AI for Private Equity

Nate Buchanan, Director, Pathfindr

Occasionally at The Path, we like to take a break from our regular, Pultizer-worthy content to write a deep dive on how AI can make a difference in a particular industry. This week we’re focusing on private equity and how GPs and their management teams can use AI to manage risk, optimize performance, and seize opportunities that others might miss.

We begin - as we often do - with a brief history lesson.

2023 was a challenging year for private equity around the globe. Capital deployment and exit values dropped nearly 26% in the US compared to 2022, while investments in Asia-Pacific tumbled 30% YoY to a 5-year low.

As shown above, 2023’s results continued a broader decline in APAC private market activity since the heady days of record-low interest rates during and immediately after the pandemic. The slowdown can be traced to a wide range of factors, among them:

High, persistent inflation - and the increased interest rates used to combat it

War in Eastern Europe and the Middle East

Broad uncertainty about the global economic outlook

These elements combined to produce conditions that led to 2023 closing with firms sitting on record levels of dry powder - close to USD 2.6 trillion. However, an industry consensus is forming that 2024 will see increasingly stable interest rates and a relaxation of monetary policy, which will lead GPs to move quickly to deploy capital and capture returns this year that were deferred last year.

How does AI fit into all this? With the wide availability of low-cost LLMs in the market today, private equity firms have an incredible opportunity to use AI to supercharge their internal processes, as well as increase revenue and reduce costs across their PortCos. A savvy implementation strategy can be the difference between modest gains (or losses) and record returns in 2024.

Consider three core activities a private equity firm typically engages in: opportunity management, deal execution, and portfolio oversight. AI can make a measurable difference in all three - quickly.

Opportunity Management

• What’s the Goal? - to identify potential targets that fit the firm’s investment strategy, develop relationships with their leadership teams, and manage them through to executing a deal
• What Challenges Exist? - in a competitive environment the same firms are often competing for the same targets, creating impactful outreach for multiple companies often requires significant manual effort, and gathering reliable data on key metrics such as revenue or number of employees can be particularly difficult for private companies

How Can AI Help?

• Manage data - crawl the web to find the most recent data on key metrics for opportunities
• Track market activity - identify trends and red flags that should inform investment decisions
• Generate outreach - create relevant, meaningful emails or other communications designed to build relationships with founders

Deal Execution

• What’s the Goal? - to perform due diligence on a target company, determine if the deal is worth pursuing, and negotiating it to close on favorable terms for both parties
• What Challenges Exist? - analysis of financial statements and related documentation can be extremely time-consuming, as can drafting the required memos, presentations, and other paperwork associated with closing a deal

How Can AI Help?

• Analyze financials - evaluate financial documents against the firm’s investment criteria to ensure alignment
• Assess risk - identify red flags in provided documents as compared to broader industry or market intelligence
• Generate documents - create inputs needed to execute the deal such as LOIs and PIMs

Portfolio Oversight

• What’s the Goal? - to facilitate a target’s transition into the portfolio, support their leadership team, and achieve the firm’s investment goals such as increasing EBITDA
• What Challenges Exist? - the target may not be accustomed to operating with external oversight, may have highly manual or repetitive processes, or may not be in a favorable position to capture new revenue opportunities

How Can AI Help?

• Reduce costs - eliminate manual work, automate complex or repetitive tasks, and increase team member productivity
• Increase revenue - identify new opportunities and improve existing offerings
• Create new products - introduce new capabilities that can be used to launch a new product or service

As outlined above, AI offers myriad opportunities for innovative private equity firms to distinguish themselves from their competition and create capabilities that will enable them to unlock untold value with minimal downside risk.

However, there is another way that AI could be deployed that might not be immediately apparent within the industry. Private equity firms could establish an AI innovation capability in-house and offer it as a service to PortCos.

This may be a departure from how many firms engage with their portfolio today, but the unique opportunity that AI offers makes it worth considering. There are significant potential benefits, including:

Leading with AI in every investment - because AI is impacting all industries that a firm is likely to invest in, having the ability to turn any partner into an AI-led platform, product, or service will materially increase the return from a future exit

Making the fund more attractive to partners and investors - most industry leaders recognize the massive potential of AI, and firms without an “all-in” approach risk losing out to competitors who are fully embracing the technology

Maximizing ROI by using the same capabilities in-house - many of the skills and tools that a firm would need to assist PortCos would apply to internal use cases as well, offsetting capability build costs through savings in multiple areas

Introducing an operating model for in-house AI capability development similar to the one shown above would be one way to put some structure around the process of use case identification and experimentation without sacrificing the benefits of rapid prototyping. Being able to put working solutions in your teams’ hands very soon after they’ve had an idea is critical to learning what’s going to add value and what needs to be discarded.

We’ve covered a lot of ground this week - what does all this mean for private equity leaders who are looking to get started with AI? Let’s conclude with four takeaways that might just inspire you to act:

It’s Not Too Late - there are a few global firms who have experimented with AI, but at-scale implementations are exceedingly rare…in other words, you haven’t been left behind (yet)

The Time is Now - to maximize your opportunity, start implementing “no regrets” use cases for back office productivity and actively experimenting with AI-powered efficiency plays in your portfolio

The Market is Wide Open - the AI ecosystem might be dominated by the likes of OpenAI and hyperscalers like Microsoft, but a killer consumer-grade use case has yet to emerge

There is Serious Value Out There - whether you’re looking at AI as a potential investment or as an accelerator for internal operations and portfolio value, you’re almost certain to find use cases that can make a big difference in the near term