There is a version of the AI conversation in private equity that has become almost commonplace.

Someone at a portfolio review mentions AI strategy. The management team produces a slide. Heads nod around the table. Everyone agrees it’s important, but nothing materially changes.

The firms that are actually moving the needle have stopped treating AI as a single agenda item. Instead, they are running two distinct conversations in parallel:

1. What AI means for the customer-facing product
2. What it means for how the business itself operates internally

These are related, but they require different thinking, different ownership, and different measures of success – and potentially different leaders in place.

Conflating them, or trying to solve both with the same initiative, is where a significant amount of time and money is being lost in Private equity portfolios right now.

AI within the Product roadmap

Across the tech-enabled services and SaaS businesses we work with, the product conversation has matured considerably.

Eighteen months ago, the question in most board meetings was “what is our AI strategy?”

Today, the better-run businesses have moved past that.

The conversation now is where AI is showing up in the metrics that matter and investors really care about: retention, net revenue retention, competitive win rates and average contract value.

The PE firms getting ahead are challenging portfolio company management teams to identify a short list of top business priorities and define specifically how AI can push those initiatives forward. Critically, they view AI as a part of the business strategy – not a standalone strategy. Portfolio companies seeing early return on investment are using AI to enhance products, boost revenue, and expand margins through operational efficiencies.

That sounds straightforward. In practice, it is demanding.

It requires leadership teams who can separate genuine product opportunity from competitive noise. Leaders who can make clear build-versus-buy-versus-partner decisions quickly, and ship features with commercial logic behind them rather than features that exist to populate the AI section of an investor presentation.

The businesses that are finding this difficult tend to share a common characteristic: they moved to implement AI because it felt strategically necessary, not because they had identified a specific problem their customers were willing to pay to solve differently.

The result is often functionality that demonstrates intent, but fails to move the needle on the metrics investors are tracking.

The more durable AI product investments we see across our mandates share three traits:

1. They increase switching costs by deepening integration into customer workflows
2. They compound in value as more data flows through the system over time or through acquisition
3. And they change the unit economics of delivery in ways that are clear to the buyer, meaning the customer can see and articulate why the AI-enhanced version is worth more.

An AI initiative that does not meet at least two of those three criteria are difficult to defend at exit.

The internal operating model discussion

The operational AI conversation tends to get less attention in investor forums, partly because it is less visible in the headline product narrative and partly because the gains are harder to attribute cleanly. However, for many of the businesses we work with, this is where the more immediate value creation opportunity sits.

AI deployment is already beginning to create real separation within private equity.

Practically, from our perspective, working with leaders in executive search, this is engineering teams to move faster because AI-assisted code generation has become part of the standard workflow. Product teams are prototyping quickly and work in a far more efficient way with their customers

The firms generating real returns from AI tend to share three things in common:

1. They treat AI as organisational infrastructure, not a tool
2. They redesign workflows around AI capabilities instead of bolting AI onto existing processes
3. And they build cross-portfolio playbooks that compound knowledge with every deal

The gap between those firms and the rest is not primarily about technology access. The tools themselves are increasingly available to everyone. The real differentiator is whether the leadership team has the capability and the mandate to change how the organisation works, not just what software it runs.  This, especially within Technology and Product, requires leadership that takes it seriously enough to redesign teams and processes rather than adding tools to existing ones.

The capital commitment is real. The returns depend on leadership.

By 2026, two-thirds of PE firms expect to invest over a quarter of their total budget in AI, up dramatically from just three years ago, when 92% of firms were spending less than a quarter of their budget on it, according to EY’s Q4 AI Pulse (Private Equity) report. That is a significant shift in focus of capital, and it is happening at speed.

The next question is around whether the leadership teams within the portfolio are positioned to take advantage of this investment. This is where the hiring conversation becomes directly material to the investment thesis.

A CTO who cannot clearly articulate how an agentic world changes the architecture decisions and cost structure of the product is not just behind the curve technically. They become a liability in the next fundraising conversation, because any sophisticated buyer or co-investor will ask that question and expect credible, fluent answers.

A CPO who has not genuinely stress-tested the product roadmap against a world where AI agents can replicate parts of what the software currently does is operating with an incomplete picture of competitive risk. If the same AI capabilities that are accelerating their own internal prototyping and compressing MVP timelines are available to every well-funded startup, the moat looks very different. The roadmap they present to the board may look coherent, but it must also contain assumptions that will not survive contact with the market in twelve months.

A CIO or Head of Technology who still treats internal tooling as a support function – something to be managed rather than a source of operating leverage – is leaving significant margin on the table in a market where margin is precisely what investors are focused on.

These are not hypothetical asks from leaders anymore; these are the parameters we are being asked to assess candidates on.

What does good look like right now?

Across the CTO, CPO, and CIO mandates we are currently delivering, the common thread in the strongest candidates is not a particular technical background or a specific set of tool experience. It’s the combination of commercial clarity and intellectual honesty about where AI creates genuine value and where it does not.

We are seeing CPO candidates producing prototypes to show CEOs at interviews – the world is moving fast.

The leaders who are adding the most value to PE-backed businesses right now are the ones who can hold both conversations at once: making clear-eyed decisions about where to invest in AI product capability, whilst simultaneously driving the internal operating changes that convert AI tooling from a cost into a competitive, value-creating advantage.

That combination is more rare than the market currently appreciates. And the gap between businesses that have it in the leadership team and those that do not is beginning to show up in performance in ways that are increasingly hard to ignore.

If you are a founder, investor, or board member working through what good looks like for technology or product leadership in your portfolio, get in touch to have a direct conversation about what we are seeing across the market right now.

John Smith, Partner, Executive Search, Digital & Technology Practice, is a Technology and Product executive search specialist with over 15 years of experience in search and extensive experience placing senior leaders in private equity- and investor-backed businesses.

Get in touch to continue the conversation.