In our previous article, we made the case that the returns that private capital firms can generate from AI investments go beyond those of standard technology adoption. However, the window to generate a sustained competitive advantage through returns on AI (“R-AI”, as we call it), is closing. As AI software becomes table stakes for more and more firms, those who move quickly and find unique ways to infuse it into core workflows or portfolio operations are going to reap the biggest rewards.

Use cases can broadly be classified into two categories:

1. Portfolio company operations: How is AI going to affect the way companies grow, operate and transform? This includes looking at industry-wide risks or opportunities to understand how the context has shifted in a world where AI is now ubiquitous.
2. Intra-firm operations: How can AI be used to make investors and deal teams smarter, faster, better at investment analysis and decision-making?

Today, we believe the latter offers the more immediate opportunity. While “internal tooling” use cases may not sound as interesting as the many possibilities across industries, once you dive in, it becomes clear that this is where a lot of the magic happens. Evaluating and executing on investment decisions are where the core competencies of most private equity and growth capital firms lie, and AI can most immediately be applied to enhancing those capabilities.

Portfolio company operations are, of course, a large area of opportunity, but the upside is much more variable across firms, more difficult to scale, and often requires more R&D, specialized talent and longer time horizons to deploy. The use cases are more disparate, industry-specific and often outside of the scope of "fintech" or other easy-to-use software.

To understand where the target areas within an investment firm may lie, it is useful to use the standard processes of a mid-sized PE firm as a roadmap. From fundraising through due diligence, the goal of deploying AI-driven processes is not to replace deal teams with software, but to actually make investors more effective at what they already do best.

Underpinning most firms' financial success is the ability to fundraise and source deals, and we have already seen a multitude of CRM tools and data pipelines enabling the infusion of AI as a means of creating better matches between investors and prospects.

Still, deal teams, especially analysts, spend significant time cleaning data and managing firm-specific processes that often boil down to collecting the right data points and placing them in the correct cell or slide. With the overarching promise of AI to reduce mundane work and free up time for higher value activity, these workflows stand out as key areas to enable investors to become more efficient, and hence, allocate time and capital to more impactful activities.

Perhaps above all, due diligence is a key area of impact, in which deal teams today may be spending upwards of 500 people-hours on a deal from start to finish. Even at a quick glance, AI offers several compelling potential benefits:

• Automating repetitive tasks: AI-powered tools can handle tasks like document review, due diligence analysis, and portfolio monitoring, freeing up valuable human resources for more strategic endeavors.
• Enhancing data-driven decision-making: Algorithms can analyze massive datasets, uncovering hidden patterns and generating insights that might be missed by human analysts, leading to more informed investment decisions.
• Democratizing access to information: Tools can bridge the information gap by providing investors with comprehensive data and analysis, fostering a more level playing field.
• Uncovering hidden gems: AI can identify promising investment opportunities beyond the traditional scope, potentially leading to the discovery of hidden alpha.

Our team at Keye has spoken to over 350 investors, and it is clear that generative AI is fundamentally transforming due diligence. Tools are emerging that can rapidly analyze vast amounts of data, and provide a more comprehensive picture of a target company's prospects. For instance, advanced algorithms can synthesize and present key findings from thousands of customer reviews or interview transcripts within minutes.

This newfound ability to process unstructured data empowers deal teams to cast a wider net for information. Consequently, the most talented investors can dedicate more time to critical activities: developing insightful interpretations and rigorously testing investment hypotheses. By pinpointing the most relevant market research and competitive analysis, generative AI acts as a compass, guiding deal teams towards the information that truly underwrites successful opportunities.

Adoption is already happening, and the window for firms who seek to use it as an advantage is closing fast. According to Roberto Prioreschi at Bain & Co., generative AI use for M&A deal processes is low at 16% today, but it is expected to reach close to 80% over the next three years. Today’s investing leaders should not be thinking about whether, but how to implement AI, and who to partner with to do so.

In our next article, we will cover the blossoming landscape of generative AI applications in private finance, and how to navigate it. Along with it, we will be publishing our first "AI in Private Finance" market map. See you there.