Specialist: Technology & Data

An experience-led perspective on turning belief in AI into a live, value-generating platform, without pretending there’s a universal playbook.

For some time now, our view at Mayfair Equity Partners has been that data and AI are not simply incremental tools, but represent a fundamental capability shift in how information is processed, patterns are identified, and competitive advantage is created.

But while our conviction was strong, our starting point was not a detailed roadmap or a neatly packaged business case; it was something far messier, and far more familiar to leadership teams than many like to admit.

“We started with conviction, not clarity.”

The ability to analyse vast volumes of fragmented information, generate insights at scale, and detect signals that humans simply cannot process fast enough is not a future state. It is already reshaping how decisions are made across every industry, and private equity is no exception.

We believed early movers would build compounding advantage, and we believed waiting for a perfect moment to adopt AI was not a viable strategy, because in fast-moving capability shifts, delay is rarely neutral and is often irreversible. Yet despite that conviction, we faced an uncomfortable reality: we didn’t yet know where to apply AI in a way that would genuinely move the needle. The technology opportunity was clear, but the business anchor was not.

Why early exploration didn’t work

Our first instinct was the same one we see across many firms: create a committee, spread the responsibility, and ask smart people to explore possibilities alongside their day jobs. Twelve months later, we had activity, discussion, and interesting ideas, but very little delivery.

Not because the team lacked talent, but because emerging domains do not respond well to part-time ownership. AI is not a bolt-on capability; it demands focus, iteration, and a willingness to make decisions with imperfect information. Committees are good at generating debate, but they are not good at shipping platforms.

The turning point: single-point accountability

The real acceleration came when we changed the model, brought in dedicated experience, and gave it clear ownership. Mayfair appointed a CTO, Josko Grljevic, with a defined mandate: crystallise the problem, define the architecture, build a working solution, and drive adoption. Not as an exploratory exercise, but as a delivery effort with executive sponsorship and momentum behind it. That shift mattered for a simple reason: in unfamiliar domains, learning slowly is expensive.

“In unfamiliar domains, experience shortens the path to value.”

The cost of stalled decisions, repeated false starts, and prolonged experimentation often exceeds the cost of bringing in experience early, and once we had a single accountable owner, decisions moved faster, trade-offs were made quickly, and experimentation became structured rather than open-ended.

Choosing the right problem to solve

AI can be applied across almost every function in a private equity firm, from due diligence to portfolio monitoring, reporting, market intelligence, and internal operations, but not every use case justifies the complexity of building something meaningful.

We needed a problem where even a modest improvement would create disproportionate impact, and that meant focusing on earlier, higher-quality investment opportunities. In a market where many deals arrive through highly intermediated processes, the real advantage comes from identifying opportunities before they become competitive auctions, which led us to a simple exam question: can we identify investable signals earlier than the wider market?

“The goal was not to replace judgement, but to focus it.”

The biggest surprise: AI wasn’t the hard part

At the outset, we assumed the most complex challenge would be the technology itself: machine learning models, tooling, and technical uncertainty. In practice, the opposite was true.
The AI stack is increasingly accessible, strong tools exist, and strong partners exist, and the technology layer can often be implemented faster than people expect. What proved far harder was defining what “good” looks like. AI systems require clarity, shared criteria, and consistent logic, and while investing is built on judgement, it is not always built on standardised definitions of what makes an opportunity compelling, which is precisely what any scalable system demands.

The real workload: data, not code

Data is messy, incomplete, lagging, and inconsistent, which initially feels like a constraint; but, if data is imperfect for everyone, then the firm that can combine, enrich, structure, and interpret it effectively builds advantage. Competitive edge comes not from having more data, but from extracting more meaning from imperfect sources, and in our experience the majority of time was spent on sourcing, cleaning, enriching, and structuring information so that AI could generate something reliable.

Adoption is the real differentiator

Even with a functioning platform, the hardest part is not deployment, it’s behavioural change, because tools only create value when they change how people work. That requires integration into existing workflows, reinforcement through visible wins, and a design philosophy that supports judgement rather than attempting to replace it.

“Technology will keep getting easier. Change will not.”

As AI accelerates, the gap between what is technically possible and what organisations can absorb operationally will widen. Change management is no longer a peripheral concern, it’s a core operating capability. The firms that win won’t necessarily have the most advanced models; they will be the firms that adapt faster, iterate more often, and embed new capabilities with less friction.

No universal playbook

There is no single blueprint for building AI inside a private equity firm, and most organisations can capture meaningful value before they ever reach advanced AI. Documenting and improving processes, connecting systems, and automating repeatable tasks often deliver outsized returns. AI should not be used to compensate for broken workflows; it should amplify well-designed ones.

We began with belief in a capability shift, but not certainty about where to apply it. We made mistakes, went down blind alleys, and challenged assumptions that proved wrong. Today, we’ve moved from conviction to a live, evolving internal capability, and more importantly, we have built the institutional memory and the organisational confidence to keep adapting as the technology evolves.

When people talk about AI, the assumption is often that it belongs to engineers and developers. At Mayfair, we are seeing something different: the most exciting breakthroughs often come from people who simply spot a problem, stay curious, and are willing to experiment.

At Mayfair’s recent Boost! Horizon event for portfolio CEOs and board members, Tessa Remp, an executive assistant in our team, presented the live event portal she had built herself using Replit. Tess is not a developer, and has never written a line of code, yet within weeks, she had produced a professional web app that delegates could access throughout the event.

“I’m an EA, not a developer. I know absolutely no code,” Tessa told the audience. “But I wanted to show that you don’t need to be technical to create something useful.”

From curiosity to a real product

It began when Mayfair hosted an internal hackathon for its portfolio CTOs with Replit, an AI-powered coding environment that allows users to build software through natural language prompts, turning plain English into working code.

The brief was deliberately open. Participants were encouraged to bring ideas rather than technical expertise, and to use natural language to turn those ideas into working software. The focus was not on building something perfect, but on understanding what AI makes possible.

“I realised there were no expectations for me to be technical,” Tessa said. “We brought our ideas to the table and watched them turn into code.”

Solving a real event problem

As part of her role supporting Mayfair’s events programme, Tessa decided to build a live event portal that moved from a simple concept to a fully functioning solution for a high-profile gathering of portfolio CEOs. Using Replit and straightforward prompts, she created an app featuring an instantly updateable agenda, speaker profiles with photos and LinkedIn links, a Citymapper-inspired travel planner to help delegates navigate to the venue, and live local weather updates.

What followed was a rapid build process driven entirely by prompts, experimentation and iteration. Tessa integrated the web app with Transport for London and weather services, despite not knowing what an API was, or how it worked behind the scenes.

“If you don’t know what API stands for, neither do I,” Tessa joked during the presentation. “But I got the gist of what it does.”

She also used Replit’s design tooling to generate a polished one-page agenda, including speaker photos, interactive elements, and embedded LinkedIn links. Pushing it further, she prompted the platform to create a moving image of the Shard to bring the portal to life, inspired by the visual design on other firms’ websites.

Working fast, learning faster

On the day of the event, Tessa realised that registration was being managed through a simple Excel checklist. With minutes to spare, she asked Replit to create a one page check-in tool. She pasted in a rough list of names and refined the output until it did exactly what was needed: tick attendees off, track arrivals and highlight who was missing. The entire tool was built in around five minutes.

Not every feature worked perfectly, but instead of being discouraged, she treated it as part of the process.

“One of the biggest lessons is that AI doesn’t always understand you the first time, or maybe even the fifth,” Tessa said. “It’s a conversation. You have to work with it.”

Key lessons: curiosity beats expertise

Tess approached the tooling with curiosity, she tested ideas quickly, asked the AI to suggest improvements, and treated every version as something that could evolve.

It was a useful reminder that deep technical knowledge can sometimes get in the way. Some of the more technically experienced participants over-engineered their projects, bringing assumptions and complexity that limited what the AI could do. By contrast, starting with plain English and curiosity proved to be an advantage.

“The most remarkable part was that Tessa built the strongest application in a room full of technical people. It was a real reminder that AI is shifting what ‘building’ looks like.”

Investing in our team, and the future

This experience reflects how we think about AI at Mayfair. We see it as a productivity tool for everyone, not just specialists. This is why we actively collaborate with leading AI companies like Replit, and why we create opportunities for our team to explore emerging tools in a practical way.

“I didn’t think I could do this six months ago,” Tessa said. “But once you start exploring, you realise how much is possible.”

A few years ago, the idea that anyone in the organization could build and deploy a live web app for an international event would have seemed unlikely. Six months ago, Tessa would not have expected it herself, but today, it is simply part of how we work.

Reflections from Boost! Horizon

Software creation is changing fast

One of the clearest takeaways from Boost! Horizon was that the biggest impact of AI may not be in analysis, research or content generation, but in how quickly organisations can now turn ideas into working software.

The Replit session in particular landed strongly because it demonstrated something that many leaders instinctively understand but have not yet seen in practice: that prototyping is no longer limited to engineering teams. The gap between a business problem and a functional digital solution is reducing rapidly, with implications for speed, innovation, productivity and competitive advantage.

Why this matters: prototyping has always been a bottleneck

In most organisations, the ability to build software has historically been constrained by engineering capacity. A marketing team might want a campaign microsite, an operations lead might want a workflow tool, or a product team might want to test a new feature, all competing for attention and resource.

At Boost! Horizon, the shift being discussed was not just ‘AI makes engineers more productive’. It was the idea that AI is changing who can build, and how quickly they can do it.

“What we believe is the next wave of software creators really is right here in this room, no matter what technical background you might have.”

This is not about removing the need for engineering, rather it is about changing the front end of innovation. If teams can prototype solutions themselves, engineering effort can be directed towards scaling, security and integration, rather than get tied up in early-stage experimentation.

The rise of “prompt-to-software”

The most significant development highlighted in the Replit demonstration was that natural language is becoming a usable interface for building digital tools. Rather than writing code line by line, users describe what they want and iterate on the output until it works.

The pace of experimentation has changed and along with that the costs of trying something new. Many ideas that would previously have required a formal project, a development roadmap, and multiple approval steps can now be tested much more quickly.

The first version of a product or tool is rarely the final one, so the ability to test and adapt quickly is increasingly becoming part of how digital businesses compete.

Engineering is not going away, but its role is shifting

A key point that came through clearly during the day is that demos are not the same as production systems. Tools can dramatically accelerate prototyping, but businesses still need technical leadership to ensure that what is built is secure, maintainable and scalable.

However, the underlying shift remains: the early-stage work that traditionally sat exclusively with engineers is increasingly becoming accessible to non-technical teams, changing what engineering teams spend time on.

It also changes the nature of internal innovation. Organisations can experiment more widely because the cost of failure is lower. A prototype can be built quickly, tested quickly, and either improved or abandoned without large sunk costs.

The organisational implication: more builders, more momentum

Many internal business processes are still held together by spreadsheets, manual reporting, email chains and repeated admin. These problems often persist not because they are hard to solve, but because they never rise high enough on a product or engineering backlog to justify investment.

“But we’re thinking, what if everyone in your company was a builder?”

AI-enabled prototyping changes that equation. If teams can build small internal tools quickly, a large number of ‘minor’ problems can be solved in ways that compound over time. Over the next few years, the companies that move fastest are likely to be the ones that treat prototyping as a distributed capability rather than a centralised function.

A competitive advantage hiding in plain sight

The most commercially relevant implication is speed. If software becomes easier to prototype, competitors can test ideas faster, launch internal tools faster, and improve customer-facing workflows faster.

For management teams, this raises a strategic question: if a competitor can build and test a tool in days, what happens to businesses still operating on quarterly planning cycles for digital delivery? In digital markets, product differentiation is often driven by user experience, responsiveness, and the ability to adapt to shifting customer behaviour, making this level of productivity highly relevant.

What Boost! Horizon revealed

Boost! Horizon was not framed as a ‘future vision’ conference. It was a practical look at what is already possible today. The Replit session demonstrated that software creation is becoming more accessible, more iterative, and more embedded into day-to-day business problem-solving.

“Unless you’re using AI on a day-to-day basis, it’s really hard to understand how magical it is.”

The larger takeaway is straightforward: AI is not just changing what software can do. It is changing who can create it, and how quickly it can be created, a major shift in how innovation happens.

Mayfair’s Boost! programme exists to strengthen connections and unlock value across portfolio businesses. Boost! Horizon was designed specifically for senior leaders and board members across our portfolio to create space for strategic thinking and be a practical forum for the reality of executive decision-making in a world where technological change is no longer incremental, but structural.

Reflections from Boost! Horizon

Whenever a new technology wave hits the mainstream the excitement comes first, then the scramble, then the inevitable attempt to treat the new thing as something to be plugged into the existing operating model, shown in a slide deck, and marked as progress.

Boost! Horizon made one point unmistakably clear: AI does not fit that pattern. It is not an add-on, just another tool to sit alongside the rest of the tech stack. It is a structural shift in how digital businesses will be designed, run, staffed, and scaled.
That distinction matters because businesses that treat AI as ‘a feature we should ship’ will likely miss the larger change already underway: AI is rewriting how work gets done, how products get built, and how competitive advantage is created.

“AI isn’t a tool. It’s something that’s much more transformational about how we operate.”

The illusion of “AI as a project”

A striking thread throughout the day was how often AI gets framed like a conventional initiative, a pilot, a transformation programme, or a digital workstream that can be scoped, funded, delivered, and moved on from.

AI behaves differently, it does not politely wait for annual planning cycles. The technology itself is evolving so fast that a project mindset almost guarantees obsolescence.

One panel discussion captured the paradox well. On the one hand, leaders are warned not to wait because competitors will move too quickly. On the other, they are reminded that what works in a demo rarely works in production.

This not a reason to disengage, instead it is a reason to build differently. The right question is not ‘how do we deliver an AI project?’ but ‘how do we build an organisation that can continuously absorb AI capability?’

“The winners will not be the companies with the best one-off AI experiment. They will be the companies that rewire themselves for constant iteration.”

The shrinking cost of building

If AI is a structural shift, it is because it changes the economics of creation and innovation. Several sessions demonstrated how quickly functional software can now be produced, even by people who are not traditional engineers. The premise of ‘everyone can be a builder’ is not just motivational language, it is a real organisational change.

When the cost of prototyping collapses, decision-making accelerates, experimentation becomes cheaper and iteration becomes continuous. The pace at which a company can test and validate its own ideas becomes a competitive advantage in itself.

Even more striking was the idea that teams can be dramatically smaller than traditional software builds require. One speaker described how AI-assisted development has reduced team size expectations to a fraction of what was once considered normal.

This is not simply productivity improvement; it is a structural shift in organisational design. It is changing how organisations staff themselves, what ‘capacity’ means and how they budget. It also changes how fast they can respond to market opportunities.

From technical advantage to cultural advantage

Ten years ago, the advantage in digital businesses was often technical with better infrastructure, better engineering talent and better data systems. Those things still matter, but Boost! Horizon surfaced a different idea: AI will increasingly reward cultural readiness.

The companies that win will be those that can adopt and adapt quickly, in theory and in practice. One panellist described how rapidly the planning cycle itself is being rewritten by the speed of AI change. The idea of committing to a single approach for a year is becoming outdated, because the tools themselves may shift within weeks.

“The cycle time of AI is different than any other technology that we’ve ever seen.”

This is why AI cannot simply be ‘owned by IT’, the structural shift is that AI becomes part of the organisational fabric. It has become something leaders need to understand at a working level, because strategic decisions increasingly depend on understanding what is now possible.

Speaker Matt Strain (the-prompt.ai) made the point that senior leaders often assume they can delegate AI to others, but without day-to-day familiarity, they will struggle to understand its real implications.

“I’ve met a number of senior leaders… that have said, ‘I have people on my team that do AI.’ I think that’s a real mistake.”

AI changes the shape of work itself

Another major undercurrent throughout the day was that AI is dissolving traditional boundaries between roles.

The lines between legal teams, risk teams, operations, marketing, product, and engineering are already shifting. This is not only because AI automates tasks, but because it enables people to perform tasks that were previously outside their skill set.

For example, a non-technical product manager can now prototype, a marketing lead can generate assets and test messaging faster, and a customer support team can deploy intelligent triage and resolution systems. At board-level, members can interrogate scenarios and model strategic outcomes with an AI co-pilot.

This is why AI is a reshaping force for the entire organisation, and with that comes discomfort. Governance and risk were framed as the disciplines needed to safely scale. Managing AI agents over time is itself a new operational competency, almost like supervising a new kind of workforce.

“How we supervise effectively our mini AI staff… is a new discipline.”

We are no longer talking just about tools, we are talking about systems that behave more like collaborators.

The new operating model: continuous experimentation

AI adoption is not a one-time transformation. It is a continuous cycle of testing, learning, refining, and scaling requiring organisations to be comfortable with unfinished versions. The panel on turning hype into impact repeatedly returned to the value of small pilots, fast iteration, and the discipline to stop what is not working.

“Try it, start small, prove out the concept. If it works, then build on it. If it doesn’t, kill it quick.”

In an environment where the underlying technology evolves rapidly, the most dangerous assumption is that you can design a perfect solution upfront. Instead, AI forces businesses to behave more like living systems: adaptive, responsive, and constantly evolving.

What this means for digital business builders

The most compelling takeaway from Boost! Horizon is that AI changes the logic of competition. If software becomes cheaper to create, then execution speed becomes more valuable. If intelligence becomes more accessible, then judgement becomes more valuable. And if content becomes easier to produce, then trust becomes more valuable.

AI is not simply improving existing businesses; it is redefining what a ‘well-run digital business’ looks like. AI is less like a new product category and more like a new layer of the economy that sits beneath everything else.

Businesses that treat AI as a feature will likely produce incremental improvements. But the businesses that treat AI as structural and integral will rebuild their organisations around it, creating a compounding advantage.

As the closing remarks at Boost! Horizon emphasised, the risk is not that leaders do too much too quickly, but that they ignore the shift entirely, waiting for certainty that will never arrive.

“The wrong answer is to just ignore it and wait until tomorrow.”

Mayfair’s Boost! programme exists to strengthen connections and unlock value across portfolio businesses. Boost! Horizon was designed specifically for senior leaders and board members across our portfolio to create space for strategic thinking and be a practical forum for the reality of executive decision-making in a world where technological change is no longer incremental, but structural.

Reflections from Boost! Horizon

Capability is becoming abundant

AI capability is spreading fast with powerful and accessible tools increasingly embedded in everyday workflows. The real question facing leadership teams is no longer whether AI works, but whether it can be trusted.

Only a few years ago, AI felt specialist and technical. Today, it feels almost ubiquitous, from prompt-driven content creation to agentic systems that build and deploy applications in minutes. As one speaker demonstrated, the barrier to entry has fallen dramatically and complex workflows that once took weeks can now be prototyped in hours.

This democratisation is profound. It changes who can build, who can experiment and who can innovate. But when everyone has access to similar foundational models, how can organisations differentiate themselves?

From demo to production: the credibility gap

In the context of customer-facing AI agents, the distinction was made clearly. A pilot can show promise, and a demo can impress, but production environments demand resilience, monitoring and governance.

This is the credibility gap. It is one thing to generate an answer and another to ensure that answer is consistent, explainable and aligned with policy. It is one thing to write code from a prompt, another to integrate that code into regulated, customer-critical systems. Trust has become a technical, operational and cultural capability.

Data as a reputation asset

AI is increasingly becoming the interface between businesses and their customers, which means the quality of the data feeding it has become a matter of brand equity.

One vivid example illustrated how domain-specific context transforms performance. A generic model, given an image of a broken machine part, produced plausible but incorrect recommendations. A different system that was grounded in validated internal documentation identified the correct part and repair path

The lesson from this is straightforward. General intelligence is powerful, but contextual intelligence is decisive.

Organisations that treat their data as an afterthought will struggle. Those that curate, validate and structure their data effectively create a trust layer around AI outputs. That layer is not cosmetic. It is foundational.

In this sense, data maturity is no longer just an analytics question. It is a credibility question.

Governance as an enabler, not a brake

It is tempting to frame governance as the counterweight to innovation but at Boost! Horizon, that narrative was challenged. In highly regulated industries, AI is already live in agentic form, but the challenge is how to supervise it.
The companies making progress are not ignoring risk, they are building mechanisms to manage it. Guardrails, red teaming, contractual controls and clear data boundaries were discussed as practical tools, not theoretical safeguards.
Crucially, leadership ownership was emphasised. AI cannot be delegated entirely to technical teams; it must have a seat at board level. Governance, done well, accelerates deployment and allows businesses to move faster because they understand the boundaries.

Trust as commercial advantage

When AI-powered interfaces become the first point of contact for customers they will judge not just speed or novelty, but reliability. Trust will shift from a marketing promise to an operational reality. If the system hallucinates, misprices or mishandles sensitive data, the brand absorbs the impact immediately. And when AI interfaces consistently deliver accurate, relevant and transparent responses, it enhances the customer experience in a measurable way.

It is no longer enough to say, “We use AI.” The more meaningful statement is, “Our AI works reliably, securely and responsibly at scale.”

Beyond hype

The phrase ‘beyond hype’ surfaced more than once during the day. Artificial intelligence is embedded, shaping product development, customer service, marketing and operations. At Boost! Horizon, the message was clear. The next competitive advantage in AI will not be capability alone.

Credibility is harder to build. It requires investment in data, architecture, governance and culture, and leaders who are willing to engage deeply rather than outsource understanding. In a landscape defined by rapid change, trust will become the most durable differentiator.

Mayfair’s Boost! programme exists to strengthen connections and unlock value across portfolio businesses. Boost! Horizon was designed specifically for senior leaders and board members across our portfolio to create space for strategic thinking and be a practical forum for the reality of executive decision-making in a world where technological change is no longer incremental, but structural.

Mayfair Equity’s bi-annual Boost Technology Forum is part of Boost!, our firm’s networking and development programme, intended to spark collaboration across the portfolio, strengthen connections, and provide our management teams with practical tools and insights. Through a series of curated events, we aim to unlock potential by empowering individuals, adding value to businesses, and shaping the digital leaders of tomorrow.

We brought together CTOs and CPOs from our portfolio to explore how agentic AI and vibe coding is transforming the future of business. Hosted by Thomas Nielsen, one of our Growth Specialists, and facilitated by Replit, the forum was designed as a hands-on workshop rather than a theoretical discussion.

During the day, participants worked side by side with the Replit team to prototype automation use cases, experiment with new workflows, and define pilot projects that could create measurable value for their organisations. The result was a day of collaboration that combined practical problem-solving with forward-looking strategy.

For Thomas, the forum epitomised Mayfair’s mission to build leaders equipped to thrive in a world of disruption.

“We’re a firm that doesn’t just invest in companies, we are actively investing in building digital leaders,” Nielsen said. “Harnessing technology isn’t optional, it’s the pathway to leadership. We give our management teams the tools, networks, and confidence to transform the way they work, and ultimately, the way their industries operate.”

Our teams saw first-hand how agentic AI can accelerate the transition from service-led workflows to scalable, productised capabilities. A demonstration from the Replit team showed how processes relevant to our companies, such as onboarding, ticket triage, and reporting can be developed and tested in minutes, not days! By the afternoon, our portfolio company teams were sharing prototypes that illustrated not only the technical feasibility of AI-enabled workflows but also the business impact they could deliver.

The discussion extended beyond technology into the organisational implications of automation. Leaders examined how AI would affect roles, governance, and the integration of automation into existing teams and processes.

Reflecting on the forum, Nielsen highlighted Mayfair’s long-term vision for its portfolio companies.

“Our role is to help our management teams build businesses that thrive in a world defined by change,” he said. “Agentic AI is a critical enabler of that journey. The Boost! Technology Forum showed just how ready they are to embrace the opportunity, and we are committed to standing shoulder to shoulder with them as they shape the future of their industries.”