By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics, according to Gartner, Inc.
“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. “However, this “impossible to quantify inner voice” grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 as VC and private equity (PE) investors turn to leveraging AI and data science insights for due diligence.”
Gartner predicts that by 2025, the AI- and data-science-equipped VC or PE investor will become commonplace. Increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy away from gut feel and qualitative decision making to a more modern platform-based quantitative process. Information gathered from sources such as LinkedIn, PitchBook, Crunchbase and Owler, along with third-party data marketplaces, can be leveraged alongside diverse past and current investments.
“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,” said Mr. Stakenas.
Current AI technology is already capable of providing insights into customer desires and predicting future behavior. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about an individual from real-time or audio recordings. While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organizations will be leveraging it to determine which leadership teams are most likely to succeed.
“The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured,” said Mr. Stakenas. “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.”
