enhancing logistical efficiency in public institutions through ai: a managerial framework for regulatory and technological integration

This study investigates regulatory gaps impeding artificial intelligence (ai) integration in public sector logistics, revealing how fragmented legislative frameworks hinder operational efficiency and innovation. through a quantitative cross-sectional survey of 182 legal professionals, public employees, and ai/legal scholars using stratified purposive sampling and validated instruments (cronbach’s α=0.985) we identified statistically significant stakeholder divergences (*p*<0.05) via χ² tests and cramer’s v effect sizes. key findings demonstrate that: (1) legal experts prioritize regulatory clarity deficits (m=4.62), while public staff emphasize institutional resistance (m=4.41); (2) human capital training is systematically undervalued (m=2.57, v=0.26) despite its theoretical importance; and (3) while regulation enhances operational efficiency (m=4.36), it paradoxically inhibits logistical innovation (m=2.48), exposing a critical innovation-governance disconnect. the study’s core contribution, a dynamic institutional alignment framework, resolves this tension through three pillars: human-centered regulatory design integrating legal-technical dimensions, adaptive policy sandboxes synchronized with ai advancement cycles, and stakeholder-specific implementation pathways. by embedding institutional adaptability within global compliance standards (eu ai act, oecd principles), this framework advances ai governance theory and offers public institutions actionable strategies for balancing technological advancement with accountability.