Sign On Artificial Intelligence (AI) is changing the investment process
Venture Capital (VC) firms have always been early adopters of new technology, whether implementing innovative hardware and communications technology to link offices around the globe or state-of-the-art software to streamline operations. Many VC funds have also been the tip of the spear when it comes to investing in AI firms. In 2022 alone, more than $45B was invested in AI start-ups globally, and now VC firms are utilizing AI to aid them in making more informed and more rapid investment decisions. Of course, humans still make the actual decisions, but AI rapidly synthesizes the data upon which decisions are based.
Increasing efficiency
VC firms are utilizing AI in a number of ways to enhance the efficiency of evaluation.
- Prospect identification – Data mining tools help VCs identify potential investment prospects by matching their criteria to emerging startups in sectors of interest. This is perhaps the most basic of uses, but also one that can save the most time.
- Financial analysis – AI can quickly analyze key financial data to spot trends and project future performance. While similar to models a VC might have previously used, AI can analyze tremendous amounts of data simultaneously and at a more rapid pace, producing detailed conclusions based on the data.
- Market research – VCs use AI to gather and analyze data on market size, competition, substitutes, and downstream demand when evaluating opportunities, while also evaluating risk of a given investment.
- Due diligence – AI can speed up lengthy due diligence processes by reviewing and analyzing thousands of documents and contracts to surface risks and red flags.
- Valuation models – AI algorithms can ingest data on the target company, market comps, and precedents in order to build valuation models to estimate a fair valuation range.
Artificial Intelligence by the numbers |
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Improving investment decisions
If harnessing AI to do much of the initial data analysis behind a potential investment, then expert human resources can redeploy their time to evaluating each aspect of the investment, logically arriving at a more accurate conclusion.
Specific qualitative areas in which VCs are using AI to evaluate potential investments:
- Success prediction – Some VCs use AI to assess start-up founding teams, products, and business models to predict their likelihood of success compared to past examples. This can result in a simpler and more objective evaluation rather than relying on the excellent, but time-consuming processes involving behavioral psychology and human due diligence.
- Sentiment analysis – Specialized web-crawling tools can scrape unstructured data such as online reviews, social media posts, and news articles to assess brand sentiment and public perception of a potential firm for investment.
- Portfolio monitoring – AI helps monitor the health of existing portfolio companies by tracking KPIs and other emerging data to spot early signs of trouble.
The main objective for the use of AI within a VC decision making framework is the increased speed of deal evaluation and due diligence and the amplification of human decisions. So, while AI alone can't replace human experience and expertise, leading VC firms are realizing promising results through the combination of AI and expert oversight.
“VCs with whom Wells Fargo works closely are embracing AI in more aspects of their investment process believing that AI helps them make faster and more accurate investment decisions.”
– Rahul Baig, Head of VC Coverage
Wells Fargo Technology Banking Group
“By 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed by AI and data analytics.”
– Gartner Research
