By Luiza Tasinaffo, Data and AI Practice Lead, Orr Search
Private equity has made its position on AI clear. Across the industry, funds are committing to AI transformation inside their portfolio companies, embedding it into value creation plans, and in some cases appointing dedicated AI leadership at the fund level. The ambition is not in question.
The problem is execution. And at the heart of the execution problem is talent.
The gap between commitment and delivery is widening
The data on AI adoption is striking in both directions. McKinsey’s State of AI 2025 survey found that 88% of organisations now use AI regularly in at least one business function. Yet the same report found that nearly two-thirds have not yet begun scaling AI across the enterprise. Most are still running pilots and experiments that never reach production at scale.
The PE market reflects this dynamic closely. FTI Consulting’s 2026 Private Equity AI Radar, drawing on the views of 200 senior PE decision-makers across three continents, found that 95% of funds report AI initiatives meeting or exceeding their original business case criteria. Encouraging, but the same report notes that those business cases were often conservatively scoped, and that the gap between isolated success and enterprise-scale advantage remains wide. Talent was cited as the primary constraint to scaling AI adoption by 35% of respondents. Not technology. Not budget. Talent.
BDO’s 2026 Private Equity Industry Predictions are equally direct: operating partners with deep experience in AI integration are now “essential, not optional enhancements.” The competition for that profile is intensifying, with higher compensation packages and more aggressive recruiting expected to continue throughout 2026.
Why this talent is so hard to find
The shortage is not simply a numbers problem, though supply is genuinely constrained. It is a profile problem. The executives who can lead real AI transformation inside a PE-backed portfolio company are a specific and rare combination.
They need to understand the technology well enough to make credible decisions about it, without being so technical that they lose the ability to lead a commercial organisation. They need to translate AI’s potential into a value creation plan that a GP and a board can hold them accountable to.
They need to move fast, because PE timelines do not allow for multi-year transformation programmes. And they need to bring the organisation with them, because, as Gartner’s research highlights, most AI value remains concentrated at leadership level while the rest of the workforce is underserved with the support and guidance to adopt it effectively.
That combination, technical credibility, commercial sharpness, organisational leadership, and PE-context awareness, does not appear often in one person. And the market has not yet developed a reliable way to assess for it.
The assessment problem is underappreciated
Most hiring processes for AI leadership roles default to the same signals: impressive titles, well-known companies, and a list of AI projects delivered. These are reasonable proxies, but they are not sufficient for a PE context.
The difference between someone who has overseen an AI programme inside a large technology company with abundant resources and dedicated data teams, and someone who can build AI-enabled capability from scratch inside a mid-market portfolio company with a lean team and a two-year runway, is significant. The CV does not always reveal which one you are looking at.
This is where the assessment gap sits. Identifying which candidates have actually driven measurable commercial outcomes from AI, rather than managed a programme that was already resourced and governed by others, requires deep market knowledge and rigorous referencing. It also requires understanding the specific context of the portfolio company: the maturity of its data infrastructure, the strength of the existing technology team, and what the value creation plan actually demands.
What the market needs to catch up
The PE funds building structural advantage in AI are not necessarily the ones spending the most. FTI’s 2026 Radar is clear on this: the differentiator between leaders and laggards is not investment level, it is execution discipline. Superior selection of use cases, stronger governance, and deeper talent integration.
Talent integration starts with finding the right person. That means going beyond pattern-matching to a job description and doing the harder work of understanding what leadership genuinely looks like in an AI-enabled, PE-backed business. It means building networks in adjacent talent pools, including technology operators who have not yet worked in PE but have the commercial instincts and execution track record to succeed in it. And it means being honest with PE clients about what is realistic to find, and what trade-offs exist between different profiles.
The funds that get this right will compound the advantage. Those that do not will continue to accumulate pilots.
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Luiza Tasinaffo leads the Data and AI practice at Orr Search, placing executive talent at the intersection of AI transformation and private equity value creation. If you are building AI leadership capability in your portfolio, get in touch.
Sources
- McKinsey and Company, The State of AI in 2025: Agents, Innovation, and Transformation
- FTI Consulting, 2026 Private Equity AI Radar
- BDO, 2026 Private Equity Industry Predictions
- Gartner, Gartner Predicts by 2027, 50% of Enterprises Without a People-Centric AI Strategy Will Lose Their Top AI Talent, May 2026
