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 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.