This post is part of our 2024 Market Tends Report: Smart Capital: tech and AI trends in PE operations. Download the full report for more insights into how firms are implementing emerging technology across their operations.
Often enablers of operational efficiencies, private equity firms are now looking to their own operations to find areas ripe for disruption.
In the last decade – what Singaporean sovereign wealth fund GIC’s Chief Investment Officer, Jeffrey Jaensubhakij, described to the FT last year as the “golden age” of private equity – conditions didn’t quite demand an efficiency mindset at the firm level.
But times have changed – Jaensubhakij went on to say the era of high valuations, lower leverage costs and minimal interest wouldn’t be “coming back any time soon.”
Private equity funds are racing to understand the impact that recent advances in artificial intelligence could have on portfolio companies but have made only tentative progress in using AI software tools to improve their own operations, like the sourcing of new investments.
AI’s potential, while vast, is still at early stages – effectively applying the tech while things are changing at pace makes implementation a challenge and AI takes time to deliver significant, tangible value. This might explain why a majority of attendees polled at the Women in PE event in March said AI hadn’t lived up to its hype at all.
There may be several causes. One is that firms are in a rush to adopt but aren’t entirely sure how, in addition to friction when adoption does finally get underway.
Reliability and accuracy are both big sources of frustration when it comes to AI, particularly at the portfolio level. A thorny challenge is bias – AI leverages human generated data, so it perpetuates human bias when analyzing this information and has been widely known to fabricate answers.
Risk governance is also a consideration for firms wanting to leverage AI: allowing the software to access proprietary data could erode the firewall they use to keep that data away from competitors.
However, as we’ve started to see from very early success stories across the industry, time and training will likely smooth over some of these issues.
“That’s something funds are thinking about, but there’s probably 10 or fewer funds that are really making a big push there,” said Richard Lichtenstein from Bain & Co. on a recent webinar.
That could soon change if more private equity firms follow the path being blazed by some of the world’s largest alternative asset managers, including The Carlyle Group and Blackstone, which have both discussed the deployment of AI internally and in portfolio companies on recent earnings calls. Blackstone employs a 50-plus member data science team and is “rapidly and significantly expanding” its AI capabilities, CEO Stephen Schwarzman said on the firm’s second-quarter earnings call.
“We believe that the new generation of AI has the potential to transform companies and industries. And the timeliness and effectiveness of its implementation will be determinative of who the winners and losers will be,” Schwarzman said.
According to Ross Morrison, a Partner at Adams Street who leads the firm’s global tech efforts, it’s very early to see tangible results. “It’s like someone has set off the starting gun on a massive global race, but this is a marathon, not a sprint – over time we’ll see clear leaders emerging as innovation journeys play out.” Those who fail to grasp AI’s importance will risk being leapfrogged, Morisson says.
Still, conversations with a range of private markets firms reveal many are in the pilot phase of implementation, experimenting with quick wins and efficiencies that can boost productivity. The higher pursuits of gleaning more intelligent insight through AI remain, largely, in the distance.
Most GPs agree the use cases of AI are numerous. Among the more commonly anticipated impacts is that it will level the competitive playing field – generative AI’s penchant for analytics at speed is expected to challenge the information asymmetry that forms the private equity competitive edge.
But if we’ve learnt anything from the very public, often comical and sometimes disturbing experimentations with AI this year, it’s that throwing data and AI into a pot and hoping for the best doesn’t always work out. Models need to learn, not only to avoid bias and false positives, but also to understand the user’s specific needs.
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