Published: May 10, 2023
In 2019 I wrote an article titled: Could AI change the way private equity firms source deals? It looked at how Artificial Intelligence (AI) could potentially reshape private equity by providing firms with a complementary way to source new investments. At that time, it appeared that only a handful of private equity firms globally had explored AI and attempted to apply the available technology to aspects of its operations, such as deal origination or due diligence.
A lot has changed during those intervening years, given the rapid advancement of AI technology. Applications like ChatGPT, which had over 100 million active users at the start of 2023, have brought AI firmly into the mainstream. There are of course concerns over the breakneck speed with which AI seems to be emerging in so many areas, as expressed last week by Geoffrey Hinton, often touted as the godfather of AI, who quit Google with a warning of its potential dangers: “I don’t think they should scale this up more until they have understood whether they can control it,” alluding to the AI arms race being waged by Google and Microsoft. However, the fact is that AI has been prevalent in many aspects of our lives for some time and isn’t just the domain of the tech giants. Banking, smart devices, social media, Netflix, voice assistants and search engines all make use of AI and it is a technology which we have all used to our benefit.
So, four years on, the question is no longer whether AI can help private equity firms source deals, but rather by leveraging the data sources and technologies now available, such as natural language processing (NLP) and machine learning (ML), the choice is there to apply it if there is the will to embrace it.
One way AI can assist private equity firms with their deal sourcing is through ML and more specifically the use of algorithms which can be developed to analyse structured data on a colossal scale. For instance, ML technology can trawl financial statements and industry reports to identify companies that meet specific investment criteria. This approach can help quickly screen large volumes of potential investment opportunities based on key financial metrics, market trends, and a whole host of other factors.
AI can also identify potential investment targets by utilising NLP to analyse large amounts of unstructured data. Using sources such as news articles, press releases and social media posts, NLP can help identify growth signals correlating to a private equity firm’s investment criteria, including new contract wins, a jump in revenues or senior management appointments. The ability to speed up the research process, at scale and utilise vast amounts of source data, can help to identify trends and patterns which otherwise may not be obvious through traditional methods, as well as to generate predictive models that can help in making more informed investment decisions.
AI can also help an investor get ahead of the curve and effectively anticipate future behaviours and opportunities. Through the analysis of detailed historical data, ML can identify patterns and make better predications about the future performance of a potential investment and offers the advantage that those algorithms will become more accurate as they process more data.
However, whilst AI is almost ubiquitous in our personal and business lives, and the future of dealmaking will likely be interlaced with AI, it is still under used within the private equity industry. Only a small number of firms have built their own AI-powered deal sourcing and workflow tools to source investment opportunities. According to research conducted by S&P Global Market Intelligence, as part of its 2022 Global Private Equity Outlook report, 14% of PE and VC practitioners leverage data science for automated deal sourcing or due diligence, while only 7% said that digital technologies have been fully implemented into their approach.
However, most private equity firms will understand the power of this technology and its potential when applied across a range of commercial applications, as many will have invested in companies that utilise AI within their products or services – in Maven’s case that includes innovate businesses such as Biorelate, Guru Systems, Nano Interactive, ORCHA, Plyable and RevLifter, which operate across a range of high growth sectors including BioTech, pharma, data analytics and AdTech.
The primary barrier to wider adoption of AI technology within the private equity industry is likely the need for a better understanding of the cost-benefit. AI can be expensive to implement as it requires specialist expertise, and there will be a reluctance to invest heavily until there is a better understanding of how effective AI technology can be in delivering a return on investment. Firms that have built AI platforms have certainly been able to deploy significant capital through AI-sourced opportunities, but in the main it is too early to determine whether these investments are delivering the same sustained level of returns as those sourced through traditional means.
Also, the decision to invest in a company is neither a linear nor purely analytical process. It is multifaceted and involves subjective judgements made by skilled investment professionals based on their own past experiences, something AI algorithms arguably cannot capture. In my 2019 article I said that it was hard to see a day when AI would fully replace human capital at an investment level, and I still see that being the case. Afterall, the makeup and character of the incumbent management team remains one of the most important considerations in deciding to invest. Despite the significant opportunities AI presents for the private equity industry, can it really replace human intuition when it comes to seeing the whites of a chief executive’s eyes. That expert judgement is surely irreplaceable.
However, with competition amongst private equity houses arguably fiercer than ever, having a differentiated origination strategy can undoubtedly provide a competitive edge. Where I see the real value is by adopting AI as a complementary deal sourcing tool, rather than looking at the technology as a replacement for current processes or the human element. Nurturing your established network of advisors and contacts will still feed a healthy deal pipeline but adding the ability to proactively hunt those elusive investment opportunities, and cast the net wider, can only help to make your deal origination strategy more robust and effective.
As traditionally late adopters of technology, it does appear that PE firms are exploring the potential of AI for their own business model with far more gusto as they seek to build a lasting, scalable advantage over the competition.