The Potential of Artificial Intelligence to Enhance Local Own-Source Revenue in Indonesia - Amilia Hasbullah, S.IP., M.Sc.; Ahmad Hermanto; Guswandi (Program Studi Magister Manajemen Universitas Krisnadwipayana, 2026)
Local Own-Source Revenue (OSR) plays a crucial role in strengthening the fiscal capacity and financial independence of local governments in Indonesia. However, local revenue administration continues to face several challenges, including limited identification of revenue potential, low taxpayer compliance, revenue leakage, and the underutilization of data in decision-making processes. At the same time, the rapid development of Artificial Intelligence (AI) offers new opportunities to improve the efficiency and effectiveness of public administration. Nevertheless, studies specifically examining the role of AI in optimizing local own-source revenue remain limited.This study aims to explore the potential of AI in enhancing Local Own-Source Revenue in Indonesia using a documentary research approach. Relevant scholarly publications, policy documents, and reports from international organizations were analyzed through thematic synthesis to identify the opportunities, mechanisms, and challenges associated with AI implementation in local revenue administration.The findings indicate that AI functions as an enabling technology rather than a direct driver of revenue growth. AI supports OSR optimization through five interrelated mechanisms: revenue potential identification, revenue forecasting, compliance monitoring, taxpayer services, and decision support. These mechanisms improve data quality, administrative efficiency, risk-based monitoring, service delivery, and evidence-based policymaking, ultimately strengthening local revenue administration. However, successful implementation depends on data quality, digital infrastructure, institutional capacity, governance, and regulatory readiness. This study proposes a conceptual framework explaining the indirect relationship between AI and OSR optimization, providing a foundation for future empirical research and practical guidance for local governments pursuing digital transformation in revenue administration.