Artificial Intelligence in Venture Capital : Insights from the CEE Region - Červinka, David (2024)
This master’s thesis explores how venture capital (VC) firms in Central and Eastern Europe (CEE) are adopting artificial intelligence (AI) into their operations and provides insights into the perceived benefits, challenges, and future outlook. The thesis uses a qualitative research approach, and it is based on 20 semi-structured interviews with venture capitalists (VCs) from firms based in the CEE region. The interview transcripts are subsequently analyzed using thematic analysis. The findings reveal that the majority of interviewed VCs are actively using AI tools, primarily in the evaluation and initial screening phases, with ChatGPT being the most utilized tool and internally developed tools focusing on founder sourcing. The main perceived benefits include uncovering new opportunities, building earlier relationships with founders, and improving operational efficiency. In contrast, challenges include limitations in the quality of AI outputs, regional specificities, integration issues, and the need for human involvement. The future outlook suggests that while sourcing will become fully automated and AI will significantly improve screening, research, and evaluation of investment opportunities, the human element will remain crucial in building relationships and making investment decisions. Additionally, the research findings emphasize the importance of augmenting VC roles with AI tools, particularly in the CEE region, where the VC ecosystem is heavily relationship-oriented. The thesis concludes with theoretical and practical contributions, limitations, and implications for future research.