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The Value of AI in Decision Making for Early-Stage Investments

Utilizing artificial intelligence (AI) to support investment decisions is a frequent practice among large venture capital funds, yet most startup investors still stick with traditional methods. However, as technology advances and the expense of building algorithms through machine learning (ML) decreases, early-stage investors are beginning to wonder if AI can outperform angel investors or human judgement.

Gartner recently predicted that more than 75% of venture capital and early-stage investor reviews will use AI data to aid in their decision-making processes, rather than rely on “gut instinct.” As any improvement in the investment decision-making process can positively impact returns, it is well worth investigating whether AI is the answer to improved accuracy in those initial decisions.AI in 2021 

According to a Harvard Business Review study conducted on AI vs. human investors, it’s possible that algorithms do produce more fair and accurate investment returns.

Other similar studies indicate that it is also likely that applying ML to pre-venue standards aids in traditional methods’ ability to determine a startup’s probability of success. In addition, it helps investors screen a larger number of potential opportunities and speeds up the overall process.

While basing investment decisions on instinct and intuition is a traditionally lucrative process, AI is far superior to humans at filtering through substantial amounts of data to discover common variables and landing on unbiased conclusions. Which leads one to wonder if a combination of AI and human interaction is the best route to take for early-stage investments.

Using algorithms to their advantage and understanding AI’s use of the laws of probability in making decisions can help investors make more informed choices. The key is to know which of the choices are best left to human interaction and which should be assigned to technology.

While humans excel at thinking creatively and counterfactually, which are much-needed traits in early-stage investing, AI reigns superior when it comes to data prediction. If decision-makers can find ways to utilize both human interaction and technology during the initial stages, they stand to amplify their returns.

Finding early-stage investments can be a challenging process that AI and building algorithms through ML might help simplify. However, utilizing human intelligence and AI together as a team effort seems to bring about the best results for early-stage investments. A collaboration between human and artificial intelligence could be the answer to increasing speed, efficiency, confidence, and accuracy in early-stage investing.



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