The Integration of Artificial Intelligence and Machine Learning in Bureaucratic Organizations

Guy Keshet () and Ariel Fuchs
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Guy Keshet: Gaia College
Ariel Fuchs: Gaia College

A chapter in New Challenges of the Global Economy for Business Management, 2025, pp 125-141 from Springer

Abstract: Abstract This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in bureaucratic organizations, examining their potential to lower bureaucratic barriers and enhance operational efficiency. Through a multifaceted approach combining theoretical framework development, literature analysis, and case study examination, we investigate how AI/ML technologies can streamline processes, reduce redundancy, and facilitate adaptive decision-making in traditionally rigid organizational structures. The study analyzes how AI/ML can enhance organizational performance in bureaucratic settings while addressing the challenges and considerations associated with their implementation. We provide insights into the differential impacts of AI/ML integration on various organizational scales by examining case studies across small, large, and complex organizations. Our findings suggest that while AI/ML offer significant potential for transforming bureaucratic processes, successful implementation requires careful consideration of data privacy, change management, and algorithmic fairness. This research contributes to the growing body of literature on organizational innovation. It provides practical insights for managers and policymakers seeking to modernize bureaucratic institutions in an era of rapid technological advancement.

Keywords: Healthcare work dynamics; Communication strategies; Team behavior; Dynamic real-time feedback (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-96-4116-1_8

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DOI: 10.1007/978-981-96-4116-1_8

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