AI for Private Equity: What Works, What Doesn’t and Why

A comprehensive survey of private equity firms reveals the industry’s current AI landscape: 98% of firms report being in the process of digital transformation, though only 7% have completed this journey. The momentum is clear: 75% of firms are either investing or planning to invest in AI within the next 12 months, and 59% now consider AI for private equity a key factor that surpasses traditional value drivers.

AI adoption across private equity, internal operations, deal sourcing and portfolio value creation remains uneven. Success stories presented at conferences are plenty – from a U.S. energy firm identifying 120+ investment opportunities using AI to a global media platform tripling its addressable market. However, many firms are discovering that meaningful AI implementation demands far more than purchasing software and hiring data scientists. Perhaps most telling is this stark reality: 36% of companies with AI strategies lack even basic Key Performance Indicators (KPIs) to measure their effectiveness.

The narrative on AI for private equity is growing more refined. Major firms highlight AI’s role in due diligence and decision-making but face talent shortages, infrastructure needs and data risks, with only 11% prioritizing these critical issues. AI capabilities like deal sourcing, financial clarity, accurate forecasting and risk identification are transforming operations, especially in deal origination. However, the integration of AI with current processes remains challenging.

This reality check isn’t about dismissing AI’s potential in private equity – it’s about understanding why even the most sophisticated firms are discovering that successful implementation requires a comprehensive transformation strategy. It’s about learning from both the successes and challenges as the industry undergoes this fundamental shift.

In this article, we’ll examine the critical factors driving successful AI implementation, the common pitfalls to avoid, and how PE firms can build a pragmatic path forward that prioritizes value creation. The lessons learned are both expensive and essential for any firm navigating the AI landscape in 2025 and beyond.

A mid-market PE firm managing a $1.5B fund encountered a common scenario in 2024: vendor pitches promising AI-powered financial analysis that would “revolutionize due diligence.” The demo was compelling – automated extraction and analysis of financial statements, promising 70% reduction in analysis time while surfacing insights human analysts might miss. The investment committee approved two pilots.

The system’s performance degraded significantly when faced with real-world deal flow. While it handled pristine financial statements well, it struggled with middle-market realities:

Most concerning was the footnote issue – the system consistently missed material information that would significantly impact valuations. Associates found themselves spending more time validating the AI’s output than they would have spent on traditional analysis. After three months, the firm had invested 1,200 associate hours in system supervision and correction and countless partner hours addressing delayed analysis concerns.

Rather than continue forcing a comprehensive solution, the firm stepped back and reassessed their actual workflow needs. They realized their existing Excel models contained years of accumulated industry expertise and deal analysis know-how. The real bottleneck wasn’t the analysis itself, but the manual work required to standardize input data.

They redirected their efforts toward solving the data input problem:

This focused approach cost roughly one half of the quoted original vendor solution while delivering immediate productivity gains. Most importantly, it preserved the firm’s analytical rigor while eliminating the most tedious aspects of deal analysis.

With standardized data now flowing into their Excel models, the firm is evaluating targeted improvements to their analytical capabilities:

This practical approach yielded what the original vendor solution promised – faster deal analysis – but did so by enhancing rather than replacing proven workflows. The firm’s deal teams now spend more time on value-adding analysis and less time on data preparation, achieving efficiency without sacrificing analytical quality.

The firms successfully implementing AI for private equity share three critical characteristics that most organizations overlook in their rush to adoption. Understanding these prerequisites reveals why even well-funded initiatives often fail to deliver promised returns.

For firms beginning their AI journey, success requires a methodical, foundations-first approach that prioritizes infrastructure over quick wins.

Remember: The goal isn’t to implement AI – it’s to improve decision-making and operational efficiency. Start with these concrete steps:

Focus on data fundamentals for at least a year before considering AI applications. This foundation-first approach dramatically improves the success rate of subsequent AI initiatives. Most importantly, it prevents the costly failure scenarios that have plagued rushed AI implementations across the industry.

Success in AI for private equity adoption isn’t about having the latest technology – it’s about having the organizational discipline to build proper foundations. Firms that internalize this reality and plan accordingly are the ones that will actually capture the value that AI vendors promise.

Withum brings a full suite of services to private equity firms’ investment life cycles with investment and operating professionals’ combined perspectives. You can learn more about our services and private equity team here.