As artificial intelligence (AI) continues to evolve, private equity (PE) firms are facing a pivotal moment. Once viewed as a back-office efficiency tool, AI is now at the center of how value is created within portfolio companies. But enthusiasm alone doesn’t guarantee results, especially in the middle market, where execution remains a significant challenge.
This article explores how leading firms are deploying AI across their portfolios, the barriers they face, and what it will take to turn early wins into repeatable strategies.
A New Mandate: Embedding AI Into the Core of Portfolio Operations
PE firms are increasingly integrating AI into fundamental business operations—procurement, finance, customer service, marketing, and content creation. According to research from PitchBook, early adopters like Apollo Global Management and CVC Capital Partners are building centralized AI capabilities to streamline and scale initiatives across their holdings.
Rather than treating AI as a series of disconnected experiments, these firms are investing in reusable infrastructure. The result: measurable gains in cost reduction, risk mitigation, and decision-making.
Mindset Over Tools: Overcoming Leadership Skepticism
Despite high interest, execution challenges persist. Many portfolio company leaders still misunderstand or underestimate AI’s practical value. Some view it as an emerging technology not yet ready for business use. Others are wary of regulatory risk, data privacy concerns, or simply don’t have the technical background to deploy it effectively.
According to Tom Davenport, a research fellow with the MIT Initiative on the Digital Economy, one of the biggest barriers to adoption isn’t access to technology—it’s leadership mindset. “There’s still a belief that AI was invented when ChatGPT came out,” he notes. “But firms that treat AI as a side project are missing the point. The real value comes from embedding it into how a business operates.”
This is echoed by Alvarez & Marsal’s Amir Malik, who cautions that “AI is often seen as voodoo” by some middle-market CEOs. For this reason, some PE firms are now requiring experimentation as a condition for continued operational support.
Building a Central AI Playbook
Leading firms are formalizing their approach to AI by developing internal “centers of excellence,” central playbooks, and shared vendor ecosystems. Paris-based fund manager Eurazeo, for example, has created an AI accelerator integrated into its broader digital transformation strategy. The program provides portfolio companies with vetted tools, subsidized licenses, and structured guidance—yet participation is opt-in, preserving local ownership.
This approach emphasizes operational redesign rather than plug-and-play adoption. “If you don’t rethink your operating model,” says Eurazeo CIO Domitille Doat-Le Bigot, “you’ll just have AI agents writing newsletters. That’s not game-changing—that’s cosmetic.”
The most effective deployments take a pragmatic, hands-on approach: identifying repeatable use cases, helping teams reimagine workflows, and providing technical support to avoid false starts.
Risk, Governance, and the Missing Framework
For all the optimism around AI, risk remains a real concern. AI systems introduce new vulnerabilities—including hallucinated outputs, IP leakage, bias, and explainability issues. According to FTI Consulting, only about 20–25% of AI projects currently deliver meaningful returns, and very few PE firms have a structured governance model in place.
Davenport notes, “I haven’t come across a PE-firm-wide AI governance platform yet. There’s some awareness, but it’s mostly reactive.” As regulators increase scrutiny and AI disclosures become common in due diligence, the cost of weak oversight will rise.
Cybersecurity is another area of concern. AI can expand attack surfaces and introduce new failure modes—particularly when used to automate high-stakes functions like legal review or ESG reporting.
From Experimentation to Advantage
Despite growing pains, AI is poised to become a long-term driver of value in private equity. The firms that are ahead today share three key characteristics:
- They centralize AI capabilities across their platform to ensure scalability.
- They invest in education for portfolio company leaders—not just data scientists.
- They balance ambition with governance, building trust while managing risk.
Yet for many firms, especially those operating in the lower middle market, the path to transformation is still unclear.
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