Gartner: AI and data science to drive investment decisions rather than “gut feel” by mid-decade

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Turns out, “calling it from the gut,” may become a strategy of the past as data increasingly drives decision-making. But how will these data-driven approaches change investment teams?

In the age of digital transformation, artificial intelligence and data science are allowing companies to offer new products and services. Rather than relying on human-based intuition or instincts, these capabilities provide organizations with droves of data to make more informed business decisions.

Turns out, “calling it from the gut,” as the adage goes, may become an approach of the past as data increasingly drives investment decisions. A new Gartner report predicts that AI and data science to drive investment decisions rather than “gut feel” by mid-decade.

“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,” said Patrick Stakenas, senior research director at Gartner in a blog post. “However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making.”

Instead, AI and data analytics will inform more than three-quarters of “venture capital and early-stage investor executive reviews,” according to a Gartner report published earlier this month.

“The traditional pitch experience will significantly shift by 2025, and tech CEOs will need to face investors with AI-enabled models and simulations as traditional pitch decks and financials will be insufficient,” Stakenas said.

Alongside data science and AI, crowdsourcing will also help play a role in “advanced risk models, capital asset pricing models and advanced simulations evaluating prospective success,” per Gartner. While the company expects this data-driven approach as opposed to an intuitive approach to become the norm for investors by mid-decade, the report also notes a specific use-case highlighting using these methods.

Correlation Ventures uses information gleaned from a VC financing and outcomes database to “build a predictive data science model,” according to Gartner, allowing the fund to increase both the total number of investments and the investment process timeline “compared with traditional venture investing.”

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” Stakenas said.

A portion of the report delves into the myriad ways these shifts in investment strategy and decision making could alter the skills venture capital  companies seek and transform the traditional roles of investment managers. For example, Gartner predicts that a team of investors “familiar with analytical algorithms and data analysis” will augment investment managers.

These new investors—who are “capable of running terabytes of signals through complex models to determine whether a deal is right for them”—will apply this information to enhance “decision making for each investment opportunity,” according to the report.

The report also includes a series of recommendations for tech CEOs to develop in the next half-decade. This includes correcting or updating quantitative metrics listed on social media platforms and company websites for accuracy. Additionally, to increase a tech CEO’s “chances of making it to an in-person pitch” they should consider adapting leadership teams and ensure “online data showcases diverse management experience and unique skills,” the report said.

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