The rise of Artificial Intelligence (AI), with its potential to be either highly beneficial or detrimental to humanity, is driving the rapid evolution of digital technologies, significantly impacting the business world and fostering new entrepreneurial opportunities. AI, characterized by its ability to learn, adapt, and make decisions, optimizes crucial elements such as time and resources, which are essential for entrepreneurial success. Generative AI (GenAI), a subset of AI, has gained unprecedented traction, exemplified by the rapid adoption of tools like ChatGPT, which democratize access to advanced technology previously limited to large corporations. The proliferation of GenAI across various business functions is ushering in a new era of entrepreneurship, where leveraging AI's efficiencies can determine a venture's success and longevity. This thesis examines GenAI's role in entrepreneurship by addressing the lack of empirical evidence through an explorative approach using interview data. It develops a framework to understand GenAI's role in different stages of the venture creation process (VCP) and offers a practical guide for entrepreneurs to leverage GenAI technologies effectively. Focusing on German tech entrepreneurs, the research uses the External Enabler (EE) framework to provide empirical evidence of GenAI's benefits in the VCP. This thesis employs an exploratory research design with semi-structured interviews to gather qualitative data. A purposive and snowball sampling strategy was used to select 5 experts and 9 entrepreneurs of the software industry with relevant experience, ensuring a comprehensive understanding of GenAI's application in the VCP. Data was analyzed using qualitative content analysis, combining deductive and inductive methods to develop a robust category system. Triangulation of data sources enhanced the credibility of the findings, validating insights through referencing and consistency checks. This thesis identifies key enabling mechanisms of GenAI in the VCP. Additionally, it highlights inhibiting factors such as technical knowledge, trust, data security, and ethics of GenAI. The findings bridge the gap between theoretical models and practical applications, offering valuable insights for entrepreneurs, policymakers, and other stakeholders. The research underscores the importance of empirical evidence and the transformative potential of GenAI in enhancing operational efficiencies and achieving competitive advantages.