Decision making in venture capital involves a lot of work. Venture capitalists must consider a number of issues when selecting an investment. To date, however, little research has been conducted on how artificial intelligence would impact the venture capital decision-making market. Therefore, this chapter extends previous contributions aimed at exploring the relationship between artificial intelligence and venture capital decisions. Evidence shows that artificial intelligence may influence venture capitalists' decisions in a number of ways, such as recognizing firms with high chances of success, assisting venture capitalists in choosing better investments, etc. However, there are also a number of barriers that venture capitalists face in adopting artificial intelligence. The originality of this chapter is the development of items that can be used to measure stages of artificial intelligence. Indeed, it provides some recommendations for how best to integrate artificial intelligence into the decision-making process of venture capitalists.
TopIntroduction
Venture capital (VC) has become a major funding source for creative businesses for the last thirty years (Blume & Hsueh 2023; Gompers et al., 2020; Gompers & Lerner, 2001). More precisely, businesses like Amazon, Apple, Facebook, Google, Netflix, Starbucks, and Gilead Sciences that have obtained VC funding have had a big influence on the US and worldwide economy (Gompers et al., 2020). However, due to the intricacy of the VC decision process, many studies (EL Harbi & Toumia, 2020; Gompers et al., 2020; Franke et al., 2008, Petty et al., 2023; Poindexter, 1976; Robinson, 1987; Röhm et al., 2022; Toumia & El Harbi, 2022; Tyebjee & Bruno, 1981; Wells, 1974) have been carried out to comprehend the venture capital decision-making process and the variables that influence it, such as management team qualities, external environmental features, and internal organizational attributes, circumstances unrelated to the contents of the proposal (i.e., the source of a proposal and the amount of readily available financial resources), cognitive biases, artificial intelligence, and so on. In fact, rapid advancements are being made in artificial intelligence (AI), opening up previously unimaginable prospects for improving venture capital firms’ performance (Mason & Harrison, 1999; Röhm et al., 2022). For instance, AI is believed to be well suited to improve venture capital organizations' decision-making processes, identify new investment opportunities and change venture capital landscape (Arroyo et al., 2019; Jain, 2018; Röhm et al., 2022; Schmidt, 2019). This chapter is the first to add to the expanding body of research on the impact of AI on venture capitalists' decision-making, as it explores the different AI stages within the VC framework. In fact, studying the effects of AI stages on VC is more interesting for several reasons: (1) it offers a more in-depth comprehension of the effects of AI, (2) enables us to spot trends and patterns in the activities of venture capital, and (3) it helps improve our hypotheses about how AI will impact the economy.
Therefore, this chapter combines two different areas, namely the literature on artificial intelligence and venture capital. We, then, presented the effects of artificial intelligence on venture capital businesses' decision-making processes as well as the main obstacles to their application. Indeed, we proposed recommendations for incorporating AI in the venture capitalists’ decision process. Finally, we have developed items to measure the three stages of AI.
There are seven main sections in this chapter. The first section provided the concepts of VC and describes the types of venture capitalists. The second section introduced the VC cycle. The third section was focused on the function that VC plays in the economy. The fourth section explained the procedure for investing in venture capital. The fifth section described artificial intelligence and its importance. The sixth section explored how AI may be used to enhance the process of making decisions in venture capital. For example, AI and machine learning may be applied to analyze Big Data, can aid investors make better choices regarding possible investments, identify potential investment risks, automate tasks, and so on... Indeed, we listed the barriers to the adoption of AI. The seventh section presented items to measure Artificial General Intelligence, Artificial Superintelligence, and Artificial Narrow Intelligence.