AI systems quietly taking over management decisions across workplaces

Workers may meet the needs of their roles in nuanced ways, but algorithmic systems recognize only what can be measured. This creates a narrowing effect, where activities that do not register within predefined metrics are devalued or ignored altogether. Over time, workers are incentivized to optimize for the system rather than for quality, care, or professional judgment.

Algorithms are increasingly making management decisions once reserved for humans, altering the way work is controlled across warehouses, schools, and public institutions. Automated systems now set performance targets, track behavior, and enforce compliance, often without explanation or appeal. What began as efficiency software has evolved into a new form of workplace authority, shifting power away from workers and embedding control directly into digital systems.

A new study, “A review of Cyberboss: The Rise of Algorithmic Management and the New Struggle for Control at Work,” published in AI & Society, examines this shift. The article argues that algorithmic management represents a structural change in labor governance, not a technical upgrade.  

Algorithmic management and the erosion of human judgment

Algorithmic management fundamentally changes what it means to work under supervision. Rather than relying on human managers to interpret performance, exercise discretion, and adapt expectations to context, algorithmic systems quantify labor through standardized metrics. These systems track output, behavior, and compliance continuously, transforming complex human activity into data points that can be ranked, rewarded, or penalized automatically.

The review highlights how this shift decouples the human experience of work from definitions of success. Workers may meet the needs of their roles in nuanced ways, but algorithmic systems recognize only what can be measured. This creates a narrowing effect, where activities that do not register within predefined metrics are devalued or ignored altogether. Over time, workers are incentivized to optimize for the system rather than for quality, care, or professional judgment.

Gent’s concept of algorithmic management is presented as more than workplace surveillance. It is described as a reconfiguration of managerial power. Algorithms do not merely assist managers; they increasingly replace decision-making functions that once required accountability and explanation. Targets are set dynamically, often beyond human negotiation, and enforcement occurs through automated warnings, penalties, or termination triggers. The review emphasizes that this form of control reduces opportunities for dialogue and resistance, as decisions appear to emerge from neutral systems rather than from identifiable authorities.

The article also stresses that algorithmic management is not limited to private-sector efficiency drives. Its logic reflects broader political and economic priorities that favor speed, scalability, and standardization. By framing performance as a technical problem, organizations can sidestep ethical questions about fairness, workload, and worker well-being. The review argues that this framing masks the human choices embedded in system design, from which data are collected to how success is defined.

From warehouses to classrooms: bureaucracy under algorithmic control

The review extends Gent’s analysis into the realm of bureaucracy. While much public discussion of algorithmic management focuses on gig workers or warehouse employees, the article demonstrates that similar systems are increasingly governing public-sector labor. Civil servants, teachers, and other street-level bureaucrats are now subject to digital oversight tools that track productivity, compliance, and adherence to policy directives.

The review draws on the concept of street-level bureaucracy to explain why this shift is particularly consequential. Street-level bureaucrats have historically exercised discretion in implementing policy, adapting rules to local conditions and individual needs. This discretion is not a flaw but a functional necessity in complex social systems. Algorithmic management, however, seeks to standardize policy implementation by minimizing variation and enforcing uniform compliance.

In education, this transformation is especially visible. Teachers are increasingly evaluated through data-driven systems that monitor lesson delivery, curriculum alignment, and performance indicators. The review argues that such systems reduce academic agency by prioritizing compliance over pedagogical judgment. As algorithmic oversight tightens, educators lose flexibility in responding to classroom dynamics, student needs, and social context.

The article highlights that algorithmic management in bureaucracy does not simply flow downward from political authorities. It can also be used by institutions to centralize power internally, strengthening oversight over frontline workers while insulating decision-makers from accountability. When performance metrics are enforced by automated systems, responsibility becomes diffuse. Workers experience control without a clear locus of authority, making contestation more difficult.

This dynamic mirrors patterns observed in highly automated private-sector workplaces, where workers are subject to constant evaluation without meaningful channels for appeal. The review suggests that as algorithmic management spreads across sectors, it creates a common experience of diminished agency that blurs traditional distinctions between public service and commercial labor.

Resistance, accountability, and the future of work

The article does not present algorithmic management as an inevitable or uncontestable force. A key theme is the possibility of resistance, particularly through collective action. Based on Gent’s conclusions, the article emphasizes that technological systems reflect organizational choices and power relations. As such, they can be reshaped through political struggle rather than passively accepted.

It argues that individual resistance is often ineffective under algorithmic management, as systems are designed to neutralize dissent through automated enforcement. Collective organization, especially through unions, is presented as a crucial counterweight. By challenging how technologies are implemented and governed, workers can push for transparency, limits on automation, and the preservation of human judgment in managerial decisions.

Accountability emerges as a key concern throughout the analysis. Algorithmic systems often operate without clear mechanisms for explanation or redress, undermining principles of responsibility that underpin both labor law and public administration. The review warns that allowing algorithms to make management decisions erodes democratic norms by removing human agents from positions of answerability.

The article also addresses broader implications for policy and governance. As algorithmic management becomes embedded in state institutions, it risks reshaping how public services are delivered. Efficiency gains may come at the cost of equity, responsiveness, and trust. The review stresses that public-sector adoption of algorithmic oversight requires careful scrutiny, as the stakes involve not only workers’ rights but also the quality of services provided to citizens.