Lee McCabe: Why AI for Private Equity Should Augment, Not Replace, Human Expertise

In a recent LinkedIn post, Lee McCabe challenges the prevalent approach to building “AI for Private Equity,” arguing that the industry’s needs are often misunderstood by technology providers. McCabe contends that the focus on developing complex software solutions overlooks the fundamental operational realities and decision-making processes that drive success in private equity.

The Misguided Pursuit of “AI for Private Equity”

McCabe highlights a common misconception among those developing AI solutions for the sector. He points out that many private equity firms continue to operate effectively with established tools like Outlook and Excel, suggesting that the perceived need for advanced software is not as widespread as some vendors believe. The core issue, according to McCabe, lies not in a lack of sophisticated dashboards or integrated systems, but in the quality of decisions made.

“PE isn’t starving for dashboards, copilots, or a 40-tab ‘deal OS.’ PE is starving for better decisions, and no software has ever replaced judgment, pattern recognition, or the ability to actually operate a company.”

This perspective underscores McCabe’s central argument: the real bottleneck in private equity is not data availability, but the capacity for interpretation and decisive action. He asserts that technology providers are often building tools to address problems that the industry itself doesn’t perceive as critical, missing the mark on what truly enhances performance.

Focusing on Interpretation and Action, Not Just Tools

McCabe elaborates on the distinction between data and its application, stating that the true value lies in understanding the data and knowing what to do with it. As he puts it:

“Because the real bottleneck isn’t data. It’s interpretation. It’s action. It’s people who know what to do next.”

This emphasis on human capital and expertise is central to McCabe’s analysis. While acknowledging that AI will undoubtedly transform certain functions such as sourcing, research, diligence, and portfolio analytics, he posits that the ultimate winners will be those who leverage AI to enhance, rather than supplant, human capabilities. The firms that thrive will be those that integrate AI in a way that sharpens the judgment and decision-making of their operators.

Leverage Over Software: The Real Need

McCabe advises technology builders to shift their focus from trying to recreate the entire private equity fund in software. Instead, he suggests understanding the enduring success of traditional tools and skilled operators. For decades, firms have generated top-quartile returns using a combination of basic software and experienced personnel.

“If you want to build something for private equity, stop trying to reinvent the fund in software. Start by understanding why Outlook, Excel, and a handful of sharp operators have been enough to generate top-quartile returns for 30 years.”

Ultimately, McCabe argues that private equity firms are not in need of more tools, but of leverage. He believes that AI can provide this crucial leverage, but not in the manner typically envisioned by many in the tech space. The key lies in using AI to amplify the effectiveness of human expertise, enabling better decisions and more impactful actions, rather than aiming to automate the core decision-making processes.

“PE doesn’t need more tools. PE needs leverage. AI can provide it, just not in the way most people think.”

McCabe concludes by urging a more nuanced understanding of the private equity landscape, advocating for solutions that empower human judgment and operational expertise, amplified by intelligent technology, rather than attempting to replace it.