Artificial Intelligence in Private Equity: An Opportunity?

While certain hedge fund managers have long been utilizing some form of artificial intelligence (AI) to aid with their investment processes, the potential applications of AI have appeared less obvious in the private equity (PE) space. However, as the PE industry continues to grow and become more competitive, it seems that more and more PE firms are turning to AI as a means of gaining an advantage over their competitors. PE firms have utilized AI to simplify some traditionally inefficient processes, enhance their due diligence practices and improve the overall quality of their investment analysis. This post will examine some of the ways in which a PE firm may be able to effectively implement AI to achieve a competitive advantage in today’s market.

Implementing AI within the context of a PE firm can help streamline research and other data-driven processes, thus lowering costs and increasing efficiency. For example, due diligence, the hiring of personnel and deal sourcing can all benefit from the implementation of AI, as well as the monitoring of portfolio companies and the development of accurate macro insights. AI can also be utilized to optimize PE portfolios, facilitate accurate market forecasts and track the daily operations of the PE firm.  

The reason AI can be leveraged to enhance all of these functions lies in its ability to process vast amounts of data with astonishing speed. The information that AI machines can analyze can range from corporate and accounting reports, to news and market data, to resumes and social media. The quantity of data and the speed with which the data is reviewed can have a significant impact on operational and economic performance alike. Having millions of data points that can be taken into account via an AI process can help yield better insights and better financial models, removing human biases from the process. This may help PE firms move ahead of their competitors by, for example, identifying attractive investments more quickly.

AI can also help optimize PE portfolios by creating sophisticated, dynamic forecasting models. PE firms can utilize AI to analyze historical precedents in order to create an assortment of potential forecast possibilities and predict future trends. These models can help mitigate risk and balance out portfolios.

In addition, AI can help alleviate risk on the cybersecurity front by being deployed to detect, analyze and respond to cyberattacks. Depending on the sophistication of the technology, AI may even be able to remediate certain cybersecurity threats automatically without any human involvement.

Aside from potentially enhancing the aforementioned processes of PE firms, AI could also be leveraged to improve investor relations. Many investors would value the increased transparency resulting from the availability of real-time data on portfolio companies and their industries, which currently few PE sponsors are able to offer efficiently. AI can be utilized to track such data. In addition, AI processes can use various data points to facilitate the identification of new investors, thereby facilitating fundraising efforts. And, of course, PE firms can further utilize AI to enhance the operations of their portfolio companies, thus compounding the value of their AI systems.

But while implementing AI can be beneficial in many ways, building out and transitioning to such internal AI processes may be a difficult endeavor. AI specialists, such as engineers and data scientists, are often difficult to recruit, as demand for their skills is high. Training non-technical employees to adopt AI processes can be costly and time-consuming. And firms will need to devote resources to maintain the security of any such AI processes from cyberattacks.  There are, however, third-party AI technologies that PE sponsors can utilize more immediately. For example, ChatGPT, the groundbreaking AI chatbot developed by OpenAI, might be leveraged by PE sponsors in various ways to optimize operations, and perhaps to enhance the investment selection process, so long as the PE sponsor ensures that no private or confidential data is revealed as a result. At its most basic level, chatbots based on the GPT language models could be used to automate certain interactions with current and potential investors, and to automatically generate certain work product such as presentations and reports. Firms may also want to leverage the technology in order to better and more quickly understand a particular sector or a potential portfolio company.

In addition, with the advent of BloombergGPT and JPMorgan’s IndexGPT, we are already seeing GPT models being implemented specifically in the financial services industry. These applications are likely only the beginning of what GPT technology can offer. As the technology further evolves, the use cases of GPT-based AI in the PE sector will expand and crystalize.

If implemented effectively, the benefits of AI could be substantial and provide smaller managers a disruptive edge in what is a highly competitive and consolidating industry. Moreover, as AI becomes more and more accessible and thus prevalent in the industry, it may become imperative for PE sponsors of the not-too-distant future to utilize AI to some significant extent in order to remain competitive.

With all of that said, relying on AI brings with it a new set of legal considerations that firms should be prepared for, such as protecting the firm’s intellectual property rights and trade secrets in connection with its proprietary AI processes. The use of AI has also been under increasing scrutiny by the SEC,^[1] and we expect this trend to continue. With our experience ranging from private funds to intellectual property to employment matters and beyond, Kramer Levin is well positioned to address all of the legal needs and concerns of its clients as the use of AI continues to expand in the PE space. 

^[1] _{For example, earlier this year the SEC announced proposed rules under the Securities Exchange Act of 1934 and the Investment Advisers Act of 1940 targeted at eliminating potential conflicts of interest associated with broker-dealers’ and investment advisers’ interactions with investors through the use of predictive data analytics or artificial intelligence.  You can find our summary of these proposed rules here.}