How is AI impacting Private Equity Skillset and Talent?
This was the focus of our recent breakfast session we co-hosted with The Line, where we explored the impact of automation, machine learning, and generative AI on private equity operations. The discussion covered current adoption trends and how these technologies are reshaping the ways juniors and seniors work. Below is a recap of the key insights from the panel.
Key takeaways:
- AI has impacted teams differently: deal sourcing has seen much greater gains compared with the finance function
- Leadership is increasingly embracing AI
- New skill sets are starting to enter the industry
Speakers:
- Alex Ford, March Consultants
- Matt Guy, Holland Mountain
- Kevin Muller, Coller Capital
Moderator: Alice Murray, The Line
This article is the first in our series on AI and Talent, drawing insights from the event discussion. Don’t forget to subscribe to The Line to stay updated with the rest of the series.
PS: We’re running an AI Adoption Survey to explore how AI is shaping Private Markets. By taking part, you’ll be entered for a chance to win an AI-powered iPhone 16. Take the survey now!
What have been the key developments, trends, and challenges AI has presented this year?
Kevin Muller: At Coller Capital, we’re in the secondary private equity business, which inherently involves high volumes and complexity. Because of this, technology, and more recently AI, plays a crucial role in managing that complexity.
AI, as a field, is incredibly broad in its applications. From a data science perspective, it’s now essential to deploy resources effectively in this area. At Coller, we do extensive work in data science, but the recent emergence of generative AI and other advanced tools is particularly promising.
That said, I view AI as just one tool in a larger toolkit. It’s more important to focus on your operational challenges and assess how these tools can be integrated effectively. There are excellent tech providers embedding AI into their applications, covering areas like data collection and ESG, which is fascinating. However, I often see people asking the wrong questions; focusing on “How do we use AI?” rather than “What challenges are we trying to solve?” It’s essential to prioritise logically. Sometimes, AI might not be the best solution; the task might be something that could be outsourced or restructured entirely.
Matt Guy: Kevin’s right that one of the most frequently asked questions this year has been, “What should I be doing with AI?”
If we think back to 2023, that was the year many people began experimenting with tools like ChatGPT personally. I initially expected 2024 to be the year of broad application, but it has turned out to be more about experimentation.
At the start of 2024, many AI-focused vendors were still in the early stages of development, offering to build custom solutions. Over the year, however, they’ve refined their offerings by collaborating with managers, creating more targeted solutions.
While some may feel AI hasn’t yet transformed their operations, I believe we’ll see tangible benefits in 2025 and 2026 as these tools mature.
How has the industry’s journey so far with AI impacted specific functions?
Matt Guy: Internally, the impact of AI has varied. For instance, fund finance teams have seen minimal change due to the nature of their work, though some tools now help structure unstructured data for easier processing. Investor relations teams, on the other hand, have benefited significantly. CRM systems incorporate features like relationship sentiment and strength analysis, and there are dedicated AI tools for processes like PDQ management.
The biggest breakthrough, in my view, has been in deal sourcing. AI now allows firms to scrape tens of millions of websites, generate detailed company profiles, and rank them against investment criteria to create shortlists.
This capability simply didn’t exist before the evolution of generative AI. While not everyone is leveraging this yet, early adopters are likely to see substantial advantages.
Alex Ford: In my experience, the focus has been on leadership understanding AI and embedding it strategically into their operations. Whether it’s investment professionals or CFOs, the question is how they’re embracing and integrating these technologies.
We’ve seen significant progress. Comparing the talent searches I conducted five years ago to what I’m doing now, the sourcing strategies have evolved dramatically. Not only has the pool of talent expanded, but the possibilities of what AI can achieve have also grown.
Increasingly, we’re working with two types of leaders: those who are educated about AI and know exactly what they want, and those who are less familiar and asking how they can start leveraging these tools.
As Kevin mentioned earlier, everything should begin with identifying the problem you’re trying to solve. That strategic approach is essential at the leadership level.
Does the increased adoption of AI mean hiring in different skill sets?
Kevin Muller: The pace of AI evolution has been remarkable. Three years ago, AI wasn’t even a major topic, but now it’s reshaping the industry.
Previously, the big focus was on data, managing, governing, and leveraging it. Concepts like data stewardship and governance didn’t exist in private markets before the pandemic. Now, these are critical themes and they’re driving a shift in the kinds of talent we need.
This industry has traditionally hired accountants and financial analysts. But today, we also need specialists like data stewards to ensure data integrity and usability. This role didn’t exist three years ago, but it’s now indispensable in handling the growing importance of data.
Alex Ford: Absolutely, and it’s also about finding talent that aligns with your company’s culture while driving innovation. Many firms are cautious, even apprehensive, about AI because they don’t fully understand it or the risks involved. That’s why building robust controls and integrating AI in a safe, sustainable, and low-risk way is crucial.
We’re still on this journey, and it’s worth asking: how are we managing risks and ensuring proper oversight?
Matt Guy: I think successful AI implementation requires a mix of personality types and skill sets. You need creative thinkers who challenge the status quo and come up with innovative use cases, as well as risk-focused individuals who ensure AI operates within a solid control environment.
It’s also important to look beyond private markets for expertise. AI innovation in other industries offers valuable insights. If we don’t tap into that expertise, we risk falling behind.
Alex Ford: That’s a conversation we have often. Private markets firms tend to be risk-averse and prefer talent from within their domain. But for firms on a growth trajectory, the necessary expertise often isn’t available locally, it’s concentrated in markets like the US. We have to encourage clients to think creatively and look beyond traditional talent pools.
Kevin Muller: Like any transformative change, AI adoption must be driven from the top. Leadership needs to embrace it, recognise its potential and understand that success depends on having strong operational fundamentals.
You can have all the AI tools in the world, but without well-organised data and a structured process, those tools won’t deliver value. Businesses must take a systems-thinking approach, ensuring every part of the organisation works cohesively.
Like a Formula 1 car where the tires, chassis, and aerodynamics all need to function perfectly together.
Are you prepared to champion AI in 2025?
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