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Democratizing artificial intelligence: How no-code AI can leverage machine learning operations

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  • Sundberg, Leif
  • Holmström, Jonny

Abstract

Organizations are increasingly seeking to generate value and insights from their data by integrating advances in artificial intelligence (AI) (e.g., machine learning (ML) systems) into their operations. However, there are several managerial challenges associated with ML operations (MLOps). In this article, we outline three key challenges and discuss how an emerging type of AI platform—no-code AI—may help organizations address and overcome them. We outline how no-code AI can leverage MLOps by closing the gap between business and technology experts, enabling faster iterations between problems and solutions, and aiding infrastructure management. After outlining the important remaining challenges associated with no-code AI and MLOps, we propose three managerial recommendations. By doing so, we provide insights into an important emerging phenomenon in AI software and set the stage for further research in the area.

Suggested Citation

  • Sundberg, Leif & Holmström, Jonny, 2023. "Democratizing artificial intelligence: How no-code AI can leverage machine learning operations," Business Horizons, Elsevier, vol. 66(6), pages 777-788.
    • Handle: RePEc:eee:bushor:v:66:y:2023:i:6:p:777-788
    DOI: 10.1016/j.bushor.2023.04.003
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    References listed on IDEAS

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    1. Simons, Martin & Roloff, Malte & Liebe, Andrea & Lundborg, Martin, 2023. "Künstliche Intelligenz mit AutoML, Low-Code und No-Code: Eine Markterhebung von Software-Tools," WIK Discussion Papers 501, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH.

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