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Paper Number
ICIS2025-1397
Paper Type
Complete
Abstract
Organizations increasingly face the challenge of preserving institutional knowledge in the face of employee turnover, retirements, and fragmented knowledge management systems. As a result, effectively organizing organizational knowledge has become critical to maintaining competitiveness and resilience. Using a qualitative research design, we conducted 18 expert interviews with professionals from knowledge management, AI, and IT consulting to identify the key risks associated with the processes of knowledge storage, retrieval, and transfer. We further explored how GenAI can be leveraged to mitigate these risks, and which new challenges its use may introduce. Our findings reveal general and GenAI-specific risks across eleven categories, including knowledge loss, system complexity, and erroneous outputs. Our study extends a foundational knowledge management framework to the context of GenAI, and it proposes a research agenda that outlines future directions to mitigate organizational knowledge management risks and improve organizational knowledge storage, retrieval, and transfer using GenAI.
Recommended Citation
Schaetzle, Anna; Zöll, Anne; Sammet, Nicklas; Brack, Fabian Gabriel; Scholz, Jonas; and Schwarz, Lennart, "A Research Agenda for Generative AI in Organizational Knowledge Management: Addressing Risks in Knowledge Storage, Retrieval, and Transfer" (2025). ICIS 2025 Proceedings. 6.
https://aisel.aisnet.org/icis2025/is_transformwork/is_transformwork/6
A Research Agenda for Generative AI in Organizational Knowledge Management: Addressing Risks in Knowledge Storage, Retrieval, and Transfer
Organizations increasingly face the challenge of preserving institutional knowledge in the face of employee turnover, retirements, and fragmented knowledge management systems. As a result, effectively organizing organizational knowledge has become critical to maintaining competitiveness and resilience. Using a qualitative research design, we conducted 18 expert interviews with professionals from knowledge management, AI, and IT consulting to identify the key risks associated with the processes of knowledge storage, retrieval, and transfer. We further explored how GenAI can be leveraged to mitigate these risks, and which new challenges its use may introduce. Our findings reveal general and GenAI-specific risks across eleven categories, including knowledge loss, system complexity, and erroneous outputs. Our study extends a foundational knowledge management framework to the context of GenAI, and it proposes a research agenda that outlines future directions to mitigate organizational knowledge management risks and improve organizational knowledge storage, retrieval, and transfer using GenAI.
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