CHALLENGES OF APPLYING GENERATIVE AI IN KNOWLEDGE MANAGEMENT: INSIGHTS FROM A SYSTEMATIC LITERATURE REVIEW
Article Sidebar
Main Article Content
Abstract
Rapid development of Generative Artificial Intelligence (GenAI) transforms the way in which organizations create, process and manage knowledge. As this technology is more and more integrated with business practices, understanding its impact on knowledge management (KM) is crucial. This paper presents a systematic literature review which aims at identification and analysis of key challenges associated with the use of GenAI in the context of KM. The review uncovers 17 challenges, which are clustered into four groups: technological and functional limitations of GenAI; trust, acceptance, and social factors; organizational and cultural impact; and legal issues, and strategic risks. The research also draws future research directions, including the need of evaluation of the long-term influence of GenAI on decision making, knowledge validation, user behavior, and organizational structures. The findings offer both theoretical insights as well as practical guidelines for researchers and practitioners, contributing to more structured and responsible approach towards integration of GenAI in knowledge-based environments.
Article Details
References
Accenture [2023], Generative AI: Understanding generative AI and how it will fundamentally transform our world, https://www.accenture.com/us-en/insights/generative-ai (accessed:15.02.2024).
Alavi M., Leidner D. E., Mousavi R. [2024], Knowledge management perspective of generative artificial intelligence, Journal of the Association for Information Systems 25 (1):1–12, https://doi.org/10.17705/1jais.00859.
Benbya H., Strich F., Tamm T. [2024], Navigating generative artificial intelligence promises and perils for knowledge and creative work, Journal of the Association for Information Systems 25 (1): 23–36, https://doi.org/10.17705/1jais.00861.
Choudhury A., Shamszare H. [2023], Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis, Journal of Medical Internet Research 25 (1), https://doi.org/10.2196/47184.
Dempsey L. [2023], Generative Al and large language models: Background and contexts, https://www.lorcandempsey.net/ intro-gen-ai/ (accessed: 15.02.2024).
Drucker P. [1992], The new society of organizations, Harvard Business Review September/October: 95–105.
Ferrara E. [2023], Should ChatGPT be biased? Challenges and risks of bias in large language models, First Monday 28 (11), https://doi.org/10.48550/arXiv.2304.03738.
ORGANIZATION AND MANAGEMENT • no. 2 / 2025 (198) 73
García-Peñalvo F. J., Llorens-Largo F., Vidal J. [2024], The new reality of education in the face of advances in generative artificial intelligence [La nueva realidad de la educación ante los avances de la inteligencia artificial generativa], Revista Iberoamericana De Educación a Distancia 27 (1): 9–32, https://doi.org/10.5944/ried.27.1.37716.
Ghimire P., Kim K., Acharya M. [2024], Opportunities and challenges of generative AI in construction industry: Focusing on adoption of text-based models, Buildings 14 (1): 220, https://doi.org/10.3390/buildings14010220.
Hu S., Zhang H., Zhang W. [2023], Domain knowledge graph question answering based on semantic analysis and data augmentation, Applied Sciences 13 (15): 8838, https://doi.org/10.3390/app13158838.
Jashapara A. [2011], Knowledge management: An integrated approach, 2nd Edition, Financial Times/Prentice Hall, London.
Korzynski P. et al. [2023], Generative artificial intelligence as a new context for management theories: Analysis of ChatGPT, Central European Management Journal 31 (1): 3–13, https://doi.org/10.1108/CEMJ-02-2023-0091.
Mertins K., Heisig P., Vorbeck J. [2000], Knowledge management: Best practices in Europe, Springer-Verlag, New York.
Milton N., Lambe P. [2020], The knowledge manager’s handbook: A step-by-step guide to embedding effective knowledge management in your organization, 2nd Edition, Kogan Page, New York.
Morandini S., Fraboni F., De Angelis M., Puzzo G., Giusino D., Pietrantoni L. [2023], The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations, Informing Science 26: 39–68, https://doi.org/10.28945/5078.
Nazeer S., Sumbal M. S., Liu G., Munir H., Tsui E. [2023], The next big thing: Role of ChatGPT in personal knowledge management challenges and opportunities for knowledge workers across diverse disciplines, Global Knowledge, Memory and Communication, vol. ahead-of-print, no. ahead-of-print, https://doi.org/10.1108/GKMC-07-2023-0246.
Naqbi H. A., Bahroun Z., Ahmed V. [2024], Enhancing work productivity through generative artificial intelligence: A comprehensive literature review, Sustainability 16 (3): 1166, https://doi.org/10.3390/su16031166.
Newell S., Robertson M., Scarbrough H., Swan J. [2009], Managing knowledge work and innovation, Palgrave Macmillan, Basingstoke, Hampshire.
OpenAI [2024], Research Index, https://openai.com/research (accessed: 15.02.2025).
Quan H., Li S., Zeng C., Wei H., Hu J. [2023], Big data and AI-driven product design: A survey, Applied Sciences 13 (16): 9433, https://doi.org/10.3390/app13169433.
Stoykova S., Shakev N. [2023], Artificial intelligence for management information systems: Opportunities, challenges, and future directions, Algorithms 16 (8): 357, https://doi.org/10.3390/a16080357.74 ORGANIZATION AND MANAGEMENT • no. 2 / 2025 (198)
Swan J., Scarborough H., Preston J. [1999], Knowledge management – the next fad to forget people?, Proceedings of the 7th European Conference on Information Systems, Copenhagen.
van der Zant T., Kouw M., Schomaker L. [2013], Generative Artificial Intelligence, in: Muller V. C., Philosophy and Theory of Artificial Intelligence, Springer, Berlin/Heidelberg: 107–120, https://dx.doi.org/10.1007/978-3-642-31674-6_8.
Wach K., Duong C. D., Ejdys J., Kazlauskaitė R., Korzynski P., Mazurek G., Paliszkiewicz J., Ziemba E. [2023], The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT, Entrepreneurial Business and Economics Review 11 (2): 7–30, https://doi.org/10.15678/EBER.2023.110201.
Webster J., Watson R. T. [2002], Analyzing the past to prepare for the future: Writing a literature review, MIS Quarterly 26 (2): xiii–xxiii.
Xing Y., Yu L., Zhang J. Z., Zheng L. J. [2023], Uncovering the dark side of artificial intelligence in electronic markets, Journal of Organizational and End User Computing 35 (1): 1–25, https://doi.org/10.4018/JOEUC.32727.