The Algorithmic Manager: How AI Is Reshaping Supervision

In December 2025, the European Parliament voted to call for new rules governing algorithmic management at work, with Executive Vice President Roxana Mînzatu confirming on behalf of the European Commission that the issue is already under active review in Brussels. The vote followed the release of an OECD report finding that algorithmic management, defined as the use of software and AI systems to instruct, monitor, and evaluate workers, is now widespread across the countries it surveyed. The OECD data, drawing on the 2024 European Working Conditions Survey, found that 42.3 percent of workers across the EU are affected by some form of algorithmic management, with substantial country variation, from 27 percent in Greece to 70 percent in Denmark.

The figure that should give policymakers pause is not the EU average. It is the range. A worker's exposure to algorithmic supervision currently depends heavily on which country, which sector, and which employer they happen to work for, not on any coherent national or international standard for what algorithmic management should and should not be permitted to do. The technology driving this expansion, AI-powered scheduling, productivity monitoring, automated performance scoring, and increasingly, generative AI systems that draft performance reviews and disciplinary recommendations, has moved from the gig economy platforms where it was first studied into the broader economy, including white-collar and public-sector workplaces where workers and managers alike are often unprepared for what it means to be supervised by software.

From Gig Platforms to the Mainstream Workplace

Algorithmic management entered public discussion through ride-hailing and delivery platforms, where the technology's role was relatively visible: an app assigned tasks, set prices, and rated performance, and workers experienced these functions as directly as they experienced a human dispatcher in a traditional taxi company. Early research on platforms like Uber, examining tools for matching riders to drivers, customer rating systems, and surge pricing, established the basic vocabulary still used to discuss algorithmic management: instruction, monitoring, and evaluation as the three core functions a human manager traditionally performs and that software can now perform instead.

What has changed since that early research is the breadth of contexts in which these three functions now operate through software, often without workers or even managers describing what is happening in those terms. A retail employee whose schedule is generated by software that optimizes for predicted customer traffic is experiencing algorithmic instruction. A warehouse worker whose movements are tracked by handheld scanners that feed into productivity dashboards is experiencing algorithmic monitoring. An office worker whose annual review draws on AI-generated summaries of their email response times, meeting attendance, and document output is experiencing algorithmic evaluation. None of these workers may think of themselves as working under "algorithmic management" in the way a rideshare driver does, but the OECD's three-category framework applies equally to all three.

The OECD report makes an important clarification that is easy to lose in public discussion: algorithmic management tools are not necessarily AI-powered, and this distinction matters for how regulation like the EU AI Act applies. A simple scheduling algorithm that has operated unchanged for a decade is technically a form of algorithmic management but raises different questions than a generative AI system that drafts a worker's performance narrative based on inferred patterns in their digital activity. Regulatory frameworks designed around the older, simpler tools may not adequately address what generative AI adds to the picture: the capacity not just to track and score, but to generate judgments, narratives, and recommendations that carry the appearance of individualized human assessment while being produced at scale by a model.

What the Research Says About Worker Experience

The academic and policy research on algorithmic management's effects on workers has converged on a consistent, if not entirely surprising, set of findings, and the consistency across study designs and contexts is itself notable.

Research synthesized by the AI Now Institute and reflected in the policy proposals now circulating in both the EU and U.S. states identifies hiring, promotion, termination, and discipline as the categories of decision where full automation raises the most serious concerns, precisely because these are the decisions with the most significant consequences for a worker's livelihood and the least tolerance for the kind of errors that AI systems, however capable, continue to make. The "human in the loop" proposals that have emerged from this research, requiring human review of algorithmically-generated decisions in these categories, with a right to reversal if a decision is later found to be incorrect, mirror closely the accountability concerns examined in PPV's analysis of generative AI in the public sector, where the same human-in-the-loop framework faces the same question of whether human review is substantive or perfunctory.

The European Parliament's December 2025 report specifically named continuous digital monitoring, automated performance scoring, and algorithmic scheduling as practices that can intensify work and increase stress, and called for safeguards to protect worker autonomy and physical and mental health. This framing is significant because it moves the policy conversation beyond the question of whether algorithmic management decisions are accurate, the focus of much of the hiring and promotion-related research, toward the question of what continuous algorithmic oversight does to the experience of work itself, independent of whether any individual decision the system makes is correct. A scheduling algorithm that optimizes labor costs with mathematical precision can be accurate in the narrow sense, producing schedules that match predicted demand, while still producing outcomes, unpredictable hours, minimal advance notice, fragmented shifts, that are well-documented to harm worker wellbeing regardless of the algorithm's technical performance.

The U.S. Regulatory Response: Worker Classification as the Entry Point

The United States has approached algorithmic management regulation through a different doctrinal route than the EU, one rooted in existing labor law rather than new AI-specific frameworks, and the 2024 Department of Labor independent contractor rule illustrates this approach directly.

The rule, which clarifies the test for distinguishing employees from independent contractors under the Fair Labor Standards Act, explicitly addressed algorithmic monitoring as a form of employer "control," the key factor in worker classification. Under the rule, technological monitoring, such as GPS tracking of a worker's location and movements, can itself constitute control even without any additional managerial action like instruction or discipline based on that monitoring. This represents a departure from the traditional understanding of control, which required some affirmative managerial action, toward a definition in which mere observation through algorithmic systems is sufficient.

The practical effect of this approach is that algorithmic management regulation in the U.S. is currently being addressed primarily as a question of who counts as an employee, with the attendant wage, benefit, and labor law protections that employee status confers, rather than as a direct regulation of what algorithmic management systems can do regardless of a worker's classification. California's proposed Workplace Technology Accountability Act (AB 1651) represents a more direct approach, more closely resembling the EU's framework, by establishing requirements specific to automated decision systems used in employment regardless of worker classification, including the human-in-the-loop and right-to-reversal provisions discussed above. The divergence between these two regulatory approaches, classification-based versus systems-based, is likely to be a defining feature of how algorithmic management regulation develops in the U.S. over the next several years, and the outcome will significantly affect which workers receive protection: classification-based approaches protect workers based on their employment status, while systems-based approaches protect workers based on the technology applied to them regardless of that status.

What This Means for Workforce Policy

The expansion of algorithmic management has direct implications for the workforce development infrastructure examined throughout PPV's coverage, in ways that are not always immediately obvious.

Workers entering occupations with high rates of algorithmic management exposure, which the OECD data suggests now spans logistics, retail, customer service, and increasingly white-collar administrative roles, need a different kind of preparation than workers entering occupations where supervision remains primarily human. Understanding how to interpret and respond to algorithmically-generated feedback, how to document one's own work in ways that are legible to monitoring systems, and how to exercise the rights to human review and reversal that regulatory frameworks increasingly provide, are becoming practical workplace competencies in their own right, not abstract policy concerns.

For the workforce development system, this suggests that digital literacy curricula, already a priority across the community college and workforce board systems examined in PPV's Education and Human Capital coverage, should explicitly address algorithmic management as a workplace reality workers will encounter, not solely as a technology skill to be mastered for technical occupations. A worker who understands that their schedule is generated by an optimization algorithm, that their performance review may draw on AI-generated summaries of their digital activity, and that they have rights, however unevenly enforced, to request human review of consequential algorithmic decisions, is better equipped to navigate the modern workplace than one who experiences these systems as an opaque and unaccountable feature of employment. As algorithmic management continues its expansion from the gig economy into the mainstream workplace, this kind of preparation will increasingly be a baseline expectation, not a specialized concern.