Eliminating Bureaucracy Without Losing Control: An AI-Enabled Framework for Organizational Excellence

Organizations continue to face bureaucratic inefficiencies that limit agility and innovation. This study introduces the Zero Bureaucracy Framework (ZBF) an AI-enabled organizational transformation model that systematically reduces bureaucratic friction while preserving governance and compliance. Rather than proposing new algorithms, the research contributes a structured framework integrating large language model (LLM) capabilities for process discovery, workflow optimization, and adaptive governance. Validated across five HR processes at the United Arab Emirates University, ZBF achieved a 59.6% reduction in process steps, 89.6% faster execution, and 100% compliance, yielding a 359% ROI, a 6.9-month payback, and annual savings of USD 446,500. Optimization gains increased with process complexity (r = 0.89, p < 0.01), showing that complexity enhances, not hinders, efficiency. The findings position ZBF as a replicable, governance-preserving blueprint for post-bureaucratic organizations, demonstrating how AI can reconcile control with agility and scale efficiency-driven innovation across knowledge-intensive environments.