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<p>Many organizations continue to face persistent bureaucratic inefficiencies that constrain agility, increase operational costs, and slow decision-making. This study introduces the Zero Bureaucracy Framework (ZBF), an AI-enabled organizational transformation model designed to systematically reduce administrative complexity while preserving governance and compliance. The framework integrates large language model (LLM) capabilities with structured process discovery, workflow redesign, intelligent automation, and adaptive governance to enable data-driven organizational transformation. The framework was empirically evaluated through a multi-case implementation across five HR administrative processes. The results indicate substantial operational improvements associated with the application of the framework, including a <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(59.6\%\)</EquationSource></InlineEquation> reduction in procedural steps, an <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(89.6\%\)</EquationSource></InlineEquation> reduction in process execution time, and full compliance with institutional policy requirements, alongside an average return on investment of <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(359\%\)</EquationSource></InlineEquation> and estimated annual savings of approximately USD 446,&#xa0;500. Exploratory analysis further suggests that processes with higher structural complexity may yield greater optimization gains, indicating that administrative complexity can represent a latent source of organizational efficiency when addressed through AI-enabled redesign. These findings position the ZBF as a replicable methodology for AI-enabled organizational transformation, providing empirical evidence that AI-enabled process redesign can substantially reduce bureaucratic friction while maintaining governance integrity in knowledge-intensive institutions.</p>

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An AI-enabled framework for reducing administrative bureaucracy in higher education while maintaining governance and compliance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Nazar Zaki,
  • Amna Alneyadi,
  • Mariam Alamoodi,
  • Shaima Alshaali,
  • Fatima Alkaabi,
  • Asia Alhashmi

摘要

Many organizations continue to face persistent bureaucratic inefficiencies that constrain agility, increase operational costs, and slow decision-making. This study introduces the Zero Bureaucracy Framework (ZBF), an AI-enabled organizational transformation model designed to systematically reduce administrative complexity while preserving governance and compliance. The framework integrates large language model (LLM) capabilities with structured process discovery, workflow redesign, intelligent automation, and adaptive governance to enable data-driven organizational transformation. The framework was empirically evaluated through a multi-case implementation across five HR administrative processes. The results indicate substantial operational improvements associated with the application of the framework, including a \(59.6\%\) reduction in procedural steps, an \(89.6\%\) reduction in process execution time, and full compliance with institutional policy requirements, alongside an average return on investment of \(359\%\) and estimated annual savings of approximately USD 446, 500. Exploratory analysis further suggests that processes with higher structural complexity may yield greater optimization gains, indicating that administrative complexity can represent a latent source of organizational efficiency when addressed through AI-enabled redesign. These findings position the ZBF as a replicable methodology for AI-enabled organizational transformation, providing empirical evidence that AI-enabled process redesign can substantially reduce bureaucratic friction while maintaining governance integrity in knowledge-intensive institutions.