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AI Startup Business Models

Author

Listed:
  • Michael Weber

    (Technische Universität München)

  • Moritz Beutter

    (Technische Universität München)

  • Jörg Weking

    (Technische Universität München)

  • Markus Böhm

    (University of Applied Sciences Landshut)

  • Helmut Krcmar

    (Technische Universität München)

Abstract

We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action.

Suggested Citation

  • Michael Weber & Moritz Beutter & Jörg Weking & Markus Böhm & Helmut Krcmar, 2022. "AI Startup Business Models," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(1), pages 91-109, February.
    • Handle: RePEc:spr:binfse:v:64:y:2022:i:1:d:10.1007_s12599-021-00732-w
    DOI: 10.1007/s12599-021-00732-w
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    References listed on IDEAS

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    1. Thorsten Schoormann & Julia Schweihoff & Ilka Jussen & Frederik Möller, 2023. "Classification tools for business models: Status quo, comparison, and agenda," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-36, December.
    2. Michael Fruhwirth & Viktoria Pammer-Schindler & Stefan Thalmann, 2024. "Knowledge Leaks in Data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures," Schmalenbach Journal of Business Research, Springer, vol. 76(3), pages 357-396, September.
    3. Jorzik, Philip & Klein, Sascha P. & Kanbach, Dominik K. & Kraus, Sascha, 2024. "AI-driven business model innovation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 182(C).

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