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

Leif Sundberg and Jonny Holmström

Business Horizons, 2023, vol. 66, issue 6, 777-788

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.

Keywords: AI; Machine learning; No-code software; MLOps; Operational AI (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0007681323000502
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:bushor:v:66:y:2023:i:6:p:777-788

DOI: 10.1016/j.bushor.2023.04.003

Access Statistics for this article

Business Horizons is currently edited by C. M. Dalton

More articles in Business Horizons from Elsevier
Bibliographic data for series maintained by Catherine Liu ().