Artificial Intelligence-Driven Local Administration: A Synthesis of Key Components and Application Approaches
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Abstract
Artificial Intelligence (AI) is reshaping public administration by enabling data-driven decision-making, service automation, predictive management, and more responsive citizen engagement. This study aimed to (1) examine the role of artificial intelligence in driving a new paradigm shift in local governance, (2) synthesize the key components of artificial intelligence-driven local governance, and (3) develop a conceptual framework for applying artificial intelligence to local administration. A qualitative research approach was employed through a review and synthesis of literature on artificial intelligence, public administration, digital transition, smart governance, and local government management. The collected literature was analyzed using thematic statement analysis to identify recurring concepts, application areas, enabling conditions, governance risks, and implications for local administration. The findings are organized into five interrelated dimensions: data governance, intelligent decision-making, public service automation, citizen engagement, and sustainable governance. Together, these dimensions show how AI can enhance administrative efficiency, policy effectiveness, resource allocation, service accessibility, transparency, responsiveness, and evidence-based governance. The analysis also indicates that successful implementation depends on reliable data systems, organizational readiness, digital capabilities, ethical safeguards, privacy protection, algorithmic decision-making, and human oversight. The proposed framework links AI capability with public value creation, responsible governance, and sustainable local development. It provides a structured basis for policymakers and local officials to plan, evaluate, and govern AI deployment while maintaining accountability and citizen-centered governance.
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