Algorithmic Management in the Workplace: The Effects of AI-Based Monitoring on Employee Productivity, Autonomy, and Trust

Abstract

Algorithmic management increasingly transfers managerial activities such as task allocation, scheduling, performance measurement, monitoring, feedback, and disciplinary recommendations from human supervisors to software systems. Advocates argue that data-driven management can improve organizational efficiency, consistency, and decision quality. Critics contend that intensive algorithmic monitoring can reduce employee autonomy, increase stress, weaken trust, and encourage workers to optimize visible metrics rather than substantive performance. This paper examines the relationship between algorithmic management and productivity, autonomy, trust, and worker well-being. Meta-analytic evidence provides little support for the proposition that electronic monitoring automatically improves performance, while showing more consistent associations with stress and somewhat lower job satisfaction. Recent measurement research on algorithmic management similarly associates more complete algorithmic takeover of managerial functions with faster work pace, reduced autonomy, and psychological irritation. These findings do not imply that algorithmic management is inherently harmful. Systems that reduce administrative burden, provide useful information, improve scheduling, or make managerial processes more consistent can create value. Outcomes depend substantially on transparency, proportionality, perceived fairness, contestability, and meaningful human oversight. The paper concludes that organizations should distinguish between systems designed to augment employees and systems designed primarily to intensify surveillance. Sustainable productivity is more likely when algorithms improve coordination while preserving professional discretion and procedural fairness.