By Benjamin Wheaton
Over the past twelve to eighteen months, the most notable change in our Chief Technology Officer (CTO) searches has not been candidate availability or compensation inflation. It has been the brief itself.
AI has moved from being an interesting line item on the roadmap to a central pillar of enterprise strategy. As a result, the job description of the CTO particularly in growth-stage and PE-backed technology businesses has shifted materially. What boards and CEOs are underwriting today is fundamentally different from what they were assessing even three years ago.
Historically, most growth-stage CTO mandates centred on scale and professionalisation. Investors wanted infrastructure hardened, engineering quality upgraded, process discipline introduced, and delivery risk reduced. The emphasis was operational excellence and execution certainty. That foundation still matters. But it is no longer enough.
Today, AI capability is embedded into the core purpose of the role. The conversation has moved decisively beyond whether a company “has an AI strategy.” The more pressing question is where, specifically, AI is driving measurable commercial impact. It is all well and good to talk about generative models, copilots and data science capability. What boards increasingly want to see are real-world examples of AI embedded into live workflows and tied directly to value creation.
In our interviews, candidates are now asked to go far deeper than vision statements. They are expected to demonstrate where AI has reduced cost-to-serve, accelerated time-to-value for customers, increased release velocity, improved utilisation rates, or driven revenue expansion. We hear questions such as: Where has AI meaningfully improved customer workflow? How has it altered gross margin? Which KPIs moved as a result? What did you stop doing in order to prioritise it?
The distinction between experimentation and execution has become critical. Running pilots with foundation models or launching peripheral AI features is no longer sufficient. Boards want evidence of integration into core product architecture and operating mechanics. They want to see intelligence embedded at the centre of the value proposition, not just layered on top of it.
One of the most significant shifts we are observing is around velocity. AI is not only transforming customer-facing products; it is transforming how technology organisations operate internally. The strongest CTOs we see today are using AI to increase engineering throughput, compress iteration cycles, automate elements of testing and documentation, and materially shorten the path from idea to production. In capital-conscious environments, this acceleration effect is powerful. It enables businesses to achieve more with flatter cost curves and greater capital efficiency.
As a result, we are increasingly encouraging candidates to articulate not just what AI did for their product, but what it did for their organisation. Did release cadence improve? Did productivity per engineer increase? Was headcount growth moderated because AI tooling improved leverage? Did product discovery cycles shorten because insights could be generated more quickly from customer data? The expectation is that AI is both an external differentiator and an internal force multiplier.
This shift is also reshaping how CTOs structure their teams. Data has moved from a supporting function to a central strategic asset. We are seeing greater integration between product, engineering, and data disciplines, with AI expertise embedded directly into core squads rather than isolated in innovation units. The traditional separation between platform engineering and data science is narrowing, replaced by unified approaches to data architecture and model deployment. At the same time, governance frameworks are maturing rapidly. As AI becomes operationally embedded, considerations around explainability, compliance, and risk are designed in parallel rather than retrofitted later.
A compelling illustration of AI executed as an operating lever rather than a marketing narrative is Joblogic. In 2025, Vista Equity Partners announced a £100 million-plus growth investment in the business to accelerate its AI-first roadmap and European expansion. The strategy is not conceptual. AI is being embedded directly into core workflows such as intelligent scheduling, job allocation optimisation, compliance insights, and data-driven decision support. The objective is clear: improve utilisation, enhance customer efficiency, increase stickiness, and expand margin simultaneously. What is notable is not simply the scale of capital deployed, but the clarity of alignment. The investment is explicitly tied to a defined AI execution agenda that sits squarely within the CTOs mandate. AI is not treated as a peripheral feature; it is integrated into the operating spine of the platform.
Examples like this underscore a broader pattern. Where the right AI-capable CTO is appointed early in a growth journey, roadmaps tend to become sharper and more coherent. AI initiatives are prioritised based on commercial impact rather than novelty. Hiring plans are calibrated against strategic objectives, protecting capital efficiency. Board conversations evolve from abstract discussions about innovation to structured dialogue around revenue expansion, margin leverage, and defensibility.
Conversely, where the brief remains anchored in yesterday’s definition of the role focused narrowly on infrastructure and delivery, fragmentation often follows. AI experimentation proliferates without integration. Costs increase without proportional value capture. Messaging drifts ahead of technical reality. In PE-backed environments, where value creation timelines are compressed and scrutiny is intense, that misalignment can materially slow momentum.
The modern AI-ready CTO is therefore differentiated not simply by technical depth, but by perspective. The strongest leaders we place today are commercially literate technologists. They understand how model selection affects cost structure. They recognise that proprietary workflow integration can create more defensibility than algorithmic novelty alone. They are comfortable articulating trade-offs to investors and boards in the language of enterprise value.
They are also decisive. The pace of AI evolution penalises both inertia and undisciplined experimentation. Effective CTOs establish clear principles around experimentation, governance, capital allocation, and integration. They create organisational clarity about where AI will, and will not, be deployed. That discipline enables speed without strategic drift.
It would be easy to characterise the current focus on AI as cyclical enthusiasm. Our experience suggests something more structural. Even as specific models and tools evolve, the expectation that technology leadership shapes enterprise intelligence, and therefore enterprise value, is unlikely to reverse.
For growth-stage CEOs and mid-market investors, this changes how the CTO role should be defined and assessed. The question is no longer whether the technology leader can keep the platform stable and scale delivery. It is whether they can demonstrate, in practical and measurable terms, how intelligence reshapes the business model, accelerates execution, and compounds defensibility.
AI has not simply expanded the CTO job description. It has shifted its centre of gravity. Those organisations that recognise this, and hire accordingly, are not just adopting AI more effectively. They are increasing velocity, tightening alignment between product and value creation, and positioning themselves to compete in a market where intelligence is no longer optional, but foundational.
In an increasingly competitive and capital-conscious landscape, that acceleration may prove decisive.
If you’d like to find out more about this, please get in touch with me. I would love to hear from you.