How will GPs take on tech?

Private markets firms have historically been way behind the curve when it comes to new technology adoption in their own organisations. Yet new developments are starting to change this. How might this affect the way GPs run their firms?

Private markets firms may have a strong track record when it comes to implementing new technologies in their portfolio companies or even backing game-changing enterprise software and apps. Yet many have remained in the dark ages when it comes to applying some of this technology to their own organisations.

That is all starting to change as some firms start to hire data scientists and look at how they can apply forms of artificial intelligence to various aspects of managing a firm as well as making and monitoring investments. Here are five shifts that LPs can expect to see among GPs in years to come.

Use cases will differ between private equity and venture capital firms

Around 60% of private equity and venture capital firms are at least in the process of adopting some form of artificial intelligence in their processes, according to Till Burges, Managing Director, HarbourVest. “For venture capital firms, the most likely application will be around narrowing down their sourcing funnels,” he says. “VCs meet thousands of companies and so they are looking at how they can get better and faster signals and hone in on who they should talk to – AI is a great tool to help them with this task. Private equity sourcing tends to be less data-driven as a good contact base goes a long way. Here, the focus will first be much more on how to make due diligence quicker and better and later on, the harder puzzle to solve with technology – how to create value in portfolio companies.”

3. Early adoption will focus on grunt work

“Most use cases will look at where the immediate value addition can come from,” says Burges. “It costs a lot of money to employ, say, 50, 100 or 200 investment professionals. Firms will be asking themselves how they can direct these professionals to work that AI can’t do. All the repetitive tasks like running analysis, writing memos and meeting notes that people have historically spent hours doing will become increasingly automated.”

4. More advanced use cases could improve investment decisions

“Private equity is very much about decision-making,” says Burges. “And there are documents such as investment memos and reports that you can mine with AI to determine whether you are as smart as you think you are. Some GPs are just starting to look at what AI can tell them about their track record based on the 10 to 15 investments they have done and then how they can become better investors using their own database combined with experience. They can use their next investment memo and AI to ask questions and come up with scores that can provide a second guess and supplement to the human process.”

2. It will be a multi-step process

“Everyone has to start with data,” says Stephen Nundy, Partner, Lakestar. “We did this several years ago and our first job was to collect and put into one central database all the data we were looking at from unstructured sources, such as pitch decks, board decks, market-wide information and so on. We then overlaid this with a crude form of AI to classify the documents and determine whether information was of interest or not. This helped us work out whether we should put an analyst on a particular opportunity or not and even which analyst would be most appropriate. From there, we were able to create automated human workstreams.”

5. GPs will need to take an experimental approach

“There are not enough data scientists on the planet to solve the problems mankind wants to have solved,” says Nundy. “So, whereas the past two decades have seen us all be consumers of technology, in the next period, we’ll all become producers of technology. That means firms need to encourage teams to experiment with the right guardrails and mindset with a view that these experiments will fail a few times before they find success. You can’t wait for the engineers and data scientists to solve all your problems or you’ll be behind at the races. The next generation of organisations will empower their workforces to figure it out for themselves.”