This article was originally published on LinkedIn.

Over the past several months, the AI consulting and advisory work that I've been doing has increasingly brought me into contact with PE-backed companies and private equity firms directly. In many ways, it's been my focus on AI implementation for companies running on NetSuite that "unlocked" these opportunities, giving me a seat at the table with a type of firm that I probably wouldn't have had access to otherwise.

Here's what I'm seeing.

Private equity firms are under real pressure to show progress with regard to AI. Sponsors want it. Limited Partners (LPs), the investors who fund these firms, are asking about it. And the portfolio companies, each at a different stage of maturity, and each with different data infrastructure and risk profiles, are waiting for direction.

So the search for a "Head of AI" begins. Someone to lead AI transformation across the entire portfolio.

And I think that's where most PE firms are getting stuck. Because the candidate profile that they're describing doesn't match the job that they really need to have done.

The typical search seems to gravitate toward one of two profiles: a technologist with some operating experience, or an operator with enough technical fluency to seem credible in the room. Both profiles solve part of the problem, but neither solves all of it. And at the portfolio level, those gaps don't stay small for long. Instead, they multiply across a dozen companies simultaneously.

What Gets Missed Without the Technical Voice

AI transformation at the portfolio level isn't just a financial governance problem. It's also a technical one, and the consequences of getting it wrong often aren't visible until they're expensive.

  • Security and risk. AI introduces new attack surfaces, data privacy exposure, and compliance obligations across every portfolio company. Someone has to own that, and the CFO isn't trained for it.
  • Data infrastructure. Every AI initiative runs on top of data architecture. If that foundation is weak, the programs built on it are probably going to fail. This is something that the CFO won't see coming.
  • Build versus buy. Evaluating models, platforms, and vendors requires technical fluency that no amount of financial acumen substitutes for.
  • Feasibility filtering. Without a technical voice at the table, AI roadmaps fill up with ideas that look great in a board deck, but collapse in production.

The hardest part of AI isn't the model. It's connecting the model to outcomes that the business really tracks. But you need both sides of that equation covered, not just one.

What Gets Missed Without the Financial Voice

The opposite problem is just as common, and just as costly. Technical leaders build programs that work beautifully, but can't be justified financially.

  • No connection to business outcomes. AI initiatives without financial discipline tend to optimize for capability, but not impact. The technology works, but the ROI is unclear. So what you end up with is a proof of concept that never moves forward.
  • Undisciplined capital allocation. Without someone who understands the financial levers, AI investment decisions get made based on enthusiasm rather than expected return. Budgets expand before value is proven, and eventually the math catches up.
  • Credibility gaps at the sponsor level. PE sponsors and LPs don't evaluate AI programs on technical merit. They evaluate them on margin impact, operational efficiency, and what they mean for exit valuation. A technical leader who can't speak that language will lose support, and of course, the budget.
  • Scaling before the value is there. Without financial rigor, AI programs expand before they've earned the right to. Headcount grows, tooling proliferates, and by the time someone asks what the return has been, the costs are already embedded, and the wins are difficult to isolate.

AI programs that can't be measured can't be defended. And programs that can't be defended won't survive the next budget cycle.

Three Options to Consider

In the engagements I've been part of, the PE firms that get AI right choose one of these options.

Option 1: A cross-functional committee with real authority. The CFO and CIO/CTO co-own the AI transformation mandate. Financial framework on one side, technical governance on the other. Neither reports to the other on this. Both report to the same sponsor. This works when the right people are already in the portfolio and the firm is willing to give them joint accountability, not just a seat at the table.

Option 2: A hybrid leader who can hold both domains. Not a CFO who took an AI course. Not a CTO who learned to read a P&L. Someone whose career has genuinely spanned both financial operations and technical implementation, and who has the credibility to lead in either direction. These people really do exist.

Option 3: A fractional Head of AI. I think this one isn't getting enough attention. And for PE firms specifically, it may be the most practical path of the three.

The PE model is already built for fractional and interim leadership. Interim CFOs, operating partners, functional advisors. These are standard tools. Applying that same model to AI leadership is consistent with how the best firms already deploy specialized expertise across a portfolio.

What makes it work is the profile. Not a consultant who presents a framework and leaves. Instead, a practitioner who has actually implemented AI in operational environments. Someone who speaks the CFO's language and the CIO/CTO's language, and can harmonize the two rather than letting them operate in parallel silos.

The fractional AI leader gets early wins on the board fast. Visible ROI that justifies continued investment to sponsors and LPs. And while doing that, they're laying the infrastructure, governance, and repeatable playbook that makes the larger program viable. By the time the engagement ends, the firm knows exactly what the permanent role needs to look like. And best of all, it'll be based on real implementation experience, not a job description written in a vacuum.

What I'm seeing is that most portfolio companies aren't ready for a full-time AI leader yet. They need the right starting point. A well-scoped fractional engagement is often exactly that.

What doesn't work, in any of these options, is optimizing for one domain and hoping the other takes care of itself. It won't.

Two Questions That Should Drive Your Decision

Wherever you are in this process, two questions should drive your decision.

  • Will this AI investment improve margins today? And does it build the kind of operational foundation that commands a higher multiple at exit?
  • Is our data infrastructure, security posture, and vendor selection positioned to support this at scale?

If the answer to either of those questions is "we'll figure that out later," then you've found a gap, and you should fill it before you do anything else.