Paper Number
ICIS2025-1429
Paper Type
Complete
Abstract
Generative AI (GenAI) allows organizations to surface and mobilize untapped organizational knowledge that is embedded in unstructured data—documents, emails, images, or videos—which represent a rich yet underutilized source of insight. In this study, we examine the case of a global telecommunications firm that developed its own large language model, enabling a wide range of GenAI use cases across operations and customer-facing services. Drawing on digital enactment systems as sensitizing theoretical lens, we analyzed how GenAI-based technologies transform organizational scanning, interpretation, and learning. Our findings reveal a reciprocal relationship: while GenAI applications rely on curated unstructured data, they also redefine how such data is captured, contextualized, and mobilized, leading to completely new ways of unstructured data management. Our study is among the first to explore unstructured data as untapped organization knowledge in the context of GenAI, theorizing the shift from traditional, human-centered practices toward recursive, GenAI-enacted data management.
Recommended Citation
Wang, Jingyang and Legner, Christine, "Uncovering Untapped Organizational Knowledge in Unstructured Data: GenAI and the Reconfiguration of Data Management" (2025). ICIS 2025 Proceedings. 6.
https://aisel.aisnet.org/icis2025/general_topic/general_topic/6
Uncovering Untapped Organizational Knowledge in Unstructured Data: GenAI and the Reconfiguration of Data Management
Generative AI (GenAI) allows organizations to surface and mobilize untapped organizational knowledge that is embedded in unstructured data—documents, emails, images, or videos—which represent a rich yet underutilized source of insight. In this study, we examine the case of a global telecommunications firm that developed its own large language model, enabling a wide range of GenAI use cases across operations and customer-facing services. Drawing on digital enactment systems as sensitizing theoretical lens, we analyzed how GenAI-based technologies transform organizational scanning, interpretation, and learning. Our findings reveal a reciprocal relationship: while GenAI applications rely on curated unstructured data, they also redefine how such data is captured, contextualized, and mobilized, leading to completely new ways of unstructured data management. Our study is among the first to explore unstructured data as untapped organization knowledge in the context of GenAI, theorizing the shift from traditional, human-centered practices toward recursive, GenAI-enacted data management.
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