From traditional to intelligent bureaucracy: Integrating Al and machine learning into public sector management - Zhang, Mengzhong; Shahid, Rumana (American Journal of Social Sciences and Humanities, 2026)
This research examines the transformation of traditional public sector bureaucracies into intelligent, technology-driven organizations through the integration of Artificial Intelligence (AI) and Machine Learning (ML). Traditional bureaucracies, characterized by hierarchical structures, rigid procedures, and slow decision-making, often face challenges in delivering efficient and responsive public services. By leveraging AI and ML, public sector management can enhance operational efficiency, enable data-driven decision-making, and improve service delivery while reducing costs and administrative bottlenecks. The paper has adopted a qualitative research design based on a Systematic Literature Review (SLR) to address how Artificial Intelligence (AI) and Machine Learning (ML) could be used to change the traditional forms of bureaucracies into intelligent, evidence-based, and responsive forms of government. The study explores key applications of AI and ML, including predictive analytics, automated workflow management, and policy simulation, highlighting their potential to foster transparency, accountability, and citizen-centric governance. It also addresses challenges associated with adoption, such as ethical concerns, data privacy, algorithmic bias, and resistance to change among public employees. Findings indicate that successful integration requires a balanced approach combining technological innovation, human oversight, and institutional reform. The research concludes that intelligent bureaucracies have the potential to create more adaptive, effective, and inclusive public administration systems. Future studies are recommended to investigate empirical outcomes, sector-specific applications, and ethical frameworks, ensuring that AI-driven governance maximizes benefits while mitigating risks.