Knowledge Transformation through AI in Public Institutions
Keywords:
Artificial Intelligence; Public Administration; Knowledge Management; Governance.Abstract
Artificial Intelligence (AI) is increasingly integrated into public administration as governments seek to enhance decision-making, efficiency, and responsiveness in complex environments. In public security institutions within developing countries, however, technological adoption is shaped by hierarchical governance structures, resource constraints, and institutional instability. This study aims to examine how AI transforms knowledge management practices and how institutional context conditions the sustainability of AI-based knowledge systems in public security governance. The research adopts a qualitative case study design based exclusively on secondary data, including academic literature, institutional reports, and policy documents related to AI implementation in public administration. An analytical framework integrating knowledge management theory and dynamic capabilities theory guides the interpretation of how AI influences knowledge creation, structuring, dissemination, and application. Thematic content analysis is used to identify patterns of institutional mediation, governance constraints, and adaptive learning processes within a developing country security context. Particular attention is given to how hierarchical authority, confidentiality regimes, resource limitations, and digital skill gaps shape the durability of AI-driven transformation. The findings indicate that AI enhances analytical capacity and formalizes knowledge routines, but its long-term institutionalization depends on governance stability, leadership continuity, and sustained competence development. The study concludes that AI-driven knowledge transformation in public security administration is a context-dependent institutional process rather than a purely technological upgrade. By integrating technological, organizational, and governance perspectives, this research contributes to advancing theoretical understanding of AI-enabled transformation in public sector settings.
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