Venture capital and risks in growth stages of artificial intelligence tech start-ups

Abstract

Venture capital (VC) investment is important for the growth and innovation of artificial intelligence (AI)-driven tech start-ups, which are often characterized by high uncertainty and rapid technological change. While existing literature has explored general risk factors in AI start-ups, however, limited understanding of how these risks vary across different stages of start up development. This study addresses this gap through a systematic literature review (SLR) of 29 studies published between 2019-2024, sourced from IEEE Xplore, Web of Science (WoS), Scopus, and ProQuest databases. Guiding investment lifecycle, risks management and ISO 31000 principles, the study identified key risks variations including market, operational, financial, technological, performance, regulatory and exit risks faced by AI tech start-ups during the seed and early, growth and maturity stages. Findings indicate that early-stage start-ups are more affected by funding, market entry, and feasibility risks, while at growth stage face issues with scaling and resource management, maturity stage with regulatory and exit related risks become more significant. A taxonomy matrix is developed to categorize these risks in a stage-specific and AI-relevant context. The review contributes to the literature by offering a structured understanding of how VC related risks evolve across start-ups stages and highlights the need for further empirical research to validate these findings and guide better investment decisions.