These are nascent efforts but one forecast suggests adoption is about to pick up. AI will be involved in 75% of venture capital investment decisions by 2025, up from less than 5% today, according to a recent Gartner Inc. forecast. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. AI's ability to recognize patterns in data and predict likely outcomes has raised hopes that it can play a bigger role in decision-making in fields such as finance and healthcare. Now, similar bets are being placed in venture capital.

Gartner has forecast that AI and data science-equipped VC or private equity (PE) investors will become commonplace in the next 4-5 years, given the recent advances in data analytics and AI models. "Successful investors are purported to have a good gut feel-- the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company," Patrick Stakenas, senior research director at Gartner said in a statement. Stakenas said this inability to quantify inner voice grown from personal experience is decreasingly playing a role in investment decision making. AI models can help investors by determining the viability, strategy and potential outcome of an investment in an early-stage startup, based on past investments, revenue growth, market penetration, consumer sentiment and their industry experience. "AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success," added Stakenas.

Since Sweden's EQT Ventures embraced AI to drive the way it makes investments, the company has learned that reaping the benefits of algorithms is a journey full of detours that involve experimenting, fine-tuning, and adaption to achieve the promised efficiencies and insights. Following the firm's launch in 2016, a team there developed Motherbrain, an AI-driven system whose goal is to help EQT spot the hidden gems that no one else sees and back them early. So far, Motherbrain has directly led to investments in five startups out of the 50 the firm has made. That may seem like a disappointment. But according to Henrik Landgren, the EQT partner who took the lead on developing the system, the practical value so far has been the ability to make partners more productive by prioritizing which companies are worth spending time getting to know.

In a venture capital firm, you want different talents that will enrich the investing team, such as a person from industry, say, mixed with people from the finance world, and perhaps people with a legal or public policy background. You may even want an automaton that crunches numbers. "Motherbrain" is the name that Henrik Landgren, operating partner, and his colleagues at venture capital firm EQT Ventures have given to the computer program that they increasingly turn to in order to get an early read on potential investments. Motherbrain uses convolutional neural networks, or CNNs, the most popular form of machine learning, to review time-series data about companies to help guide where the firm should invest. The technology has seriously improved EQT Ventures's ability to scope out deals early in the pipeline, Landgren said in an interview with ZDNet.

At a meeting in Berlin with venture lead Ashley Lundström, Anydesk cofounder Philipp Weiser learned about Motherbrain, a machine learning system EQT Ventures built to find under-the-radar startups. "She told us we were among the first companies that were discovered by this software," says Weiser. In May, AnyDesk, which sells remote desktop software powered by a proprietary compression system, closed a funding round of $7.6 million with EQT Ventures. Whether the money EQT is putting into Anydesk will turn into a success story remains to be seen. But the firm has applied the Motherbrain algorithm to historical data and shown that it would have identified some of today's highest-flying tech companies as promising investment candidates before they became phenoms.