The hardest part of AI adoption in most private equity-backed businesses is not choosing the right tools, it’s the slower work of shifting how people think about their roles, creating conditions where experimentation feels safe, and building the internal credibility to sustain change over time. That challenge often falls to the people and talent function. We have seen that the leaders in Mayfair’s portfolio who have gone furthest with AI are the ones who recognised that and leaned in, rather than waited.
We brought HR and talent leaders from across our portfolio together at one of our Boost! events to discuss how AI is changing their operating models, what the pioneer profile looks like in practice, and what this shift means for career development inside private equity-backed businesses.
How HR leaders in PE-backed businesses are restructuring their operating models around AI
One of the more practically instructive frameworks to emerge from Boost! discussions came from a head of HR at one of Mayfair’s portfolio companies, who has restructured their entire function around two named AI agents: one responsible for HR operations and documentation, the other handling recruitment workflows and salary benchmarking. Each has a defined scope, a job description, and what they describe as a personal development plan: a roadmap of expanding capability tied directly to their own growing understanding of what the tools can do.
The framing matters as much as the tooling. By assigning formal roles to AI tools within the operating model, with clear boundaries around what sits inside the tool’s remit and what remains a human decision, they created a structure the wider business could understand and engage with. It also created accountability because when the operations agent produces a probation outcome letter or a new-starter contract, there is a defined approval step before anything moves forward.
Running the HR function as a standalone across a headcount of 65, with 16 new hires processed in seven months, they attribute the onboarding workflows they built to freeing up enough time to deliver leadership training and present to the business in ways that simply would not have been possible otherwise.
Why the most valuable early AI adopters are defined by experimentation, not technical skill, and how to identify them
The leaders making the most progress with AI are characterised by a willingness to experiment and fail fast, rather than by technical background. The implication for talent and HR functions is a practical one: if the Pioneer Profile is not technical, it can exist anywhere in the organisation, and it will only become visible if there are structured opportunities to surface it.
One Mayfair portfolio company has built a deliberate incentive architecture around exactly this. An AI Innovator Award offers up to £2,000 per month for employees who submit working AI projects reviewed by the senior leadership team. This runs alongside internal leaderboards, team prizes, and a requirement for all employees to complete Anthropic’s free Claude 101 training programme as a shared baseline. This visibility mechanism surfaces the people who are already building things quietly, without waiting for permission. Instead of treating them as a compliance problem, bringing them in and structuring their experimentation is how pilot projects become operational systems.
How building AI capability is expanding the remit of people and talent leaders in PE-backed businesses
There is a longer-term career pattern worth making explicit, because it is directly relevant to how talent leaders should be thinking about their own development in AI-enabled organisations.
The people and HR leaders who have most expanded their operating mandates over the past decade are those who moved into digital and organisational change before it was standard practice for their function. HR leaders who built credibility in people analytics ended up owning broader business transformation. Those who led complex integrations or capability build programmes, across multiple geographies or through periods of significant M&A activity, found themselves with a seat at strategy discussions well outside their original remit. What AI does is accelerate the timeline for that kind of transition and broaden the range of business problems a people leader can credibly engage with.
The leaders who are furthest along in Mayfair’s portfolio are not the ones who waited for a clear mandate or AI strategy to be handed to them, they are the ones who treated ambiguity as the entry point and recognised that building AI capability is, fundamentally, a people challenge. That is, finding the right individuals, creating the right conditions, and using the capacity it generates to earn a broader role in the business.
Mayfair Equity Partners invests in management teams with exactly that disposition and supports them through our Boost! programme’s network of practitioners working through the same challenges in real time.
