Artificial Intelligence: The Next Revolution in Private Equity

Published July 6, 2018 | 3 min read

Artificial Intelligence is advancing as a disruptive force in the private equity industry. This article was originally posted on RBC Investor and Treasury Services’ website.

At a recent Private Equity Summit hosted by RBC Investor & Treasury Services in Paris1, Olivier Younès, founder of tech-centred investment bank EXPEN and a professor at HEC Paris, University of California, Berkeley and the Singularity University, noted that, “Artificial Intelligence (AI), by following the inevitable process of digitalizing both information and the real world, represents a pivot from big data to smart data”.

Key insights

  • The Artificial Intelligence (AI) wave has already begun disrupting many sectors, from healthcare to education, banking and manufacturing
  • Private equity players are investing in innovative companies using AI, but the investment criteria of quality of team and market relevant remain key investing triggers
  • Private Equity and Venture Capital firms are in the process of using AI for their day-to-day operations, beginning with sourcing to pre-empt deals, and optimizing due diligence practices

The impact and influence of AI is being felt across all sectors of the economy. In healthcare with remote diagnosis and assisted surgery, in education through virtual reality and also in finance where Robo-advisors already advise investors across the world and high-frequency trading is standard practice in trading rooms. Private equity is not immune. Through capital raising, deal origination, and portfolio management, Younès asserts that, “there will be disruptions at all levels of this chain.”

AI already a preferred investment

Private equity participants already have a foothold in AI through their investments. For example, AnotherBrain, which is developing a chip that mirrors the workings of the cerebral cortex, raised EUR10 million in February 2018 from five funds and business angels. Sophia Genetics, a biotech firm working to diagnose genetic diseases and cancer, has raised EUR 58 million since its inception seven years ago. Furthermore, some venture capital funds focus exclusively on this field, such as Serena Data Ventures, a vehicle developed by Serena Capital.

“Three years ago, most Limited Partners were discovering the topic,” said Anne-Valérie Bach, a partner at Serena. “Today, no one questions the relevance of AI and big data as a major disruption in all sectors.”

Nevertheless, it can be challenging to invest in this sector for the simple reason that it has been built on the concept of 'open source', meaning the original source code is openly available for anyone to modify and redistribute. “The major groups decided to open up the game,” explains Michel Dahan, Executive Director at Kreaxi. “Instead of considering AI a trade secret, they made their tools open source, dramatically accelerating the sector's development and increasing the number of players in the market.” This means the technical solutions developed by companies have limited opportunity to be unique, which makes assessing the viability of AI investments a challenge.

There will be disruptions at all levels of this chain

"The technology is one of a number of criteria,” suggests Bach. “The team and market relevancy are also important.” This is critical because the market is sometimes hesitant to adopt AI solutions. In 2017, venture capital firm Alven invested in a company using AI to manage quality control in industrial supply chains. “It raises many questions,” explains Raffi Kamber, partner at Alven Capital. “How much does quality cost? Should we downsize our teams that have years of experience? Can we replace them by machines? If the company is able to shepherd its clients through this process, a huge market opens up for them.”

Sourcing: the first level of disruption

Manufacturers are not the only ones asking themselves questions, however. Aside from investing in AI, private equity players have also begun implementing tools to increase efficiency. In a study published in December 2017, secondary investment fund Coller Capital notes that two-thirds of limited partners anticipate the use of AI tools in private equity within the next five years. “For example, we can imagine retrieving LinkedIn data and detecting a position change, from data scientist to founder,” offers Kamber. “Some American and British funds already do.” As for Serena Capital, it has developed sourcing tools to discover potential businesses as early as possible, using data available on the Web. “It's useful but that's not what makes the difference,” notes Bach, for whom the human factor remains key to the investment process.

Aside from investing in AI, private equity players have also begun implementing tools to increase efficiency

“The agreement with the company head, believing in that individual and their ability to grow, continues to be what triggers the decision to invest,” adds Kamber, nonetheless admitting the usefulness of such tools for sorting and grading files.

Many tools already exist or are being developed in other areas, such as contract writing, auditing or due diligence, but the real urgency lies elsewhere. “Any investment firms that don't quickly equip themselves for sourcing companies to invest in will be left behind,” predicts Michel Dahan. It is what investment private equity players may consider, if they have not already left the starting block.

A recent notable AI development was Google's introduction of Duplex, an intelligent assistant capable of making appointments over the telephone. In early use cases, people did not realize they were talking to an AI system. Has the Turing test, developed by British researcher Alan Turing in 1950 to distinguish between human and machine, become obsolete? Clearly, AI advancements are steadily evolving.

Artificial Intelligence Insights

Artificial Intelligence