(untitled) (Don Bosco Institute of Technology Delhi Journal of Research, 2026)
The rapid adoption of AI in public administration is altering decision-making processes and service delivery across government institutions. Federal agencies increasingly use AI systems for tasks such as fraud detection, benefit administration and public service management. Although these technologies are offering innovative efficiency, they also raise concerns regarding accountability, transparency and regulatory compliance. Several public organizations still face challenges in ensuring effective human oversight and governance of AI systems. So, how organizational governance mechanisms can strengthen AI-driven compliance effectiveness in government institutions have been examined in this work. The main purpose of the research is to analyze the effect of three organizational factors on AI compliance effectiveness: Human-in-the-Loop (HITL) oversight mechanisms, governance of AI infrastructure and strategic human resource capability for AI oversight. A quantitative research design using a structured survey instrument has been applied in the research. Data are analyzed using PLS-SEM to examine the relationships among the variables and to evaluate the validity and reliability of the measurement model. The findings confirm all three independent variables significantly influence AI compliance effectiveness. Human-in-the-Loop oversight has a positive strong effect, which indicates that structured human supervision improves accountability in algorithmic decision-making. AI governance infrastructure also demonstrates a significant connection with compliance effectiveness. Additionally, Strategic human resource capability strengthens compliance outcomes. The findings suggest that successful AI governance requires strong human oversight, clear institutional policies and skilled personnel who can manage algorithmic risks. Managers and policymakers should therefore develop formal AI governance frameworks, strengthen human supervision systems and invest in workforce training on AI ethics and compliance. Despite its contributions, the study has some limitations since cross-sectional design and reliance on survey responses were used.