<![CDATA[This study explores the communication challenges organizations face when adopting AI-driven management systems, a growing trend in digital transformation. The integration of algorithmic management, where AI systems autonomously handle tasks such as scheduling, performance tracking, and decision-making, has led to a shift in traditional communication flows. This research aims to investigate how these systems alter organizational communication, focusing on the implications for employee engagement, feedback mechanisms, and decision-making processes. Using a qualitative approach with multiple case studies across the logistics, e-commerce, and digital services sectors, data was collected through semi-structured interviews, observations, and document analysis. The findings reveal that algorithmic management systems reduce communication to one-way, impersonal directives, resulting in emotional dissonance, alienation, and ambiguity among employees. Informal communication networks emerge as critical spaces for meaning-making and restoring agency. The study concludes that while algorithmic systems optimize efficiency, they must be designed to incorporate dialogic processes and feedback loops to preserve organizational cohesion and trust. This research highlights the need for more communicatively informed approaches to algorithmic management that balance efficiency with relational dynamics.]]>
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