Two numbers from this year’s reporting season should be read together. The first is encouraging: 95% of private equity funds now say their AI initiatives are meeting or exceeding the business case they were underwritten against, with revenue acceleration cited as the top priority. The second is the one that should hold a sponsor’s attention. Only 17% of funds say AI is significantly exceeding that business case. Almost everyone is getting something. Very few are getting the outsized result. (FTI Consulting, 2026 Private Equity AI Radar.)
That gap between “working” and “winning” is the most important talent question in private capital right now, and it is being misdiagnosed. When sponsors look at the firms pulling ahead and ask what they are missing, the reflex answer has become a job title. Across the mid-market, the instinct is to appoint a Chief AI Officer and consider the capability gap closed. It is the wrong instinct, and the data explains why.
The constraint was never the technology
Ask the funds themselves what is holding AI back and they do not point at the tooling. The single most cited barrier to scaling adoption is talent, named by 35% of respondents, and 68% now rank hiring as their top priority. As the FTI analysis puts it plainly, the main barrier to scaling AI is not technological. It is human. Scaling AI is an organisational challenge wearing a technical costume.
This matters for how sponsors hire, because an organisational problem and a technical problem call for completely different solutions. A technical problem is solved by a specialist you can drop into an org chart. An organisational problem is solved by leadership that can move an entire operating model. Appointing a Chief AI Officer answers the technical question. It does very little for the organisational one, and the organisational one is the one actually costing returns.
A Chief AI Officer only pays off at a maturity most portfolio companies have not reached
The case for a dedicated AI chief is real, but it is narrow. The most AI-mature organisations, those running AI across the enterprise rather than in pilots, are nearly three times more likely to have a Chief AI Officer as their primary AI decision-maker: roughly 29% of them, against 12% everywhere else. The catch is that this most-mature tier represents only around 13% of companies. For the other 87%, a full-time AI chief is a solution imported from a stage of maturity they have not reached.
The economics make the mismatch sharper. A full-time Chief AI Officer now carries an all-in cost north of $400,000, and the strongest candidates are being fought over by every sector at once. For a typical mid-market portfolio company still moving AI from pilot to production, that is a large, single-point bet on a function that has not yet earned a permanent seat. It can also do quiet harm: appointing one executive as the owner of AI gives the rest of the leadership team permission to treat it as someone else’s remit. The CEO stops feeling accountable for an AI-led P&L. The commercial and operations leaders wait to be told. The very diffusion of fluency that separates the 17% from everyone else never happens, because the org has been told, structurally, that AI lives in one office.
What the winning portfolio companies are actually hiring for
The firms turning AI into outsized performance are not the ones with the most impressive AI title on the masthead. They are the ones whose existing leadership team is AI-fluent end to end. The CEO can set an AI agenda and hold the executive team to it. The CFO can underwrite an AI business case and tell a real return from a science project. The commercial leader knows where AI moves pipeline and where it does not. AI fluency is treated as a leadership competency distributed across the team, not a department bolted onto the side of it.
For sponsors, that reframes the hiring brief at exactly the points where leadership is already in play. At the post-close leadership build, the question is no longer only whether a CEO has run a business of this size in this sector. It is whether they can lead a transformation, because every value-creation plan written in 2026 is, in part, an AI plan. At the mid-hold review, the honest test is whether the incumbent team can carry an AI-enabled operating model or whether one or two targeted additions are needed. And where a specialist genuinely is required before the business can justify a permanent chief, a fractional or interim AI leader, typically a fraction of the cost of a full-time hire, bridges the gap without locking in a role the company has not grown into.
The practical implication for sponsors
The performance spread that FTI identifies, the distance between the 95% who are getting a result and the 17% who are getting a decisive one, will not be closed by tooling that every fund can now buy. It will be closed by leadership teams that can absorb that tooling and turn it into margin and growth. That is a talent problem, and it is solved before the technology arrives, not after.
The sharpest sponsors have stopped asking “who is our Chief AI Officer?” and started asking a harder question: is this leadership team, as currently constituted, capable of leading an AI-led value-creation plan over the hold we are underwriting? Sometimes the answer is yes, and the work is to back and stretch them. Sometimes it is a targeted upgrade. Either way, the answer is found in the calibre and fluency of the leaders already running the business, not in a title added to the org chart in the hope it will compensate for the ones who cannot.
HMN Capital — Specialist Executive Search & Interim Management for Private Capital.
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