates
The adoption numbers tell a clear story. AI is no longer an experiment for early movers. It is a baseline expectation, and the 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 LLM-based software built around them, that number rises to 47-56%. The gap between those two numbers is the entire business 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, this means the adoption opportunity at US-based portfolio companies may actually be larger than in certain Asian and Middle Eastern markets.
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 program. A baseline expectation. When a public company CEO makes AI usage a condition of employment, the adoption curve has moved from "early adopter" to "table stakes."
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. This is the data that AI can now process but that traditional software could not. For PE firms, this means the biggest gains are not in structured financial data (which your existing tools already handle). The biggest gains are in the 80% of information that currently requires a human to read, interpret, and act on.
Source: a16z, 2026
2. AI ROI and Productivity
ROI claims are everywhere. Most are vendor-funded and self-serving. The numbers below come from academic researchers and institutions with no product to sell.
7. BCG consultants with AI access completed 12.2% more tasks, worked 22.5-27.6% 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 entire 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 (which is most firms), this changes the economics of team building entirely.
Source: Dell'Acqua, Mollick et al., 2023
9. Customer support agents saw a 14% productivity increase with AI assistance. This was measured across thousands of agents at a Fortune 500 company. The finding that matters: the improvement came primarily 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 experienced a 34% productivity improvement with AI. Experienced workers saw minimal impact. This aligns with the BCG findings. AI is an equalizer, not an amplifier. If your portfolio company's competitive advantage depends on having the most experienced team, AI does not widen that moat. But if your portfolio company competes on speed and volume, AI changes the math on who you need to hire.
Source: Brynjolfsson et al., 2023
11. AI Transformation programs should show concrete results within 6-12 months. Andrew Ng has said this repeatedly and it remains the best benchmark for PE firms evaluating AI initiatives at portfolio companies. If a program cannot show measurable results within a year, the problem is not that AI is slow. The problem is that the program is poorly scoped.
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 that Andrew Ng frequently cites. Even if the actual figure lands at half that projection, we are talking about 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 reveal the scale of the AI investment wave and, just as importantly, 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 matter because they explain why only a handful of companies can build frontier models. But using those models is a completely different cost equation, and that cost is falling fast.
Source: Stanford HAI, 2025
15. AI model costs dropped by 500x in 18 months while capabilities improved. Kai-Fu Lee highlighted this as one of the most underappreciated dynamics in AI. The models get better while getting cheaper. In most industries, quality and cost move in the same direction. In AI, they are moving 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. This is a single vertical within AI tooling. It gives you a sense of 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 just 6 months. For context, it took Slack 5 years and Zoom 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 provide the context for why AI matters. A $10.5 trillion industry with median single-digit IRRs has obvious room for operational improvement. The capital deployment data is particularly striking.
19. Private capital fund AUM grew 15-fold (14% per annum) over 25 years. The industry has grown enormously, which means more deals to screen, more portfolio companies to monitor, and more investor reports to produce. The manual processes that worked when your fund was $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. The sheer number of funds means competition for deals is 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 return levels, even small improvements in operational efficiency, whether through faster deal screening or better portfolio monitoring, can meaningfully move the needle.
Source: Phalippou, 2024
22. Carlyle Group manages nearly half a trillion dollars in client assets. David Rubenstein mentioned this in the context of how the industry has evolved from small, relationship-driven shops to institutional-scale asset managers. That evolution requires 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. This means a significant portion of committed capital sits idle. AI-powered deal screening and sourcing can help firms identify and evaluate opportunities faster, potentially improving deployment rates 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 was used for fees, expenses, and recycling rather than direct investment. This number puts enormous pressure on GPs to maximize returns on the capital that is deployed, making 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 governance question is not "should we?" It is "can we afford not to?" The SEC is already watching.
25. The number of 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 trajectory is clear, and it is accelerating. PE firms that wait to build governance structures until regulations force them to 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 decisively from universities to corporations. This matters for PE because it means the models your portfolio companies depend on are controlled by a small number of large technology companies, creating 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 made this announcement in March 2024. The message is clear: using AI to commit or facilitate illegal activity will be treated as 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. For PE firms, this means diligencing AI claims at portfolio companies is not just good practice. 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 data points reveal the shape of what is coming, and why firms that invest in AI infrastructure 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 capabilities. About current ones. The models that exist 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. This exponential cost curve will consolidate AI model development among a shrinking number of companies. For PE firms, this has two implications: the moat around model providers is deepening, and the cost of building your own foundation model is permanently out of reach.
Source: Kai-Fu Lee, 2024
31. Notion AI's attach rate surged from 20% to over 50% in one year, reaching approximately 50% of company ARR. This is the clearest proof point that AI features can transform existing software businesses. For PE firms evaluating SaaS portfolio companies, the question is whether the company's product lends itself to AI augmentation, and whether management has the capability to execute on it.
Source: a16z, Early 2026
32. Horizontal AI applications account for 60% of spending, while vertical (industry-specific) applications account for 40%. The market is still tilted toward general-purpose AI 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 represent the most defensible investment opportunities.
Source: a16z, 2025
"The numbers here are not predictions. They are measurements. Seventy-two percent adoption. Forty-three percent quality improvement for average performers. A 500x cost reduction in 18 months. PE firms that are still debating whether AI is 'ready' for their workflow are debating a question the data answered two years ago. The question now is which AI, built how, deployed where."
Dr. Leigh Coney, Founder of WorkWise Solutions
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. When including generative AI specifically, that number rises to 78%, up from 55% in 2023. This represents the fastest year-over-year jump in AI adoption since McKinsey began tracking the metric.
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 22.5-27.6% faster, and produced 40%+ higher quality output. Critically for PE firms, the bottom-half performers saw a 43% quality improvement versus only 17% for top performers. This means AI has the greatest impact on leveling up junior team members, which changes the economics of 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, with GPT-4 costing $78 million and Google's Gemini Ultra estimated at $191 million. At the same time, the cost of using AI models has dropped by 500x in 18 months, meaning 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 annum) over 25 years. Notably, research by Pintado and Spichiger (2025) found that the Capital Deployment Factor of PE funds rarely exceeds 60%, and for the median fund, only 28.2% of capital paid in was actually deployed.
How is AI regulation evolving in the United States?
The number of 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-utilized crimes, and 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, this regulatory acceleration means AI governance is no longer optional.
Where are the biggest AI opportunities for PE firms right now?
Andrej Karpathy (former Tesla AI Director) stated in December 2025 that the industry has not realized "anywhere near 10% of their potential even at present capability." For PE firms specifically, the opportunities cluster around deal screening automation, portfolio company monitoring, investor reporting, and knowledge management. With 80% of corporate knowledge living in unstructured formats (a16z, 2026) and AI model costs dropping 80% year-over-year, the economics of AI implementation have shifted 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 statistics inform how we design AI solutions for PE firms, family offices, and private credit teams. See how our data-driven approach works 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.