Private markets managers step up AI adoption but pace varies

Article May 26, 2026

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BOTTOM LINE UPFRONT

Investors exploring partnerships with AI giants, developing or acquiring tools
Origination, diligence, financial modeling are prevalent use cases at GP level
Portfolio companies, LPs are slower adopters, recognize need to accelerate

Large private markets managers have been positioning themselves to capitalize on artificial intelligence (AI) for over a decade, often appointing chief AI officers or dedicated operating partners to streamline workflows.

Blackstone was an early adopter. The alternative asset manager kicked off its data science efforts in 2015 and today counts over 50 full-time specialists across its technology and innovations, data science and portfolio technology optimization functions.

“This effort stemmed from a belief that AI can enhance private equity dealmaking, making it easier for analysts to review large quantities of data when putting together an investment thesis,” Chief Technology Officer John Stecher told Mergermarket. “More broadly, we think AI has the potential to make our employees more productive and help them make better informed investment decisions.”

Blackstone is reportedly one of several large asset managers discussing an AI consulting venture with Anthropic that would see the likes of Claude rolled out across portfolio companies. Meanwhile, TPG, Advent International, Bain Capital, and Brookfield Asset Management are exploring a similar arrangement with OpenAI, according to a recent Reuters report.

Just last week, Thoma Bravo announced a strategic partnership with Google Cloud intended to accelerate AI implementation. Orlando Bravo, a co-founder and managing partner at the firm, said in a statement that portfolio companies are now positioned to “build the next generation of AI solutions” across nearly every enterprise process and industry.

He reeled off a list of impacted industries or functions – including human capital management, interconnected planning, procurement, aviation, manufacturing, financial services, healthcare, and real estate – as well as referencing “almost every segment across cybersecurity solutions.”

The ventures are part of a broader trend whereby GPs and LPs, irrespective of size, are intensifying efforts to leverage AI. The pressure to act is becoming ever more acute as AI models rapidly advance.

“We’re at an early stage in this journey, and it’s evolving so quickly that it scares me and excites me,” said the CIO of one US-based endowment.

Sourcing and screening

At the GP level, some of the biggest AI gains have come in deal origination, diligence and financial modeling. This includes using the technology to scour filings and news articles for leads on potential deals, as well as drafting pitch letters to business prospects with specific information about why their firm would be a good fit. Offer letters are also being churned out.

Hebbia, an AI platform designed to serve the financial services community, offers a virtual data room product that ingests all deal-related files and screens opportunities for risks while assessing whether they meet specific investment criteria, said George Sivulka, the firm’s founder and CEO. AI tools are also used in quality-of-earnings analysis and marking portfolio assets to market.

An executive at one private credit investment firm described how an analyst built a full collateralized loan obligation (CLO)-to-cash-flow model from scratch in two hours using Claude’s Opus model. “That normally takes days for anyone to do,” the executive said.

Most implementation today remains focused on efficiency rather than enterprise-wide transformation.

Thomas T. Thomas, CEO of Teragonia, a Chicago-based provider of AI applications for PE-backed clients, cites EQT’s Motherbrain sourcing and origination intelligence platform and BlackRock’s Aladdin system as examples of proprietary or semi-proprietary tools supporting investment, risk and operations. “A lot tend to be a little secretive,” he said.

Some mid-market GPs also have proprietary tools. Boston-based Ethos Capital, for instance, developed Petra for sourcing, due diligence, and portfolio management, as previously reported by Mergermarket. One use case is meeting preparation: the tool can swiftly deliver information such as real-time data that would take a human considerable time and effort to pull together.

Other investors are acquiring AI capabilities as evidenced by Southfield Capital’s recent purchase of Contextual.ai. Andy Levisson, founder and managing partner of the lower middle market-focused GP, told Mergermarket that his team had been using Contextual.ai internally and in portfolio companies for six years.

Slow starters

AI adoption starting at the GP level and spreading across the portfolio is a common theme. Nick Leopard, CEO of Accordion, a financial consulting firm focused on private equity, points to increasing use of AI by portfolio company CFOs.

We’ve been working with a number of sponsors on GP-specific AI solutions, whether that’s taking portfolio company data and quickly synthesizing it to generate insights for internal reporting, or using it for deal-committee use cases.

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Still, for many portfolio companies, these efforts are relatively nascent. Teragonia’s Thomas early areas of focus include procurement, invoice processing and document management, as well as customer-facing processes in certain industries.

LPs also recognize the need to step up the pace of implementation. “We have to use this to make us smarter and think deeper, not just lazier,” the CIO of the US-based endowment said.

The CIO pointed to a recent practical example where a team member had used Claude for Excel to replicate an internal model for running scenario analyses. Another endowment CIO outlined how their organization had formed a cross-functional team to develop AI policies and run pilot exercises, with several tools earmarked for near-term deployment.

Others are not quite there yet. A senior director at one public pension fund admitted they have only just begun exploring AI applications. These include using AI in quantitative analysis to determine where returns have historically been generated and whether those drivers are sustainable.

“I would say we’ve just stepped up to the plate – the first pitch hasn’t even come through yet – but it is something we’re thinking about,” the senior director said.

A senior investment professional at another state pension fund added that political and governance concerns can also get in the way. Freedom of Information Act rules and getting elected officials comfortable with AI processes were cited as obstacles.

Risks and resistance

These issues highlight the importance of actively managing AI-related risk at many institutions. Thomas of Teragonia observed that data privacy and cybersecurity concerns initially contributed to hesitance in using AI for document processing and other sensitive workflows, though these have to some extent been eased by safeguards.

Blackstone’s Stecher drew parallels with the challenges presented by any emerging technology. On AI specifically, the firm’s mitigation efforts include centralized data governance, robust cybersecurity and legal and compliance protocols, responsible AI frameworks, and significant investment in technical talent and partnerships.

At the same time, some market participants believe those who hesitate – perhaps delaying expenditure on necessary supporting infrastructure – are exposing themselves to different risks.

“Inertia is real,” said an executive at another private credit firm. “There will be people who don’t want to participate, and it will be game over for them. But for those who are willing, this is the most exciting thing I’ve seen in 37 years.”

FAQ

How are large private equity firms positioning themselves around AI?

The largest alternative asset managers have been building AI capabilities for years, not months. Blackstone launched its data science function in 2015 and today operates more than 50 full-time specialists across technology, data science, and portfolio optimization. The current wave of activity reflects a maturation of that groundwork — firms like TPG, Advent International, Bain Capital, Brookfield, and Thoma Bravo are now formalizing partnerships directly with AI providers including Anthropic, OpenAI, and Google Cloud to accelerate deployment both internally and across portfolio companies.

What are GPs using AI for today?

The most prevalent use cases at the GP level are deal origination, due diligence, and financial modeling. AI is being used to screen filings and news for investment leads, draft targeted pitch letters, conduct quality-of-earnings analysis, and mark portfolio assets to market. Virtual data room platforms like Hebbia can ingest all deal-related files and screen opportunities against specific investment criteria in a fraction of the time previously required. In one documented example, an analyst at a private credit firm built a full CLO-to-cash-flow model from scratch in two hours using Claude’s Opus model — a task that would typically take days.

How is AI being used at the portfolio company level?

Adoption at the portfolio company level is growing but remains earlier-stage than at the GP level. Common entry points include procurement, invoice processing, document management, and customer-facing workflows in select industries. CFOs at PE-backed companies are increasingly being brought into GP-led AI initiatives — for example, using AI to synthesize portfolio company data for internal reporting or to support deal committee workflows. Accordion works with a number of sponsors on exactly these kinds of GP-specific AI solutions.

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