The Impact of Digital Transformation and Artificial Intelligence on Bureaucratic Culture Between Efficiency and Discretion

This study examines the impact of digital transformation and artificial intelligence (AI) on bureaucratic culture in the public service sector, especially related to employee efficiency and discretion in decision making. This study aims to understand how the application of AI determines the flexibility, accountability, and effectiveness of bureaucracy in Gorontalo Province, as well as explore strategies that can be implemented to maintain a balance between technological innovation and administrative policy. Academically, this research contributes to filling the literature gap related to technology-based bureaucratic transformation, especially in the context of developing countries. The practical importance of this study lies in the policy recommendations provided to improve bureaucratic adaptability to technological changes while maintaining flexibility in decision-making. The research method used is a qualitative approach with in-depth interviews with employees at various levels of hierarchy in regional apparatus organizations in Gorontalo that have adopted AI. The results show that the application of AI has improved administrative efficiency and accountability in decision-making, but on the other hand, limits the discretionary space of employees in situations that require contextual considerations. The resulting theoretical novelty is the concept of algorithmic bureaucracy, where AI plays a key element in the bureaucratic decision-making process that has the potential to shift the discretionary function of employees to be more automated. This study recommends that in bureaucratic transformation there are 2 important things that need to be implemented, namely: 1) Strengthening digital capabilities and bureaucratic adaptability. 2) Algorithmic supervision technology governance and policy. As for the strengths of the research where the relevant study introduces new concepts regarding bureaucratic transformation, the weaknesses of the research are limited to geography, the risk of subjective bias, and the lack of quantitative validation of the concepts introduced.