Last year was not an especially good one for the private equity (PE) space.
As Bloomberg News reported Monday (Feb. 23), PE firms returned fewer profits to their investors for the fourth consecutive year, with the industry sitting on $3.8 trillion in unsold assets, and struggling to raise cash for new funds.
Distributions as a percentage of net asset value remained at 14% for 2025, which was the second-lowest level since the 2008 financial crisis, the report said, citing data from Bain & Co. In addition, this rut has persisted for longer than what PE firms faced 17 years ago.
The value of deals last year climbed 44% from 2024 to $904 billion, though it had little effect on the industry’s “dry powder,” or money on hand to invest. Total transactions were down 6%.
“Not everybody is feeling like it was a great year,” Rebecca Burack, head of global private practice at Bain, said in an interview with Bloomberg
The uncertainty stemming from President Donald Trump’s “Liberation Day” tariffs halted dealmaking that seemed like “it was gangbusters” as recently as last January, she added.
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However, Burack argued that PE is still a strong investment, offering a level of diversification no longer found in public markets.
“It’s just a little stuck,” she said.
Elsewhere in the PE space, PYMNTS wrote late last year about the industry’s efforts to test whether artificial intelligence (AI) can compress diligence, sharpen forecasting and surface risks long before they appear in earnings. The shift indicates AI is moving from an experiment to more of an operational requirement.
The shift is most apparent in the earliest phases of investment work. BayPine, a PE outfit based in Boston, has started to weave AI into its investment and operating workflows.
“The immediate use case for AI application is across areas with large amounts of repetitive tasks that are largely hand-done or highly exposed to software engineering,” Tim Kiely, head of data, analytics and AI at BayPine, said in an interview with PYMNTS.
He pointed to revenue cycle management in healthcare as an early example, calling it “highly manual, highly repetitive, super value-add.”
In addition, AI gives firms earlier insights into performance risks. Models can identify signs of customer churn, margin pressure or supply-chain strain as soon as raw operational data makes its way into internal systems, long before those signals show up on quarterly financials.
“For firms focused on rapid value creation, that speed is becoming a differentiator,” PYMNTS wrote.
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