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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

    as
    1. Täuscher, Karl & Laudien, Sven M., 2018. "Understanding platform business models: A mixed methods study of marketplaces," European Management Journal, Elsevier, vol. 36(3), pages 319-329.
    2. Borges, Aline F.S. & Laurindo, Fernando J.B. & Spínola, Mauro M. & Gonçalves, Rodrigo F. & Mattos, Claudia A., 2021. "The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions," International Journal of Information Management, Elsevier, vol. 57(C).
    3. Martin Obschonka & David B. Audretsch, 0. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 0, pages 1-11.
    4. Gerrit Remane & Andre Hanelt & Jan F. Tesch & Lutz M. Kolbe, 2017. "The Business Model Pattern Database — A Tool For Systematic Business Model Innovation," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 1-61, January.
    5. Elia, Gianluca & Margherita, Alessandro & Passiante, Giuseppina, 2020. "Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    6. Andreas Hein & Maximilian Schreieck & Tobias Riasanow & David Soto Setzke & Manuel Wiesche & Markus Böhm & Helmut Krcmar, 2020. "Digital platform ecosystems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 87-98, March.
    7. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    8. Jan Jöhnk & Malte Weißert & Katrin Wyrtki, 2021. "Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 5-20, February.
    9. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    10. Benjamin Amshoff & Christian Dülme & Julian Echterfeld & Jürgen Gausemeier, 2015. "Business Model Patterns For Disruptive Technologies," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-22.
    11. Gerard George & Adam J. Bock, 2011. "The Business Model in Practice and its Implications for Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 35(1), pages 83-111, January.
    12. Buxmann, Peter & Hess, Thomas & Thatcher, Jason Bennett, 2021. "AI-Based Information Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149083, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Alina Köchling & Shirin Riazy & Marius Claus Wehner & Katharina Simbeck, 2021. "Highly Accurate, But Still Discriminatory," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 39-54, February.
    14. Chay Brooks & Cristian Gherhes & Tim Vorley, 2020. "Artificial intelligence in the legal sector: pressures and challenges of transformation," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 135-152.
    15. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    16. Peter Buxmann & Thomas Hess & Jason Bennett Thatcher, 2021. "AI-Based Information Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 1-4, February.
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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.

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