Document Type : Review
Author
Assistant Professor, Department of Knowledge and Information Science, Azarbaijan Shahid Madani University
Abstract
Purpose: This study aimed to systematically review the role of generative artificial intelligence (AI) in transforming organizational knowledge management and to propose an integrated framework based on an analysis of published literature.
Methodology: This study was conducted as a systematic review with qualitative content analysis of articles published in reputable databases between 2018 and 2024. The search was performed following the PRISMA protocol using standardized keywords. From an initial pool of 257 studies, 72 relevant articles were selected based on inclusion and exclusion criteria and underwent a multi-stage analytical process.
Findings: Data analysis revealed that generative AI has the potential to transform organizational knowledge management through four key mechanisms: (1) automated knowledge generation, (2) intelligent knowledge sharing, (3) extraction of tacit knowledge, and (4) personalization of knowledge processes. The most significant identified challenges fell into three categories: (a) technical and validation issues (e.g., model hallucination, low accuracy, and the black-box problem), (b) ethical and security considerations (e.g., bias, privacy violations, and accountability), and (c) organizational and human barriers (e.g., resistance to change and the need for skill development).
Conclusion: Generative AI serves as a transformative technology with the potential to redefine organizational knowledge management ecosystems. However, its success requires adapting an integrated approach that simultaneously addresses technical, human, and organizational dimensions. The proposed framework from this research can act as a roadmap for researchers and managers to implement this technology responsibly and effectively.
Value: This study contributes to the literature by offering a comprehensive synthesis of generative AI's role in knowledge management and presenting an integrated framework that bridges technical capabilities with organizational readiness. It provides actionable insights for practitioners and a foundational reference for future empirical research in this emerging field.
Keywords
- Generative Artificial Intelligence
- Organizational Knowledge Management
- Systematic Review
- Qualitative Content Analysis
- Integrated Framework
- Opportunities and Challenges
Main Subjects
Acemoglu, D., & Restrepo, P. (2020). The wrong kind of AI? Artificial intelligence and the future of labour demand. Cambridge Journal of Regions, Economy and Society, 13(1), 25–35. https://doi.org/10.1093/cjres/rsz022
Acs, Z. J., Song, A. K., Szerb, L., Audretsch, D. B., & Komlósi, É. (2021). The evolution of the global digital platform economy: 1971–2021. Small Business Economics, 57(4), 1629–1659. https://doi.org/10.1007/s11187-021-00561-x
Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), e35179. https://doi.org/10.7759/cureus.35179
Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3
Baier, P., & Sviokla, J. J. (2025). Turbocharging organizational learning with GenAI. MIT Sloan Management Review. https://sloanreview.mit.edu/article/turbocharging-organizational-learning-with-genai/
Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45(1), 315–342. https://doi.org/10.25300/MISQ/2021/15883
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 1–20. https://hbr.org/2017/07/the-business-of-artificial-intelligence
Carillo, K., Scornavacca, E., & Za, S. (2017). The role of media dependency in predicting continuance intention to use ubiquitous media systems. Information & Management, 54(3), 317–335. https://doi.org/10.1016/j.im.2016.09.002
Davenport, T. H., & Mittal, N. (2022). How generative AI is changing creative work. Harvard Business Review Digital Articles. https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work
Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Press.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Dezi, L., Santoro, G., Gabteni, H., & Pellicelli, A. C. (2018). The role of big data in shaping ambidextrous business process management: Case studies from the service industry. Business Process Management Journal, 24(5), 1163–1175. https://doi.org/10.1108/BPMJ-07-2017-0215
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
European Parliament. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005
Ferraris, A., Santoro, G., & Dezi, L. (2017). How MNC's subsidiaries may improve their innovative performance? The role of external sources and knowledge management capabilities. Journal of Knowledge Management, 21(3), 540–552. https://doi.org/10.1108/JKM-09-2016-0411
Ferraris, A., Santoro, G., & Pellicelli, A. C. (2020). "Openness" of public governments in smart cities: Removing the barriers for innovation and entrepreneurship. International Entrepreneurship and Management Journal, 16(4), 1259–1280. https://doi.org/10.1007/s11365-020-00651-4
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Huang, L. Y., Zhang, X., Wang, Q., et al. (2024). Evaluating media knowledge capabilities of intelligent search dialogue systems: A case study of ChatGPT and New Bing. Journal of the Knowledge Economy, 15, 17284–17307. https://doi.org/10.1007/s13132-024-01755-w
Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Press.
IBM Institute for Business Value. (2023). The CEO's guide to generative AI: Actionable insights for the next era of business. IBM Corporation. https://www.ibm.com/thought-leadership/institute-business-value
Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87–99. https://doi.org/10.1016/j.bushor.2022.03.002
Jatobá, M. N., Ferreira, J. J., Fernandes, P. O., & Teixeira, J. P. (2023). Intelligent human resources for the adoption of artificial intelligence: A systematic literature review. Journal of Organizational Change Management, 36(7), 1099–1124. https://doi.org/10.1108/JOCM-03-2022-0075
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Manning, S., Mishkin, P., Rock, D., & Eloundou, T. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130. https://arxiv.org/abs/2303.10130
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773. https://doi.org/10.1016/j.respol.2019.03.018
Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
Pletsch, A. L. B., Tonial, G., Matos, F., & Koch, L. L. (2026). Digital transformation: The role of generative AI in the evolution of knowledge management systems. Journal of Knowledge Management. 1-27. https://doi.org/10.1108/JKM-09-2025-1398
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Santoro, G., Beimer, P., & Lazari, A. (2026). Ok CESARE, can you do this for me? Insights on how artificial intelligence fosters knowledge management. Journal of Knowledge Management, 30(3), 1032–1044. https://doi.org/10.1108/JKM-01-2025-0070
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2022). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 64(4), 85–109. https://doi.org/10.1177/00081256221092035
Sumbal, M. S., & Amber, Q. (2025). ChatGPT: A game changer for knowledge management in organizations. Kybernetes, 54(6), 3217–3237. https://doi.org/10.1108/K-06-2023-1126
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
Vom Brocke, J., Maass, W., Buxmann, P., Maedche, A., Leimeister, J. M., & Pecht, G. (2020). Future work and enterprise systems. Business & Information Systems Engineering, 62(4), 357–366. https://doi.org/10.1007/s12599-020-00646-z
Wessel, L., Baiyere, A., Ologeanu-Taddei, R., Cha, J., & Blegind-Jensen, T. (2021). Unpacking the difference between digital transformation and IT-enabled organizational transformation. Journal of the Association for Information Systems, 22(1), 102–129. https://doi.org/10.17705/1jais.00655
Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
World Economic Forum. (2023). The future of jobs report 2023. https://www.weforum.org/reports/the-future-of-jobs-report-2023/
Zhang, L., Shao, Z., Chen, B., & Benitez, J. (2024). Unraveling generative AI adoption in enterprise digital platforms: The effect of institutional pressures and the moderating role of internal and external environments. IEEE Transactions on Engineering Management, 72, 335–348. https://doi.org/10.1109/TEM.2024.3513773
Zuin, G. L., Mastelini, S. M., Loures, T. C., & Veloso, A. (2025). Leveraging large language models for tacit knowledge discovery in organizational contexts. In 2025 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). https://doi.org/10.1109/IJCNN64981.2025.11227259
)