Abstract: Algorithmic management—the use of automated systems to direct, evaluate, and discipline workers—has expanded from platform-based gig work to traditional employment across multiple sectors. This shift fundamentally alters workplace relationships, introducing automated decision-making processes that can affect trust, autonomy, and wellbeing while offering potential gains in efficiency and consistency. Evidence suggests that algorithmic supervision correlates with complex outcomes including both performance improvements and worker resistance. When implemented with consideration for transparency, fairness, and meaningful human oversight, algorithmic tools may augment rather than replace human judgment. This article examines the organizational leadership challenge of governing workplaces where AI supervises humans, synthesizing verified research on prevalence and consequences, then outlining practical approaches including explainability frameworks, participatory design, human-in-the-loop architectures, and procedural justice mechanisms. Leaders navigating this transition must deliberately design sociotechnical systems that balance operational objectives with worker dignity and voice.
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