Finatal’s Technology Leadership practice sits at the point where technology stops being a support function and starts becoming a value creation lever. In this interview, Max Yish, Director at Finatal, explains how investor expectations are changing, what AI hiring demand really looks like, and why execution capability now matters more than ever.
Max Yish leads Technology Leadership practice at Finatal, covering technology, data, go-to-market and AI mandates.
(7 minute read)
Technology leadership has become central to value creation in private equity. How has that changed what investors expect from technology leaders?
Technology has always mattered in private equity, but the expectation has shifted. Historically, it was about stability, infrastructure and keeping the platform running. Now, investors expect technology leaders to have a direct impact on value creation.
That means driving growth, improving scalability, supporting margin expansion and making decisions that influence exit value. The role has also broadened. Technology, product and data are far more connected than they used to be.
The leaders who do well in PE are board-facing, strategically credible and able to explain how the platform needs to evolve to support the value creation plan.
How has Finatal’s Technology Leadership practice evolved to meet that demand?
The Technology Leadership practice is continuing to scale quickly and is now one of the fastest-growing practices across the Finatal portfolio.
This month alone, we have placed several CTOs into PE-backed businesses across the spectrum of private equity, from large-cap funds through to mid-cap investors. Despite the differences in scale between those businesses, a clear theme runs through the mandates.
Each of those CTO appointments came with a defined AI transformation expectation. In most cases, the AI roadmap or transformation plan had already been discussed and agreed at partner level, involving both the fund’s operating partners and the C-level leadership team within the portfolio company.
That reflects a broader shift we are seeing across the market. Technology leadership hires are increasingly expected to arrive with a clear view on how AI will shape the operating model and support the value creation plan of the business.
As a result, our role is evolving as well. We are helping investors shape these conversations earlier, using our market insight to support discussions around leadership capability, team structure and execution before the hire is made.
AI is dominating boardroom conversations. What are you actually seeing investors hire for?
The conversation has matured.
Early on, demand was often for standalone AI specialists. Those roles still exist, but now investors are more often looking for AI capability embedded within broader leadership positions.
Rather than hiring someone to sit on the side and talk about AI in theory, they want AI-native CTOs, CDOs or product leaders who can build it into the business. We are also seeing AI capability emerge at fund level to support portfolio-wide value creation.
The real shift is from experimentation to execution. Investors are asking where AI can create ROI, improve efficiency or support growth now.
Everyone says there is a shortage of AI talent. From your perspective, how real is that gap?
It is very real, but it is also very specific.
There are plenty of people who can talk confidently about AI. There are far fewer who have actually delivered it in a meaningful way. The pool gets much smaller when you look for leaders who have deployed models into production, built AI-driven products and delivered measurable commercial outcomes.
That matters even more in PE-backed businesses, where pace and expectations are high. Investors are not paying for theory. They need people who can combine technical credibility, commercial judgement and execution.
When you assess AI leaders, what separates the exceptional candidates from the rest?
For me, it comes back to value.
A lot of execs can talk about tools and models. The real question is what value they have actually delivered, what changed because of their work, and what commercial outcome came from it.
The strongest execs can explain how they translated AI into business value. They understand the infrastructure behind it, they know what good deployment looks like, and they are comfortable working cross-functionally in a fast-moving environment.
Finatal has also supported investors with AI operating partner work. What does that involve?
A big part of that work is helping investors move from AI ambition to something practical. We are seeing growing demand from funds for AI operating partner capability, either through dedicated AI operating partners or by expanding the remit of existing value creation teams.
These individuals typically work across multiple portfolio companies, helping identify practical AI opportunities where there is a clear case for efficiency, growth or both. Their role often starts with assessing AI maturity across the portfolio, identifying high-ROI use cases, prioritising where AI can create the most value, and determining whether the right leadership capability is in place to deliver.
That is where our Technology Leadership practice increasingly intersects with this work. In some cases, the outcome is a clearer roadmap. In others, it leads directly to new leadership hires across CTO, CDO or product leadership.
The common theme is execution. The focus is not on theory or innovation for its own sake. It is on where AI can genuinely drive value creation and what leadership is needed to make that happen.
Looking ahead, how do you see AI reshaping leadership teams in PE-backed companies?
I think there has been a bit of an overreaction in some parts of the market, particularly around SaaS. There is a fear that AI is going to come in and eat SaaS for breakfast. I do not see it like that. If anything, it is more of a light lunch.
The best SaaS businesses still have real moats, real stickiness and real complexity in their data and customer environments. From the outside, it can look easy to replicate something quickly. In reality, these are not businesses that are easily displaced overnight.
What we will see is AI being embedded into leadership teams rather than completely reshaping them. It will sit across product, operations, finance and sales. In five years, AI literacy will be a baseline expectation for leadership teams.
Are we at risk of hiring too many AI strategists and not enough AI operators?
Yes, I think there is a real risk of that.
There are definitely businesses hiring AI strategists before they have worked out the use case, the implementation path or the actual outcome they want. In PE, that can become expensive very quickly.
What investors increasingly want is execution capability. They want leaders who can build, deploy and deliver commercial impact. If you are just adding layers of strategy without improving outcomes, you are missing the point.
What advice would you give investors thinking about their next technology leadership hire, and how do you see the role evolving over the next few years?
The biggest mistake investors can make is hiring purely for technical expertise without thinking about the commercial context.
The reality right now is that none of us can say with certainty what technology organisations will look like even in the near future. You are already seeing large technology companies restructure teams as they pivot towards AI. The message behind those moves is clear: the way technology and data teams are built is changing quickly.
That makes commercially driven technology leaders even more valuable. The best leaders are the ones who can help investors think through what the organisation should look like, how teams evolve and where AI can genuinely create leverage.
For operating partners, that commercial lens is probably the most important thing to vet for today. It used to be a buzzword. Now it is quickly becoming one of the defining characteristics of the strongest technology leaders in the market.
This discussion was documented by Ali Delaney, Senior Marketing Executive at Finatal.