Private equity’s long-standing formula of leverage and operational fixes is meeting a new force multiplier. As deal cycles tighten and competition for assets intensifies, firms are turning to artificial intelligence not as a curiosity but as a core component of value creation.
The industry is testing whether AI can compress diligence, sharpen forecasting and surface risks long before they show up in earnings. The shift signals that AI is becoming less of an experiment and more of an operational requirement.
That shift is most visible in the earliest stages of investment work. BayPine, a Boston-based PE firm, has begun embedding AI throughout its investment and operating workflows, per a recent Axios report. Analysts and associates now use AI copilots to structure diligence plans, draft early deal memos and run first-pass market scans before manual reviews begin. The goal is to shorten the discovery phase without weakening rigor.
“Underwriting value creation from data and AI at the outset significantly increases the likelihood of successful implementation during the ownership period,” Cory A. Eaves, partner and head of Portfolio Operations at BayPine, said in a June Private Markets Insights report.
Other firms are moving quickly to systematize that approach. Charlesbank Capital Partners, which manages more than $22 billion, told Axios it built an AI stack anchored by ChatGPT Enterprise, Microsoft Copilot and Blueflame AI. General-purpose copilots manage drafting and reasoning, while finance-specific tools automate CRM updates, model templates and deal-flow pipelines.
Brightstar Capital Partners, with roughly $5 billion in assets, has created dozens of internal AI agents that review CIMs, map markets and draft initial memos. Brightstar CEO Andrew Weinberg said in the Axios report that a CIM review that once took hours now takes minutes. Ethos Capital, which oversees about $3.1 billion, built an internal platform called Petra that pulls data from email, Slack, public filings and internal systems. Executives said Petra now produces a company analysis in about 15 minutes, a task that once took weeks.
These moves point to the same conclusion: deal teams are no longer waiting for artificial intelligence to mature. They are using it to shrink timelines, standardize early analysis and elevate human review to the final stages rather than the initial ones.
Portfolio Work Shifts to Continuous, Data-Driven Cycles
As AI expands beyond sourcing and diligence, portfolio oversight is shifting from periodic reviews to continuous analysis. Harvard Business Review reports that firms are using AI to run real-time simulations of pricing, demand and cost structures, allowing partners to spot operational issues earlier and intervene faster. Instead of relying on backward-looking reports, operating teams can monitor trends as they develop.
“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, told PYMNTS. He cited revenue cycle management in healthcare as an early example, calling it “highly manual, highly repetitive, super value-add.”
Artificial intelligence also gives firms earlier visibility into performance risks. Models can detect signs of customer churn, margin pressure or supply-chain strain as soon as raw operational data enters internal systems, well before those signals appear in quarterly financials. For firms focused on rapid value creation, that speed is becoming a differentiator.
Reality Check
AI is reshaping dealmaking, but results still depend on people, processes and disciplined implementation. Large language models require close human supervision because output quality hinges on data clarity and prompt design. Early pilots in several firms stalled when teams realized AI struggled with financial nuance without strict guardrails. As Wells Fargo EVP Kunal Madhok noted, “A lot of studies talk about plug-and-play POCs. That does not generate ROI. The big unlock is rethinking how you do things with the tools.”
Scaling also introduces compliance friction. Security reviews, model-risk assessments and procurement checks add steps that firms cannot bypass. JPMorgan Chief Data and Analytics Officer Teresa Heitsenrether emphasized the stakes, telling Axios that “data protection is job No. 1, and centralized capabilities let us bake controls into the ecosystem.”
For private equity, the opportunity is clear. The challenge is ensuring the technology enhances judgment rather than replaces it and that the systems built today can withstand scrutiny as artificial intelligence becomes integral to the value-creation playbook.
Source: https://www.pymnts.com/
