How to Hire a Head of AI at a Private Equity Firm: 2026 Guide
When to make the hire, what to call the seat, which backgrounds work at your fund size, how big the team gets and how to run the search. Built on the Vardis PE AI Leadership Dataset, which tracks 626 verified AI and data leaders across 380 private equity, growth and venture firms, and on what we hear directly from the people in these seats through our surveys, ongoing conversations with the candidate pool and AI leadership searches for PE sponsors. Figures cover the 230 AI leaders at buyout and growth equity sponsors unless noted.
More AI leaders have started at buyout and growth equity sponsors in 2026 so far than in all of 2025. For many of these firms it is a first hire, which means there is no internal playbook for the role, the title or the profile. This guide is built from who your peers actually hired.
- Most PE AI leaders serve the portfolio. 44% are mainly portfolio-facing and another 25% split their time between portfolio companies and the firm. 17% work only on the firm's own investing and operations.
- "Head of AI" is the most common title (30%), followed by CTO, CIO and digital titles (27%) and AI or data operating partner (20%). Chief AI Officer is rare at the fund level (2%).
- The right background shifts with fund size. Sponsors under $2.5B lean toward builders and founders, $2.5-10B sponsors hire operators who have run technology at PE-backed companies, and the largest funds hire more consulting alumni and leaders of large technical teams.
- Most first hires start as a team of one. The median core AI team runs from 1 person under $2.5B of AUM to 10 at $100B+.
- The most common failure is not technical: a vague mandate, no senior sponsor, or one person pulled in too many directions.
Does your PE firm need a head of AI yet?
If AI work is already happening across your portfolio without an owner, usually yes. The firms hiring now are not necessarily the most technical. They are the ones where portfolio CEOs are being asked for AI plans, deal teams are building their own tools, and nobody is accountable for turning that activity into measurable results.
The mid-market is moving fastest. Sponsors with $2.5-10B of AUM made 20 AI leadership hires in 2026 through mid-October, versus 15 in all of 2025. Firms that wait are now recruiting against peers who have already filled the seat.
- Your partners are asking what your AI strategy is, and nobody owns the answer.
- AI has come up as a risk or an opportunity in a recent IC memo or board meeting.
- Portfolio CEOs are being asked for an AI plan in the first 90 days after close, and the plans don't show a return.
- Two or more portfolio companies are running AI pilots with no shared approach, vendor view or way of measuring results.
- Your deal team hasn't seen a best-in-class playbook for how to use AI in sourcing and diligence.
- LPs are asking about AI in diligence questionnaires and you are answering one portfolio company at a time.
The seat is also moving down-market. In October 2026, VSS Capital Partners, a lower middle market sponsor, named Otilia Ciotau, previously a senior advisor to the managing director and CFO of the World Bank Group, as Managing Director, AI and Value Creation.
What does a head of AI at a PE firm actually do?
Most run portfolio value creation. Across 230 AI leaders at buyout and growth sponsors, 44% are mainly portfolio-facing and 25% split their time between portfolio companies and the firm. 17% work only on the firm's own investing and operations, 6% are advisory and 3% sit with deal and diligence teams.
| Primary mandate | Under $2.5B | $2.5-10B | $10-40B | $40-100B | $100B+ |
|---|---|---|---|---|---|
| Portfolio-facing | 17% | 57% | 44% | 43% | 47% |
| Both portfolio and firm | 29% | 25% | 30% | 24% | 19% |
| Firm only (investing and operations) | 20% | 9% | 21% | 18% | 22% |
| Advisory | 20% | 5% | 0% | 2% | 8% |
| Deal and diligence | 9% | 2% | 2% | 2% | 3% |
Each leader counted once by primary mandate. Columns may not total 100% where the mandate is not public.
Smaller sponsors ask one person to do everything. Under $2.5B, only 17% of AI leaders are purely portfolio-facing; the rest split their time, advise or sit with the deal team. From $2.5B up, portfolio work dominates. If this is your first hire, write the split into the role specification so the candidate and the partners agree on it before day one.
At the fund level
- Sourcing: agents that find, enrich and score targets in your subsectors
- Diligence: analyzing a target's raw customer and operating data in days rather than weeks
- Portfolio monitoring and LP reporting
- Firm-wide AI adoption, with usage and cost under control
At portfolio companies
- Revenue: pricing, speed-to-lead and new products built on data the company already has
- Cost: finance, back-office and service workflows
- Decisions: forecasting, inventory and budget pressure-testing
- An AI plan for each new platform in the first 90 days after close
In our conversations with AI leaders at PE firms, a consistent pattern comes up: the ones who earn credibility fastest pick one or two wins in their first six months and report them in hours saved or EBITDA, not in model names. The ones who struggle were handed a broad mandate with no access to data, no access to portfolio CEOs and every decision waiting on a partner's sign-off.
Chief AI Officer, AI operating partner or head of AI: which title should you use?
Use the title that matches the mandate and the economics. "Head of AI" or "Head of Data Science" is the most common fund-level title (30%). "Operating Partner" signals partner-level standing, portfolio work and usually carry. CTO, CIO and digital titles (27%) tend to include infrastructure and security alongside AI. "Chief AI Officer" is rare at the fund level (2%) and far more common inside portfolio companies.
| Title | Share of PE AI leaders | What it usually signals |
|---|---|---|
| Head of AI / Head of Data Science | 30% | Builds and runs the function; hands-on or leading a small technical team |
| CTO / CIO / Chief Digital Officer | 27% | Firm technology, security and data infrastructure, with AI added to the remit |
| AI or Data Operating Partner | 20% | Partner-level, focused on portfolio value creation, typically carry-eligible |
| Advisor / executive in residence / board role | 12% | Part-time or project-based; often how firms test the role before a full-time hire |
| Chief Data / Analytics Officer | 3% | Data platform and reporting first, AI second |
| AI-focused investment partner | 3% | A deal-team role investing in AI companies, not an operating seat |
| Chief AI Officer | 2% | Rare at the fund; more often a portfolio company title |
Our advice: pick one title and be ready to flex up for an exceptional candidate (titles are cheap), rather than various options. It is easier to move a title up than down, and a candidate who is only interested at the higher title is worth a second look.
Two recent examples: Berkshire Partners created a new role, Operating Partner, Head of Data Science and AI, when it appointed Richard Lichtenstein in January 2026, and Great Hill Partners appointed Leland Lockhart as Head of AI in March 2026.
What background should a PE AI leader have?
It depends on fund size more than anything else. Across 230 AI leaders at buyout and growth sponsors, 30% previously worked at a PE or VC firm, 26% are Big Tech alumni (Google, Amazon, Microsoft, IBM, Oracle and similar), 26% have a consulting background, 24% have run technology at a PE-backed company and 31% have founded a company. Roughly four in five were hired from outside the firm and its portfolio. The mix shifts sharply by sponsor size.
Who your peers hire, by fund size
| Share of AI leaders who... | Under $2.5B | $2.5-10B | $10-40B | $40-100B | $100B+ |
|---|---|---|---|---|---|
| Previously ran technology at a PE-backed company | 11% | 34% | 21% | 20% | 31% |
| Previously worked at a PE or VC firm | 34% | 25% | 40% | 25% | 28% |
| Are Big Tech alumni (Google, Amazon, Microsoft, IBM, Oracle and similar) | 17% | 25% | 28% | 29% | 28% |
| Have a consulting background | 34% | 23% | 33% | 24% | 19% |
| Are McKinsey, Bain or BCG alumni | 14% | 6% | 9% | 20% | 19% |
| Have founded a company | 40% | 35% | 33% | 22% | 25% |
| Are hands-on builders (write code or models) | 20% | 22% | 16% | 12% | 8% |
| Hold a PhD | 17% | 6% | 14% | 8% | 11% |
| Hold an MBA | 31% | 31% | 23% | 25% | 22% |
| Have experience in a sector the sponsor invests in | 60% | 62% | 56% | 35% | 67% |
| Median years of experience | 19 | 23 | 22 | 22 | 25 |
| AI leaders tracked | 35 | 65 | 43 | 51 | 36 |
Buyout and growth equity sponsors. Background attributes overlap, so columns do not total 100%.
The operator advantage
At $2.5-10B sponsors, 34% of AI leaders previously ran technology at a PE-backed company, three times the rate at sponsors under $2.5B (11%). Mid-market firms want someone who has already delivered AI inside a portfolio company and can be credible with portfolio CEOs from day one.
Smaller funds hire builders and founders
Under $2.5B, 40% of AI leaders have founded a company and 17% hold a PhD. A first hire with no team has to build things themselves, and these candidates often weigh autonomy and upside alongside cash.
The largest funds hire consulting alumni and team leaders
At $40B and above, about one in five AI leaders are McKinsey, Bain or BCG alumni, and at $100B+ three in four lead technical teams rather than building hands-on. These firms already have teams, so the hire is more often about leading people and influencing partners than building from scratch.
Sector experience helps but is not a requirement: 55% of AI leaders had experience in a sector their sponsor invests in, which means 45% did not.
Where do PE firms find AI leaders? Five talent pools
By employer immediately before the PE seat, 20% of AI leaders came from Big Tech or technology companies, 19% from other operating companies, 16% from consulting, 15% from another PE or VC firm, 12% from PE-backed portfolio companies, 7% from startups and 8% from banks and asset managers. Each pool brings different strengths, risks and costs.
1. Already in a PE seat
What they bring: they know how a fund makes money, speak partner language and ramp fastest. What to screen for: this is the most expensive pool to move, often with unvested carry to buy out, and a playbook built with a large team does not always work on a smaller platform.
- Leland Lockhart, Executive Director of AI and Machine Learning at Vista Equity Partners, appointed Head of AI at Great Hill Partners (March 2026).
- Pavan Arora, Partner, Managing Director and CTO at Cerberus, and earlier CTO and Chief AI Officer at Aramark, joined Gryphon Investors as Operating Partner, Head of AI & Automation (2025).
2. Consulting AI practices
What they bring: structured diagnostics across many companies, PE exposure from diligence work and comfort in front of partners. What to screen for: outcomes they owned rather than recommendations they made, and whether they have run something after the presentation ended.
- Richard Lichtenstein, Senior Expert Partner and Chief Data Officer for Private Equity at Bain & Company, appointed to the newly created role of Operating Partner, Head of Data Science and AI at Berkshire Partners (January 2026).
- Sam Liu, Associate Partner in Bain & Company's AI practice after nearly eight years at Boston Consulting Group, appointed Director of AI at Great Hill Partners (August 2026).
- Sherif Barrad, Partner in Data & AI at EY, joined Charlesbank as Advisory Director, AI (2025).
3. Portfolio company and operating executives
What they bring: they have delivered inside a business like yours, are credible with portfolio CEOs and are often the best value for money. What to screen for: breadth across many portfolio companies rather than depth in one, and the ability to influence without direct authority.
- Nick Olsen, CTO of Anyone Home and a founding technology leader at ResMan, a company Mainsail invested in, appointed Head of AI Innovation at Mainsail Partners (2025).
- Pritesh Patel, Vice President of Products at GoHealth, joined Sheridan Capital Partners as Director of Digital and AI Transformation on its Acceleration Team (July 2026).
- Tom Antony, who led LifeStance Labs at LifeStance Health, joined Shore Capital Partners as Head of AI, Healthcare in the Office of the CEO.
4. Big Tech and AI-native builders
What they bring: experience building AI products and large teams at scale, and the deepest technical bench. What to screen for: leading change in non-tech businesses, and adjusting to smaller budgets and teams. PE wins this pool on carry and portfolio-wide impact, not year-one cash.
- Donal McMahon, VP of Engineering, AI and Data Science at Indeed, where his organization grew to more than 300 people, after seven years at Google, appointed Head of AI & Data Science at Genstar Capital (August 2025).
- Avi Shah, Senior Director of Product Management for Agentforce at Salesforce, now Head of AI Solutions on AEA Investors' AEA Elevate team.
- Dina Dede, who led AI initiatives in the Microsoft-LSEG partnership at Microsoft, joined Ambienta as Director of AI & Data in Portfolio Operations (October 2026).
5. Founders and technical specialists
What they bring: a builder's mindset, comfort with ambiguity and the ability to ship with little support, which makes them a strong fit for a first hire at a smaller fund. What to screen for: partner-level communication and patience with a firm's pace and process.
- Al Rahrooh, CTO across multiple venture-backed companies, appointed Principal and Head of Artificial Intelligence at Pacific Avenue Capital Partners (June 2026).
Each pool prices very differently, mostly because of what candidates already hold. See what the same hire costs from each talent pool.
Which pool fits your firm comes down to three questions: is the seat fund-level or portfolio-facing, what do your portfolio companies look like, and do you need someone full-time? Our AI Leader Profile Assessment walks through it in five questions.
How big is a private equity firm's AI team?
Smaller than most people assume. At sponsors where Vardis has mapped the full team, the median core AI and data team is 1 person under $2.5B of AUM, 2 at $2.5-10B, 4-5 at $10-40B, 8 at $40-100B and 10 at $100B+. Most first hires start alone and lean on contractors or outside builders.
| Sponsor AUM | Median core AI and data team (including the leader) |
|---|---|
| Under $2.5B | 1 |
| $2.5-10B | 2 |
| $10-40B | 4-5 |
| $40-100B | 8 |
| $100B+ | 10 |
Core team excludes IT, cybersecurity, part-time advisors and outsourced developers.
Below the leader, teams are balanced rather than dominated by data scientists: data science (18%), ML and AI engineering (17%), analytics and BI (17%), data and AI generalists (16%), data engineering (12%), product and program management (12%) and software engineering (9%). 58% work from the firm's headquarters, 22% from another firm office and 10% remotely. The most common prior employers for these team members are Deloitte, BCG, PwC, Accenture and McKinsey.
How should a PE firm run a head of AI search?
Agree on the outcomes before the profile. The searches that go well start with two or three measurable results the partners care about, a named senior sponsor and a decision on who pays for the seat. The talent pool, title and compensation follow from those choices.
- Agree the mandate: fund-level, portfolio-facing or both, with two or three outcomes and how they will be measured.
- Name a senior sponsor who will clear the path to data, portfolio CEOs and budget.
- Decide who pays: the fund, the portfolio companies or both. How sponsors structure it.
- Choose the talent pool and title that fit the mandate, using the data above.
- Bring two or three portfolio CEOs into the interviews if the role is portfolio-facing. Their buy-in is what turns the hire into results.
- Assess every candidate against the same scorecard, not against how fluent they sound about AI.
- Time the offer around bonus cycles. The best candidates are passive and almost always leave something behind: unvested carry, an incentive cycle or equity mid-hold.
What a strong scorecard measures
- Owned results: outcomes they delivered in EBITDA, revenue or hours saved, not projects they advised on.
- PE fluency: how a fund makes money, hold periods, and what partners and portfolio CEOs need from them.
- Technical depth matched to the mandate: a hands-on builder for a first hire, a leader of builders for a scaling team.
- Change leadership: driving adoption with people who did not ask for AI.
- Data and governance judgment: data foundations, security, compliance and vendor choices.
- Communication with non-technical partners: turning technical work into investment committee language.
Five mistakes we see
- A big mandate with no data access, no portfolio CEO access and every decision waiting on partner sign-off.
- Hiring a builder for an influence job, or a strategist for a build job.
- Treating the first hire as the whole team.
- Skipping the data and compliance groundwork, then finding the problems a year in.
- Paying for AI vocabulary instead of owned outcomes.
Frequently asked questions
- Does my private equity firm need a head of AI?
- If AI work is already happening across your portfolio without an owner, usually yes. Signs a firm is ready include portfolio CEOs being asked for AI plans after close, several portfolio companies running separate AI pilots, and deal teams building their own tools. Firms without a senior sponsor start with a fractional or project-based AI leader instead.
- What does a head of AI at a private equity firm do?
- Most work on portfolio value creation. Across 230 AI leaders at buyout and growth equity sponsors tracked by Vardis, 44% are mainly portfolio-facing and 25% split their time between portfolio companies and the firm, while 17% focus only on the firm's own investing and operations, such as sourcing, diligence and LP reporting.
- What is the difference between an AI operating partner and a head of AI?
- An AI operating partner is usually a partner-level, carry-eligible role focused on value creation across portfolio companies. A head of AI or head of data science typically builds and runs the firm's AI capability and team. In Vardis data, head of AI or data science is the most common fund-level title (30%), followed by CTO, CIO or digital titles (27%) and AI or data operating partner (20%).
- What background should a private equity head of AI have?
- It depends on fund size. At sponsors with $2.5-10B of AUM, 34% of AI leaders previously ran technology at a PE-backed company. Under $2.5B, 40% have founded a company and 17% hold a PhD. At $40B and above, about one in five are McKinsey, Bain or BCG alumni.
- Where do PE firms find AI leaders?
- From five talent pools. By employer immediately before the PE seat, 20% of AI leaders came from Big Tech or technology companies, 19% from other operating companies, 16% from consulting, 15% from another PE or VC firm, 12% from PE-backed portfolio companies and 7% from startups. The pools differ in cost, ramp time and fit.
- How big is a private equity firm's AI team?
- Usually small. The median core AI and data team is 1 person at sponsors under $2.5B of AUM, 2 at $2.5-10B, 4-5 at $10-40B, 8 at $40-100B and 10 at $100B+. Most first AI hires start as a team of one and rely on contractors or outside builders.
- Should a PE firm hire a fractional AI leader first?
- It can make sense when the mandate is narrow, portfolio data is not ready, or no partner is ready to sponsor a full-time seat. A common pattern is a fractional engagement that proves results at one or two portfolio companies, then converts into a full-time fund-level hire.
- How much does a head of AI at a PE firm cost?
- Total cash ranges from roughly $275K-$475K at funds under $1B of AUM to $800K-$1.75M at mega-funds, with carried interest included in most senior packages. Full benchmarks by fund size and talent pool are in the Vardis PE AI Leader Compensation report. See the full compensation benchmarks.
- Which private equity firms have hired heads of AI?
- Recent fund-level AI leadership hires include Genstar Capital, Berkshire Partners, Great Hill Partners, Gryphon Investors, Mainsail Partners, AEA Investors and Charlesbank. The Vardis PE AI Adoption Map lists who has made the hire, by fund size. See the PE AI Adoption Map.
Hiring your first AI leader?
Vardis runs AI leadership searches for PE sponsors and their portfolio companies, and tracks the 626 AI and data leaders behind this guide. Talk to Josh King about your mandate, how your peers have structured the seat and who is available in your market.
Josh King leads Vardis's technology practice and specializes in placing AI, data and technology leaders. He was selected over 50 other search firms to lead the Chief Data Officer search for The White House.
Talk to Josh KingEvaluating a PE seat yourself?
See what PE firms pay AI leaders by fund size and talent pool, how carry is structured and what to negotiate.
Source: Vardis Partners PE AI Leadership Dataset (626 verified AI and data leaders across 380 PE, growth and venture firms; this guide uses the 230 leaders at buyout and growth equity sponsors), updated October 2026. The dataset combines Vardis surveys and conversations with AI leaders at PE firms, our search work and verified career records. Named appointments are drawn from firm announcements and published firm biographies.