The Impact of Generative AI on Knowledge Management Processes within Organizations

Abstract

The rapid emergence of Generative Artificial Intelligence (GenAI) signifies a profound paradigm shift in how organizations manage, create, disseminate, and apply knowledge. A brief review of the contemporary literature reveals that while GenAI offers significant opportunities to enhance knowledge management (KM) processes through its capabilities in content generation, cognitive task automation, and complex data processing, its effective integration within organizational contexts remains a critical challenge. This is largely due to an underdeveloped understanding of GenAI's interdependencies with foundational disciplines such as Information Management (IM), Data Management (DM), and Data Governance (DG), which are paramount for ensuring data quality, operational optimization, and strategic alignment. Furthermore, a notable gap exists in the academic discourse regarding a comprehensive, sociotechnical informed framework that addresses the human, organizational, and ethical dimensions of GenAI deployment in knowledge-intensive environments. Recognizing this critical need, this dissertation adopts a Design Science Research (DSR) paradigm to develop a conceptual framework: the Generative AI-Enhanced Knowledge Management Framework (GAKMF). This framework captures the multifaceted impact of GenAI on core KM processes - specifically, knowledge creation, storage and retrieval, transfer and sharing, and application - while rigorously situating this transformation within the interconnected domains of IM, DM, and DG. The research involved a critical literature review to identify key concepts and relationships, followed by the construction of detailed conceptual models for each discipline and their interactions with GenAI. The resulting GAKMF is structured across five interdependent levels: Organization Strategic Definition, Data and Ethical Preparation, GenAI System in a Sociotechnical Context, Knowledge Management Processes Enhanced by GenAI, and Organizational Expected Results. The framework's construction and initial evaluation, informed by expert validation through a focus group, underscore its relevance, rigor, and applicability for both researchers and practitioners. It is concluded that the GAKMF provides a structured, interdisciplinary lens for organizations to strategically and ethically integrate GenAI, fostering improved performance, accelerated innovation, and human-centric workforce empowerment. This analysis has become particularly relevant as it addresses the current lack of integrated conceptual representations for GenAI's role within complex knowledge environments. Finally, this work offers foundational insights for the future design of responsible GenAI implementation strategies, emphasizing the continuous connection between technological advancements and human-organizational factors.