CHALLENGES OF ARTIFICIAL INTELLIGENCE FOR KNOWLEDGE MANAGEMENT SYSTEMS: A BIBLIOMETRIC ANALYSIS PERSPECTIVE

Constantin Bratianu () and Alexandru Ioan ()
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Constantin Bratianu: Bucharest University of Economic Studies, UNESCO Department for Business Administration, Bucharest, Romania; Academy of Romanian Scientists, Romania.
Alexandru Ioan: National University of Political Studies and Public Administration, the Faculty of Management, Bucharest, Romania

Oradea Journal of Business and Economics, 2025, vol. 10, issue 1, 108-120

Abstract: This paper explores the opportunities and challenges associated with integrating artificial intelligence (AI) into knowledge management systems (KMS), by using a bibliometric analysis. The rapid advancement of AI technologies, particularly generative models, has opened new avenues for enhancing KMS theories and practices. The study examines publication trends, key contributors, predominant research themes, and the practical applications of AI in KMS, with a specific focus on how these technologies can transform knowledge creation, sharing, and dissemination. The study draws on data from the Scopus database, revealing the significant impact of AI on KMS practices, particularly its capacity to enhance knowledge transfer, support decision-making processes, and foster organizational learning. However, the study also identifies several challenges, including ethical concerns, the interpretability of AI-driven tools, and the scalability of AI methods. The analysis underscores the need for further research in addressing these challenges and exploring the full potential of AI to fill knowledge gaps and create new knowledge artefacts. This paper provides valuable insights for scholars, practitioners, and organizations looking to harness AI for improving KMS theories and practices, offering a systematic analysis for future research on the evolving intersection of AI and KMS.

Keywords: knowledge management systems; artificial intelligence; deep learning; large language models; bibliometric analysis (search for similar items in EconPapers)
JEL-codes: C88 D83 J24 L86 M15 O32 O33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ora:jrojbe:v:10:y:2025:i:1:p:108-120

DOI: 10.47535/1991ojbe209

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