Who leads AI at private equity firms?

The AI lead is the newest seat in private equity: In our research, of leads with a known start date, seven in ten took the job in 2025 or 2026.Note 11Note 1: 1 One caution. A hire announced with a press release is easier to date than one made quietly years ago, so the dated half leans recent and the true share of new hires is probably a little below seven in ten. Even if every undated lead started before 2025, more than a third of all leads would still be in their first two years (47 of 131). We also see only who holds each seat today, so a role that changed hands recently looks new. Turnover would affect AI-titled and data or technology-titled seats about equally. The AI-titled seats skew far more recent, which suggests the AI seat itself is new.. In our first post,Note 22Note 2: 2 Midici, “The AI gap in private equity is a size gap,” September 29, 2026. https://midici.com/blog/the-ai-gap-in-private-equity-is-a-size-gap/ we counted which firms have an AI lead. The answer depended on size: four in ten firms managing more than $10 billion have one, and in the lower mid-market it’s one in twenty. That post ended on a question: how does a smaller firm get the capability without the headcount? To answer that, we first needed to know what the headcount looks like.

So this time we looked at the people:Note 33Note 3: 3 Methods are described in How we did this, below. what they’re called, where they came from and what they work on.

A brand-new job

We found a public start date for about half of the AI leads. Of those, 53% started in 2026 and another 16% in 2025. Only about one in five has been in the seat since before 2024.

What’s new is AI itself. Among leads with AI in their title, 81% started in 2025 or 2026, and 58% started this year alone. The year isn’t over: at its pace through September, 2026 would end with more than three times as many new AI-titled leads as 2025. The rest are heads of data science, digital and technology who now carry the AI remit. Their roles show what an established seat looks like in the same data: 60% started before 2025, against 19% of the AI-titled leads.Note 44Note 4: 4 We grouped leads by their current job title. 90 have AI, artificial intelligence or agentic in it, or hold an AI role under a generic title; 41 have data, data science, digital or technology titles instead. Eight leads with generic titles such as Managing Director were grouped by the role described in their profile. Only 20 of the technology group have a dated start, so its share is approximate. Many of those jobs existed before generative AI and have since absorbed it.

The surge shows up at every size of firm. Of all dated leads, 64% at mega-funds started in the last two years, 80% at large firms, 67% at mid-sized firms and more than half in the lower mid-market.

Private equity is part of a wider wave. IBM’s 2026 CEO study, which surveyed 2,000 CEOs, found 76% of organizations have a chief AI officer, up from 26% in 2025.Note 55Note 5: 5 IBM Institute for Business Value, 2026 CEO Study (2,000 CEOs in 33 geographies, surveyed February–April 2026 with Oxford Economics). Press release, “IBM Study: CEOs are Reshaping C-suite Roles for the AI Era,” May 4, 2026: https://newsroom.ibm.com/2026-05-04-ibm-study-ceos-are-reshaping-c-suite-roles-for-the-ai-era. Study: https://www.ibm.com/thought-leadership/institute-business-value/en-us/c-suite-study/ceo Recruiters say private equity has moved early. In early 2025, Korn Ferry’s global head of data and AI said PE was taking the lead on AI recruitment by creating AI operating executive roles that assess portfolio companies for where AI can improve processes.Note 66Note 6: 6 Alex McMurray, “Point72 data head joins the new hot space for AI jobs: Private equity,” eFinancialCareers, January 6, 2025. https://www.efinancialcareers.com/news/point72-data-head-joins-the-new-hot-space-for-ai-jobs-private-equity

So, most people in this job are in their first year or two of doing it inside a private equity firm. Holds typically run several years, which means few have yet seen an AI program through one at their current firm.

Who gets hired

The typical PE AI lead is a technical manager. Of the leads whose depth we could judge from public sources, 57% manage technical teams, a third are business or strategy people, and only one in ten is a hands-on ML engineer.

They arrive from everywhere. Counting every earlier employer type in each career:

The most common single path, a quarter of the careers we could classify, is the data science practitioner who moved into management. CIO/CTO-type operators and ex-consultants follow at about one in eight each. Fewer than one in ten are best described as big-tech hires, and about one in fifteen as founders.

They are well credentialed. Of the leads whose education we could find, 13% hold PhDs and 58% a master’s or MBA. Where the subject is stated, four in five studied a technical field such as computer science, engineering, math or statistics.

Background shapes the program. In the largest survey of PE investors to date, Gompers, Kaplan and Mukharlyamov found that firms use different mixes of financial, governance and operational engineering, and that these strategies are strongly influenced by the career histories of the firm’s founders.Note 77Note 7: 7 Paul Gompers, Steven N. Kaplan and Vladimir Mukharlyamov, “What Do Private Equity Firms Say They Do?” Journal of Financial Economics 121(3), 2016, 449–476. doi:10.1016/j.jfineco.2016.06.003. Working paper: https://www.hbs.edu/ris/Publication%20Files/15-081_9baffe73-8ec2-404f-9d62-ee0d825ca5b5.pdf The same likely holds for AI programs. The profile here, technical managers who have worked inside operating companies, fits a job that is mostly about changing how a business works. CEOs see it the same way: 83% of those surveyed by IBM say AI success depends more on people’s adoption than on technology.Note 5See note 5

Finally, they are mostly quiet. 62% have no public talks, podcasts or bylined articles at all. Whatever they are learning stays inside their firms.

The job shrinks with the firm

“Head of AI” covers everything from a managing director with a team to a single analyst. Search firms see the same spread. Egon Zehnder writes that many firms are discovering that AI leadership is not a single, uniform role.Note 88Note 8: 8 Egon Zehnder, “The Great AI Divide in Private Equity: Why Firms Need Two AI Leadership Roles,” August 2026. https://www.egonzehnder.com/industries/private-capital/insight/the-great-ai-divide-in-private-equity Seniority tracks firm size closely:

Above $2 billion, 39% of AI leads are partners or managing directors. Below $2 billion, it’s 28%. The share who are VPs or below moves the other way, from 25% to 38%. The rest are principals or directors, about a fifth at both sizes, plus Chief AI Officer-type titles whose rank isn’t stated and a few we couldn’t place. At a mega-fund, the AI lead is most often a managing director (46%). In the lower mid-market, 44% are VPs, managers or individual contributors.

The remit narrows too. Above $2 billion, 77% of leads work on the portfolio. Below, 55% do. Part of that gap is bios that don’t say what the person works on. Counting only leads whose remit is stated, it’s 88% against 74%.Note 99Note 9: 9 Remit is unstated for 10 of the 79 leads at firms above $2 billion and 12 of the 47 at firms between $100 million and $2 billion. Of those whose remit is stated, 61 of 69 and 26 of 35 work on the portfolio. Either way, smaller firms point less of their AI lead at the portfolio, which is where the value shows up when the firm sells.

Some of that pull toward the firm’s own work is rational. In the Bain and StepStone 2026 GP survey, GPs reported the highest returns from generative AI in deal sourcing and due diligence. Within portfolio companies, benefits skewed toward cost savings, and 39% of GPs did not expect material financial impact from AI in 2026.Note 1010Note 10: 10 Bain & Company and StepStone Group, “Private Equity’s Reality Check: The GP Outlook for 2026,” March 2, 2026. https://www.bain.com/insights/private-equitys-reality-check-gp-outlook-2026/. Press release: https://www.stepstonegroup.com/news-insights/bain-company-and-stepstone-group-release-2026-private-equity-gp-outlook/ The early returns sit with the firm. The value at exit sits in the companies, and buyers are starting to check for it, as we’ll see below.

Across all firms, 36% of leads work only on portfolio companies, 32% split their time between the portfolio and the firm’s own investing, and 14% work only inside the firm. Fewer than one in five bios don’t say. Most firms have one person: 69% list a single AI lead, 23% list two and 8% list three or more.

All of this describes firms that have an AI lead, and most don’t. Of the firms above $2 billion we researched, 25% have a dedicated AI lead: one in four. Between $100 million and $2 billion, it’s 6%, about one in sixteen. By size, that’s 39% of mega-funds, 19% of large firms, 8% of mid-sized firms and 4% of the lower mid-market.Note 1111Note 11: 11 Firm counts are from our sponsor dataset as of October 6, 2026: 95 mega-funds (37 with an AI lead), 222 large firms (43), 389 mid-sized firms (31) and 382 lower mid-market firms (16). They differ from our first post’s because a re-check that day downgraded 11 firms and added newly researched sponsors. One lead, at S2G, was found that day and has not been profiled yet, so the seniority and remit figures cover 79 leads above $2 billion, not 80.

Put all three together, having a lead, its seniority and its remit, and the drop-off is steep:

Picture two firms: one managing $1 billion in private equity, the other $5 billion.

At the $1 billion firm, there’s about an 8% chance anyone’s job is AI.Note 11See note 11 If someone’s is, fewer than one in three is a partner or managing director, and it’s a coin flip whether they work on the portfolio at all.

At the $5 billion firm, there’s about a 19% chance. One in three of those leads is a partner or MD, and three in four work on the portfolio.

Across the whole market, about one in thirteen firms above $2 billion has a partner-level AI lead working on its portfolio. Between $100 million and $2 billion it’s about one in 110.Note 1212Note 12: 12 Above $2 billion, 25 of the 317 firms we researched have an AI lead who is a partner or managing director and works on the portfolio: 317 ÷ 25 ≈ 13. Between $100 million and $2 billion, 7 of 771 do: 771 ÷ 7 ≈ 110. Firms under $100 million are left out.

In their own words

Most AI leads say nothing in public, so the few who do are setting the agenda. Nearly all of them work at the largest firms, where the AI lead is often a managing director with a team, and their statements are starting to sketch a playbook.

What these leads share in public is principle. The specifics stay inside the firm: which plays worked, in which businesses, and what they returned. The playbook is being written at the top of the market, and the part that matters most is staying there.

Why the gap compounds

A firm that learns which AI plays work takes that playbook into its next deal. Private equity has long built its edge this way, carrying practices from one company to the next. In a survey of more than 15,000 firms, Bloom, Sadun and Van Reenen found PE-owned firms are better managed than government, family and privately owned firms, with their strongest edge in monitoring practices such as lean manufacturing and continuous improvement.Note 1313Note 13: 13 Nicholas Bloom, Raffaella Sadun and John Van Reenen, “Do Private Equity Owned Firms Have Better Management Practices?” American Economic Review: Papers & Proceedings 105(5), 2015, 442–446. doi:10.1257/aer.p20151000. Earlier version: CEP Occasional Paper 24, 2009. With AI, the firms carrying lessons forward today are the ones with a dedicated lead, and as the figures above show, those are mostly the largest firms.

AI is now part of that toolkit, and the stakes are rising on both ends of a deal. On the way in, Bain argues “12 is the new 5”: today’s deals demand faster EBITDA growth, and the winning firms will invest in talent and AI.Note 1414Note 14: 14 Hugh MacArthur, introduction to Bain & Company, Global Private Equity Report 2026, 2026. https://www.bain.com/insights/topics/global-private-equity-report/ On the way out, EY’s 2026 exit readiness study finds AI strategy emerging as a key exit differentiator, with buyers assessing AI adoption, disruption exposure and its role in future value creation.Note 1515Note 15: 15 EY, Global Private Equity Exit Readiness Study 2026, June 2, 2026. https://www.ey.com/en_gl/insights/private-equity/private-equity-exit-readiness-study EY’s Europe West private equity leader, Cord Stümke, says buyers want embedded AI use cases and a proven record of how they translate into EBITDA.Note 1616Note 16: 16 “Why private equity exit readiness can’t wait,” SuperReturn / Informa Connect, September 25, 2026, quoting Cord Stümke, EY Europe West Private Equity Leader. https://informaconnect.com/superreturneurope/article/why-private-equity-exit-readiness-cant-wait/ Grant Thornton reports the same pressure showing up at the deal table.Note 1717Note 17: 17 Chen Liu, “Buyers are asking questions about AI, and it’s impacting valuations,” Grant Thornton, July 24, 2026. https://grantthornton.com/insights/articles/pe/2026/buyers-are-asking-questions-about-ai

A firm without the playbook bids for the same companies with a thinner value-creation plan, and sells to buyers who will ask what it did about AI.

What this means

A lower mid-market firm can afford an AI hire, but not the one who has already done the work. The hire will probably be a VP, probably new to PE and probably working alone. That one person has to cover deal work, back office and a dozen portfolio companies at once.

A smaller firm can’t close the gap by copying the largest firms. It can’t hire a managing director with a team, and one VP spread across a dozen companies won’t get far.

There’s still time. Seven in ten AI leads have been in the job less than two years, and nearly 40% of GPs don’t expect AI to move portfolio financials this year. The portfolio playbook is still mostly unwritten, and the largest firms’ head start is a year or two at most.

What a smaller firm needs is the experience and the playbook it produces, applied inside each company for the weeks it takes to land a win. That experience can come from outside the payroll. An in-house hire at a smaller firm learns only from that firm’s own companies. Hiring someone who has already done this work is a $350,000–450,000 annual commitment before anyone has looked at a single company.Note 2See note 2 An assessment is a small fraction of that, applied to one company, and credited against the work that follows.

That’s what Midici does. We assess each portfolio company to find where AI will move EBITDA, then put engineers inside the business to build it alongside the people who do the work. Because we do this across many companies and many sponsors, each engagement adds to the playbook the next one starts from: which plays work in pricing, field operations or claims, and what they return. The largest firms build that playbook in-house. We carry it across the lower mid-market, at a cost sized for it.

How we did this

We started from the PE sponsors in our first post’s dataset, drawn from SEC Form ADV filings,Note 1818Note 18: 18 U.S. Securities and Exchange Commission, Form ADV filings, Part 1 and Schedule D (private fund reporting). whose public materials show a dedicated AI lead. For each, a research agent identified the person or people whose primary job is AI, data science or data at the firm. It then built a profile from public professional sources: firm bios, press releases, conference and podcast pages, published articles and public LinkedIn pages. A second, independent agent reviewed every record against its sources and changed at least one field in 187 of the 190 profiles. It removed details it couldn’t tie to a source, downgraded overstated titles and rejected people who weren’t really AI leads. Our first post’s data listed 142 firms with a dedicated AI lead. The research found no current lead at four of them, and a re-check on October 6 downgraded seven more, leaving 131 firms with a verified AI lead.

We recorded only professional facts. We collected no ages, family details, locations beyond work city, gender or ethnicity.

Figures on seniority, background and remit count the most senior AI person at each firm. Several fields are unknown for a quarter or more of leads. Unless stated otherwise, percentages are of those with a known value.