Paper Number
ICIS2025-2152
Paper Type
Short
Abstract
The temporal attributes of knowledge are critical to understanding complex knowledge processing systems, yet they remain underexplored compared to structural attributes. We distinguish between two dimensions of temporality: volatility (validity over time) and persistence (retention over time) of knowledge in these systems. This is particularly relevant for generative AI systems that use generative AI models to process organizational knowledge with diverse temporal attributes. We introduce a conceptual framework that distinguishes knowledge according to its volatility (static vs. dynamic validity) and persistence (transient vs. cumulative retention). Using this framework, we propose a multiple case study of archetypical generative AI systems characterized by different levels of volatility and persistence of organizational knowledge. We aim to explain differences in generative AI systems’ designs, how they influence broader organizational technology architecture, and evolve. This research extends the Knowledge-Based View (KBV) by incorporating knowledge temporality and offers a framework for strategic generative AI implementation.
Recommended Citation
Heimburg, Vincent; Yoo, Youngjin; and Wiesche, Manuel, "Temporality of Organizational Knowledge in Generative AI Systems" (2025). ICIS 2025 Proceedings. 13.
https://aisel.aisnet.org/icis2025/diginnoventren/diginnoventren/13
Temporality of Organizational Knowledge in Generative AI Systems
The temporal attributes of knowledge are critical to understanding complex knowledge processing systems, yet they remain underexplored compared to structural attributes. We distinguish between two dimensions of temporality: volatility (validity over time) and persistence (retention over time) of knowledge in these systems. This is particularly relevant for generative AI systems that use generative AI models to process organizational knowledge with diverse temporal attributes. We introduce a conceptual framework that distinguishes knowledge according to its volatility (static vs. dynamic validity) and persistence (transient vs. cumulative retention). Using this framework, we propose a multiple case study of archetypical generative AI systems characterized by different levels of volatility and persistence of organizational knowledge. We aim to explain differences in generative AI systems’ designs, how they influence broader organizational technology architecture, and evolve. This research extends the Knowledge-Based View (KBV) by incorporating knowledge temporality and offers a framework for strategic generative AI implementation.
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17-Innovation