This "AI is revolutionizing..." thing has become a familiar refrain across industries, the journey from artificial intelligence research to practical implementation in private equity (PE) presents unique challenges. Integrating AI into high-stakes PE workflows, from sourcing deals to conducting due diligence and managing portfolios, requires more than just cutting-edge technology. In an industry built on expertise and trust, many investment professionals remain wary of cutting-edge black-box models guiding million-dollar decisions. This article explores the complex barriers between AI's theoretical potential and its practical application in PE, examining integration hurdles, trust issues, and real-world lessons from early adopters. We'll also investigate how bridging this gap requires a two-sided evolution: AI researchers developing more practical, interpretable solutions while PE firms build stronger technological foundations through investments in data infrastructure, talent, and cultural change.
PE firms handle a spectrum of activities – identifying investments, vetting targets, improving portfolio companies, and assessing risks. AI can theoretically enhance each of these, but incorporating them into existing processes is (still) far from plug-and-play. Key areas include:
| AI Use Case | How AI Helps | Challenges |
| Deal Sourcing | AI models scan financial databases, news, and social media to identify promising investment targets and emerging sector trends. | Integrating AI insights with traditional relationship-driven sourcing. Requires vast, high-quality data and seamless integration. |
| Due Diligence | AI quickly analyzes financials, legal documents, and customer data, accelerating due diligence by summarizing large datasets. | Trust and accuracy concerns—ensuring AI insights do not miss critical deal-specific nuances. |
| Portfolio Management | AI optimizes pricing, finds cost savings, predicts maintenance needs, and provides analytics to flag potential operational inefficiencies. | Each portfolio company has different systems and data governance practices; AI adoption requires significant training and data standardization. |
| Risk Assessment | AI assists in risk modeling, scenario analysis, and monitoring real-time data to alert investors of emerging risks. | Private market risks are often rare, qualitative, and difficult to quantify, making AI predictions less reliable for black swan events. |
Finding promising companies to buy is labor-intensive. AI models can scan financial databases, news, and even social media to spot hidden gems or sector trends that human partners might miss.
Challenge: Integrating AI insights with traditional relationship-driven sourcing. Senior partners may be skeptical if an algorithm flags a target that isn’t already on their radar. Moreover, effective AI requires vast, high-quality data, and seamless development and integration.
Before acquiring a company, PE investors pour over financials, legal documents, customer data, and more. AI can turbocharge this process by quickly analyzing documents and datasets. Generative AI tools are already scanning “reams of data in a fraction of the time” it takes humans.
Challenge: Trust and accuracy. An AI might summarize patterns, but investment committees need to trust that nothing critical was missed. Each deal is unique, and context matters – something even the most advanced AI agents can overlook without human guidance. Firms are starting to bake AI into standard diligence (e.g. using scorecards to assess AI-related opportunities and risks), but making these analyses as routine as traditional legal or financial diligence will take
After an acquisition, PE firms work to improve the company’s performance. Here AI can help optimize pricing, find cost savings, or predict maintenance needs. Advanced analytics can flag early warning signs in KPIs or suggest operational tweaks to boost efficiency. In practice, some PE firms now deploy AI specialists to work with portfolio companies on projects like customer segmentation or supply chain optimization.
Challenge: Each portfolio business has its own systems and culture. Bringing cutting-edge AI into a mid-sized manufacturing firm, for example, means dealing with messy data and retraining staff to use new tools. Data governance is critical – many institutions admit to weak data practices, and only 18% of front-office teams at investment firsms say they can get data they need without manual intervention. Needless to say, without clean, integrated data, even the best models fall flat.
PE deals entail significant risk – from market swings to a target company underperforming. AI models can aid in risk modeling by analyzing scenarios or simulating how macroeconomic changes might impact a portfolio. AI can also monitor real-time data (news, social sentiment, etc.) to alert investors to emerging risks.
Challenge: Many risk factors in private markets are rare or qualitative (e.g. a sudden regulatory change or a CEO’s health) and hard to quantify. AI predictions are only as good as their training data, and in finance black swan events are, by definition, very difficult to spot in advance.
Integrating AI into these workflows is complex and requires deep expertise and change management. Research by McKinsey noted that many companies’ early attempts at AI and advanced analytics in business “failed… because it’s hard” – success demands not just technology, but also organizational change, patience for ROI, the right set of skills and strong data governance. In private equity, inserting AI into the mix means redesigning workflows and convincing humans to adapt. This complexity helps explain why, despite the potential, adoption has been cautious.
From what we've seen, trust seems to be one of the biggest barriers to AI adoption in PE. Even more for cutting-edge research. High-stakes decisions and reputation rests on delivering returns to investors. It’s no surprise that professionals are wary of delegating decisions to AI without clear evidence and transparency. This survey by KPMG shows nearly three in five people are wary of trusting AI systems, and about 67% report only low to moderate acceptance of AI in general. In the PE boardroom, this wariness is amplified: an algorithm might analyze faster, but can it be trusted to capture the nuance of a deal?
A core issue is that recent advancements in AI are often “black box” models. Complex machine learning algorithms that generally lack full explainability – they can output a recommendation but not a clear understandable rationale that humans can follow or relate to. In a domain like finance, if an AI suggests passing on an investment that seasoned partners favor (or vice versa), it will likely be challenged to “show its work.” Without explainability, investment committees default to human judgment, which they understand and can justify to their own investors.
There’s also the matter of accountability and risk. If a deal goes wrong, fund managers must answer to their limited partners. Relying on AI doesn’t absolve them of blame. This incentivizes cautious use: AI might be an advisor, but rarely the final word. Building trust in AI tools takes time – often by starting with low-risk tasks. For instance, a firm might first use AI to automate mundane data gathering or to double-check models, gradually gaining confidence in the tool’s reliability.
Industry experts highlight that trust must also extend to AI vendors. According to Bain & Company’s Richard Lichtenstein, a “lack of trustworthy vendor solutions” is a challenge – few off-the-shelf AI products have proven they can consistently deliver on PE use cases. Apparently, many funds have tried third-party analytics platforms that promised to revolutionize sourcing or forecasting, only to find the results underwhelming or not fully credible to deal teams. This has made some PE investors skeptical of vendors’ hype.
To overcome these trust issues, PE firms are increasingly demanding explainable and auditable AI. They want models where assumptions can be examined and stress-tested. A potential strategy to achieve that is developing in-house AI capabilities, so they can retain full control and understanding of the algorithms. Others are focusing on “glass box” AI (simpler models or interpretable approaches) for critical decisions, reserving black-box models for auxiliary analysis.
Despite rapid advances in AI research, a gap remains between cutting-edge models and practical use in PE. In theory, today’s AI – with state-of-the-art predictive analytics and massive language models – could transform how investment decisions are made. In practice, adoption has been slow and uneven. Many PE firms are still “on the digital sidelines”, lagging behind other financial sectors in AI uptake. A recent global benchmarking study by McKinsey found less than 20% of institutional investors feel “ready” to incorporate AI and other future technologies into their processes. Another industry poll by the World Economic Forum revealed that only 2% of PE firms expect to realize significant AI-driven value by 2025, even though 93% anticipate at least moderate benefits from AI in the long run. In other words, nearly everyone believes AI is important, but almost no one thinks they’re getting major value from it today.
Why the disconnect? One reason is that cutting-edge AI often doesn’t fit neatly into real-world constraints. Academic researchers might develop a novel machine learning model that predicts company defaults or market shifts with impressive accuracy on historical data. But a PE firm trying to implement that model faces hurdles: Do we have the required data (and rights to use it)? Does the model output make sense to our decision-makers? How do we integrate it into our due diligence workflow or investment committee reviews? The latest research often assumes ideal conditions – complete datasets, clear objective functions – which rarely exist in the messy reality of private markets. Moreover, PE firms are typically lean operations when it comes to tech. Unlike big banks, a PE fund might not have a large IT and analytics division. Lichtenstein notes that even “large” PE funds might have just 1–3 data scientists for the whole firm. This limited in-house expertise means there’s a capacity gap – few people to interpret state-of-art academic AI research or rebuild it for the firm’s needs. PE investors can’t “throw an army of engineers” at the problem of customizing AI tools, so the latest innovations often remain stuck in papers and prototypes. It’s telling that most PE funds are only experimenting at a small scale. McKinsey found that while about 60% of PE portfolio companies had at least a pilot in generative AI, only ~5% had scaled any AI initiative to production across the business
The gap is also evident in outcomes versus expectations. Private investors widely agree AI will be transformative, yet current deal outcomes rarely cite “AI” as a differentiator. For example, Bain's recent survey of PE investors showed 83% wanted to see companies they acquire be ‘AI data ready’, but only 40% of evaluated targets met that bar. Similarly, three-quarters of investors wanted targets to have a bold AI strategy, but only one-third of companies delivered on that. This suggests PE firms are hungry for AI advantages, but the market hasn’t caught up – many companies (and PE internal teams) are still at the starting line of the AI race.
Bridging this gap will require convergence from both sides: AI researchers focusing more on practical, interpretable solutions, and PE practitioners becoming more tech-savvy. Encouragingly, some PE firms are investing in the foundation – data infrastructure, talent, and cultural change – to pave the way for AI. Leading investors “treat data as a strategic asset” and build robust data lakes and governance so that AI models have reliable fuel. They also embed tech specialists alongside deal professionals, fostering collaboration between data scientists and traditional investors. As PE firms strengthen these foundations, the impressive capabilities seen in AI research can more readily translate to real investment gains.