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Generative Artificial Intelligence in Startups: A Systematic Mapping Study
Book chapter   Peer reviewed

Generative Artificial Intelligence in Startups: A Systematic Mapping Study

Usman Rafiq, Marlene Klotz, F Pattyn, M Biasi, Xiaofeng Wang and Christoph Stöckmann
Advances in Software Startups: Generative AI, Product Engineering and Business Development, pp.13-33
Springer
2026
Handle:
https://hdl.handle.net/10863/50788

Abstract

GenAI Innovation Artificial intelligence Start-ups Early-stage companies Technology adoption Literature review Entrepreneurship
The recent advancements in generative artificial intelligence (GenAI) technologies have created new opportunities and challenges across various industries. In response, companies of all sizes and sectors have been exploring, adopting, and utilizing these technologies. Startups, in particular, stand out to benefit significantly from revolutionary GenAI technologies due to their focus on innovation, operating within limited resources and uncertainty, and short runways. Although research on GenAI has received significant attention, it remains fragmented across disciplines and underexplored. To address this gap, the current chapter aims to provide a comprehensive view of current research trends, opportunities, challenges, tools, and technologies of GenAI in startups. Following established protocol and guidelines, we conducted a systematic mapping study and identified 21 relevant studies from an initial pool of 236 studies, collected from four academic databases. Our findings reveal trends in research on GenAI for startups, GenAI opportunities, GenAI challenges, and GenAI tools used in startups. These findings provide an overview of GenAI’s capacity for startup practitioners to leverage its full potential while highlighting challenges that can amplify startup risks. Furthermore, we established a linkage between the startup stages and areas where GenAI presents the most significant opportunities and challenges. Finally, we suggest several practical implications based on these insights.
url
https://link.springer.com/chapter/10.1007/978-3-032-04294-1_2View

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