Last year, I wrote about how Private Equity (PE) firms were using Artificial Intelligence (AI) to drive investment returns – the key takeaway being that the PE industry was still far behind in terms of AI maturity, primarily due to structural reasons including limited data availability, long hold periods for assets, and non-recoverability of costs associated with building a data & analytics infrastructure.
While many of these challenges remain, PE professionals are increasingly concluding that the potential of AI in their industry is impossible to ignore – and we are starting to see many PE shops think differently and creatively about how to capture the AI opportunity.
Most of the new use cases we have seen emerge over the past 12 months are still narrow in nature, and security selection decisions still sit firmly in the hands of humans – but momentum is certainly starting to build. Some of the most interesting use cases I have observed over the past 12 months include:
- Investment research. AI-based solutions that have historically targeted public markets investors are now being refined to cater to the PE space. For example, AlphaSense (an AI-based investment research platform with 800+ clients) now covers 175,000+ private companies and includes PE-specific data sources.[1] These adjustments, coupled with the ability of most AI-based investment research tools to consume proprietary data from PE shops, are driving a growing popularization of AI-powered investment research solutions in the PE space. Among other benefits, early adopters are seeing ~10% capacity release among investment professionals, which translates into a broader investment funnel (i.e., more details screened) at no extra cost.
- Portfolio company reporting. AI has also emerged as a potential way to more efficiently process and consolidate portfolio company reporting, which is often not standardized. For example, the portfolio company value creation team of a large Canadian institutional investor used AI to automate 92%+ of the process to create a consolidated financial view across the portfolio. The team was also able to leverage AI to quickly identify the key metrics or business areas to focus on in each portfolio company. Among other benefits, early adopters spend up to 30% more time thinking about specific issue areas versus identifying them.
- Capital preservation. Identifying and proactively managing the risk of permanent impairment of capital is a perpetual objective of PE investing, and new AI solutions are emerging to assist investment professionals in this task. For example, Parabole AI is a solution that (i) allows PE investors to define, in their own words, the types of risk they want to proactively manage; (ii) scans a wide range of sources (e.g., business news, investment research, social media) to develop a score for each risk category defined by the PE investors; and, (iii) enables users to quickly navigate to the specific paragraphs in the sources that are driving risk scores.[2] Among other benefits, early adopters could create a consistent view of risk over time, free from cognitive biases, and more quickly identify key portfolio companies and areas to focus on.
Finally, it may be interesting to highlight that over the past 12 months some PE firms – particularly those operating in the mid-market space – are starting to organize differently to unlock the power of AI across their portfolio companies. Specifically, these PE shops are creating small data & analytics teams that offer specialized capabilities to portfolio companies. As these teams gain in popularity, one could reasonably expect that they will drive greater AI adoption within the PE shops as well.
The PE industry moves fast, and this blog will no doubt be out of date before too long. If you’d like to keep the conversation going, please feel free to reach out directly at paulo.salomao@accenture.com.
