IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v66y2023i6p777-788.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681323000502
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2023.04.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gangopadhyay, Partha & Jain, Siddharth & Bakry, Walid, 2022. "In search of a rational foundation for the massive IT boom in the Australian banking industry: Can the IT boom really drive relationship banking?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.
    3. Erdsiek, Daniel & Rost, Vincent, 2022. "Datenbewirtschaftung in deutschen Unternehmen: Umfrageergebnisse zu Status-quo und mittelfristigem Ausblick," ZEW Expert Briefs 22-09, ZEW - Leibniz Centre for European Economic Research.
    4. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
    5. Woszczyna Karolina & Mania Karolina, 2023. "The European map of artificial intelligence development policies: a comparative analysis," International Journal of Contemporary Management, Sciendo, vol. 59(3), pages 78-87, September.
    6. Yi Sun & Shihui Li & Lingling Yu, 2022. "The dark sides of AI personal assistant: effects of service failure on user continuance intention," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 17-39, March.
    7. Mohd Syaiful Rizal Abd Hamid & Nor Ratna Masrom & Nur Athirah Binti Mazlan, 2022. "The Key Factors of the Industrial Revolution 4.0 in the Malaysian Smart Manufacturing Context," International Journal of Asian Business and Information Management (IJABIM), IGI Global, vol. 13(2), pages 1-19, August.
    8. Hannes Rothe & Katharina Barbara Lauer & Callum Talbot-Cooper & Daniel Juan Sivizaca Conde, 2023. "Digital entrepreneurship from cellular data: How omics afford the emergence of a new wave of digital ventures in health," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
    9. Marcel Rolf Pfeifer, 2021. "Human Resources during COVID-19: A Monthly Survey on Mental Health and Working Attitudes of Czech Employees and Managers during the Year 2020," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    10. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    11. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    12. Christoph Keding, 2021. "Understanding the interplay of artificial intelligence and strategic management: four decades of research in review," Management Review Quarterly, Springer, vol. 71(1), pages 91-134, February.
    13. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl, 2022. "Radiologists’ Usage of Diagnostic AI Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 293-309, June.
    14. Jean-Luc Pradel Mathurin Augustin & Shu-Yi Liaw, 2020. "Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model," International Business Research, Canadian Center of Science and Education, vol. 13(6), pages 1-73, June.
    15. Fawwaz Tawfiq Awamleh & Ala Nihad Bustami, 2022. "Examine the Mediating Role of the Information Technology Capabilities on the Relationship Between Artificial Intelligence and Competitive Advantage During the COVID-19 Pandemic," SAGE Open, , vol. 12(3), pages 21582440221, August.
    16. Pablo Matías Herrera & Ana Inés Heras, 2023. "Epistemic Opacity And Epistemic Transparency. Organizations That Produce, Support And Recreate Them In Platform Economies," Revista Ciencias Administrativas (CADM), IIA, Universidad Nacional de La Plata, Instituto de Investigaciones Administrativas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, issue 22, pages 1-10, July-Dece.
    17. Giorgi Shuradze & Yevgen Bogodistov & Heinz-Theo Wagner, 2018. "The Role Of Marketing-Enabled Data Analytics Capability And Organisational Agility For Innovation: Empirical Evidence From German Firms," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-32, May.
    18. Power, Michael, 2022. "Theorizing the economy of traces: from audit society to surveillance capitalism," LSE Research Online Documents on Economics 112167, London School of Economics and Political Science, LSE Library.
    19. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    20. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:bushor:v:66:y:2023:i:6:p:777-788. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/bushor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.