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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. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    2. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    3. Sturm, Timo & Gerlach, Jin & Pumplun, Luisa & Mesbah, Neda & Peters, Felix & Tauchert, Christoph & Nan, Ning & Buxmann, Peter, 2021. "Coordinating Human and Machine Learning for Effective Organizational Learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 125653, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Jarrahi, Mohammad Hossein & Askay, David & Eshraghi, Ali & Smith, Preston, 2023. "Artificial intelligence and knowledge management: A partnership between human and AI," Business Horizons, Elsevier, vol. 66(1), pages 87-99.
    5. Sebastian Lins & Konstantin D. Pandl & Heiner Teigeler & Scott Thiebes & Calvin Bayer & Ali Sunyaev, 2021. "Artificial Intelligence as a Service," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 441-456, August.
    6. Koppe, Timo & Schatz, Jonas, 2021. "Cloud-based ML Technologies for Visual Inspection: A Case Study in Manufacturing," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124696, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    2. 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.
    3. France, Stephen L., 2024. "Navigating software development in the ChatGPT and GitHub Copilot era," Business Horizons, Elsevier, vol. 67(5), pages 649-661.
    4. Ramaul, Laavanya & Ritala, Paavo & Ruokonen, Mika, 2024. "Creational and conversational AI affordances: How the new breed of chatbots is revolutionizing knowledge industries," Business Horizons, Elsevier, vol. 67(5), pages 615-627.
    5. Sundberg, Leif & Holmström, Jonny, 2024. "Innovating by prompting: How to facilitate innovation in the age of generative AI," Business Horizons, Elsevier, vol. 67(5), pages 561-570.

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