Management & Human Resources | August 28, 2026

Software now assigns shifts, distributes work, monitors pace, recommends ratings and flags people for intervention. The management question is no longer whether an algorithm makes the final decision. It is whether the human around it has enough context, time and authority to make a different one.

By Frank Farnel

Executive summary

• Algorithmic management is not a future-of-work abstraction. An OECD employer survey found at least one tool used to instruct, monitor or evaluate workers in 90% of surveyed U.S. firms and an average of 79% across France, Germany, Italy and Spain. A July 2026 European Commission analysis, using several worker and establishment surveys, offered a more conservative estimate: roughly one-quarter to one-third of EU workers are subject to some form of it.
• The tools are not all artificial intelligence. A scheduling rule, productivity score or peer benchmark can exercise managerial power without using machine learning. Governing only systems labeled “AI” leaves a large operational gap.
• The business case is real. In a study of 5,172 customer-support agents, an AI assistant increased issues resolved per hour by 15% on average and helped newer workers learn faster. But assistance is not the same as control: agents could ignore or edit suggestions and remained responsible for the conversation.
• Risk rises when data move from helping people to ranking, paying, disciplining or dismissing them. Nearly two-thirds of managers using algorithmic-management tools told the OECD they had at least one concern; accountability, explainability and worker health were the most frequently reported.
• “Human in the loop” is inadequate as a governance slogan. Real oversight requires notice, relevant context, trained judgment, protected decision time, authority to override and a credible appeal-and-learning process.

The quiet transfer of managerial power

A manager once allocated the Friday shift. A manager decided whether a slow hour reflected poor performance, a difficult customer, a malfunctioning scanner or a colleague helping someone else. A manager knew that the employee with the weakest weekly metric had also trained two new hires.

In many organizations, those judgments are now distributed across software: workforce-planning platforms, warehouse systems, customer-service dashboards, productivity analytics, talent tools and ordinary business applications that generate recommendations or alerts. The transfer is quiet because no single system announces itself as the new boss. Each one performs a small managerial function. Together, they determine tempo, visibility and consequence.

The latest evidence makes the scale difficult to dismiss. The OECD’s cross-country employer survey, published in December 2025, found that 90% of surveyed U.S. firms and 79% of firms across four European countries had adopted at least one tool to instruct, monitor or evaluate workers. France recorded 81%, Germany and Spain 78%, and Italy 76%. In Japan, where business digitalization was lower, the figure was 40%.

Those rates are firm-level estimates based on managers and a broad definition of the tools. They should not be read as the proportion of individual employees managed by algorithms. A European Commission analytical document dated July 20, 2026, reconciles several surveys and proposes a more conservative range: between one-quarter and one-third of EU workers may be subject to some form of algorithmic management. Depending on the survey, 10% to 29% reported automated allocation of working time, 17% to 22% reported automated task allocation, and roughly one-fifth to one-quarter reported automated monitoring, instruction or performance rating.

The estimates differ because definitions, respondents and units of analysis differ. That is an evidence limitation, not a reason to ignore the phenomenon. The managerial reality is already visible in the functions: deciding who works, what they do, how fast they should do it, whether performance is acceptable and what follows when it is not.

Established fact: multiple recent surveys find algorithmic direction, monitoring and evaluation in ordinary workplaces, although prevalence estimates vary materially.

Analysis: power is shifting even when a person formally signs the decision, because the system often determines which facts are visible, which cases are flagged and what default action appears reasonable.

Hypothesis: organizations will find that the quality of their human override function—not the sophistication of the model alone—best predicts whether employees experience the system as useful coordination or arbitrary control. That proposition requires longitudinal testing.

What algorithmic management is—and what it is not

Algorithmic management is the automated or semi-automated performance of functions conventionally exercised by managers. It can allocate tasks and shifts, issue instructions, measure time and output, compare workers, set targets, recommend rewards, trigger investigations or support decisions about promotion and discipline.

Two distinctions matter.

First, algorithmic management is broader than AI. A deterministic rule that marks every call above a specified duration as an exception may not learn anything. It can still shape behavior more forcefully than a sophisticated predictive model. The Commission’s July 2026 analysis states this plainly: some systems use advanced AI; others rely on traditional software. Their common feature is the managerial function they perform.

Second, not every digital tool that helps a worker is a manager. A writing assistant, diagnostic aid or knowledge-retrieval system may augment a person without allocating, evaluating or disciplining them. The boundary is not always stable. A coaching tool can become a control system if its usage data later inform ratings. A safety sensor can become surveillance if data collected for prevention are repurposed for discipline. Governance therefore has to follow the decision and the data flow, not the vendor’s product category.

This is why the most useful executive question is not, “Does this count as AI?” It is: “What authority does this system exercise over a person’s work?”

Theory: algorithms do not remove control; they redesign it

Organizational control has always combined direction, observation and evaluation. What changes with algorithmic management is its reach and architecture. Software can observe continuously, apply the same metric to thousands of events, compare employees instantly and turn a prediction into the default recommendation before a manager sees the underlying case.

Katherine Kellogg, Melissa Valentine and Angèle Christin describe this terrain through six mechanisms of algorithmic control: employers can use systems to recommend, restrict, record, rate, replace and reward. Their 2020 review in the Academy of Management Annals is valuable because it moves the discussion beyond the black box. A transparent score can still constrain discretion; an accurate recommendation can still create an unfair process; a technically nonbinding output can still become mandatory in practice.

That last problem is automation bias. Faced with a confident score, a time-limited reviewer and an institution that values consistency, the human may approve the recommendation without genuinely reassessing it. The person exists in the workflow but not in the decision.

The managerial issue can be understood through three layers of control:

1. Data control: the system decides what is measured and what disappears. Speed may be visible; mentoring, restraint, emotional labor and exception handling often are not.
2. Decision control: the system determines the default, threshold or ranking around which a manager must reason.
3. Remedy control: the organization decides whether a worker can understand the result, reach a person, correct the data and obtain a new decision.

A system can be excellent at the first layer and dangerous at the third. The model may predict the narrow outcome it was designed to predict while the institution fails to provide a fair route for exceptions. That is not merely a technical failure. It is a failure of management design.

Human oversight must be an operating role

Many policies promise “human oversight.” Too often, that means a supervisor’s name appears at the end of a process. Meaningful oversight is more demanding. It requires six conditions.

Oversight condition	Question for leaders	Evidence it is real	Common failure
Notice	Do people know the system is influencing their work?	Plain-language explanation of purpose, data, outputs and consequences before use	A generic privacy notice or vendor description
Context	Can the reviewer see relevant facts the system cannot?	Access to source data, exceptions, qualitative evidence and the worker’s account	Review limited to the same score and dashboard
Competence	Does the reviewer understand both the job and the tool?	Training in operations, bias, uncertainty, data limits and employment obligations	Technical training without managerial judgment—or vice versa
Time	Can the reviewer pause and investigate?	Protected review time and service levels appropriate to the consequence	Hundreds of alerts and a one-click approval queue
Authority	Can the human change the outcome without informal penalty?	Documented power to override, suspend or escalate; overrides monitored for learning	A theoretical override that harms the manager’s own metrics
Appeal and learning	Can the affected person challenge the result, and does the system improve?	Named contact, timely review, reasoned response, correction and feedback into design	A complaint channel disconnected from system ownership

The European Parliament used remarkably similar language in its resolution adopted on December 17, 2025 and published in the Official Journal on May 6, 2026. It said human oversight should not be a formalistic exercise and that the human contact point must have the competence, training and authority to intervene. The resolution recommends information rights, consultation when systems affect pay, evaluation, scheduling or task allocation, and human review of consequential decisions. It is a recommendation to the Commission, not a binding workplace-management law. But it is a clear signal of the standard policymakers increasingly expect.

The point for executives is practical. If an override role has no workload allocation, no training budget, no access to source information and no protected authority, it is not a control. It is documentation.

Case one: when AI assists rather than judges

A large field study of a generative-AI assistant offers a useful positive case—and a warning against treating every workplace algorithm as the same problem. Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied the staggered introduction of a conversational assistant among 5,172 customer-support agents at a Fortune 500 business-software company. Their peer-reviewed article, published in the Quarterly Journal of Economics in February 2025, found a 15% average increase in issues resolved per hour.

The design matters. The tool monitored customer chats and offered real-time suggestions. Agents remained responsible for the conversation and could ignore or edit the recommendations. The average adherence rate was 38%, evidence that workers did exercise discretion rather than merely transmitting model output.

The gains were uneven. Less experienced and lower-skilled workers improved most; the study reports roughly a 30% increase in issues resolved per hour for those groups, and newer agents moved down the experience curve faster. The authors also found evidence of learning that persisted during system outages. Customer tone improved, and attrition fell, particularly among newer workers—although the attrition analysis was less causally secure than the main productivity result.

There was also a caution. The most experienced, highest-skilled agents saw small gains in speed and small declines in conversation quality. The authors note a longer-term possibility: if expert workers converge on the system’s recommendations and contribute fewer original solutions, the knowledge feeding future models may become less diverse. What looks like standardization today can become capability erosion tomorrow.

This case does not prove that generative AI improves every service job. It concerns one tool, one company and one occupation. It does show a better design principle: use the system to provide timely knowledge while preserving the worker’s ability to interpret, edit and decline. Augmentation creates room for judgment. Management-by-score can remove it.

Case two: similar tools, different institutions, different outcomes

A joint project by the European Commission’s Joint Research Centre and the International Labour Organization examined algorithmic management in logistics and healthcare in France, Italy, India and South Africa. The 2024 report is important because it looks beyond platforms and technology companies into regular workplaces.

The findings resist a simple pro- or anti-technology conclusion. Across the cases, digital tools streamlined processes and generated efficiency or service benefits. In France and Italy, the researchers found a generally positive effect on job quality and no immediate evidence of increased monitoring and surveillance. In South Africa, negative effects on job quality were more evident, and the study found clear evidence of increased worker monitoring. India presented a different, more mixed pattern.

According to the JRC’s synthesis, institutional and regulatory settings helped mediate the effects. That conclusion deserves attention from multinational employers. A global system is not deployed into an empty organization. It encounters local labor relations, consultation rights, professional norms, infrastructure, digital literacy, bargaining power and managerial practice.

The lesson is not that European deployments are automatically safe or that problems elsewhere are inevitable. The sample is qualitative and limited. The lesson is that technology does not carry one fixed employment outcome inside it. Governance conditions shape whether coordination becomes support or surveillance.

This also changes the standard global rollout. “One platform” does not justify one thin control environment. A common technical core may require locally stronger consultation, staffing, explanation or appeal mechanisms. The operating model has to travel with the software.

Case three: Amazon, undisclosed quotas and the cost of invisible standards

In June 2024, California’s Labor Commissioner cited Amazon.com Services LLC $5,901,700 for alleged violations of the state’s Warehouse Quotas law at distribution centers in Moreno Valley and Redlands. The official enforcement announcement said the investigation found 59,017 violations between October 20, 2023 and March 9, 2024.

The law, effective January 1, 2022, requires covered employers to provide written notice of quotas, including the number of tasks to be completed within a defined period and the potential adverse consequences of failing to meet them. California characterized Amazon’s peer-to-peer evaluation system as the kind of quota covered by the law. The state’s concern was not simply that productivity was measured. It was that workers were allegedly subject to an undisclosed standard that could affect their pace and their ability to take breaks.

This should be described accurately: it was a citation and enforcement position by the Labor Commissioner, not a final judicial finding presented here. Amazon disputed the characterization of its system as a fixed quota. That disagreement itself exposes the governance problem. If an employer regards a benchmark as comparative analytics while workers experience it as a consequential production requirement, the practical standard is operating without a shared definition.

For management, notice is not administrative housekeeping. People cannot exercise judgment around a target they cannot identify. Managers cannot evaluate exceptions fairly if they cannot explain the operative standard. Safety and performance become falsely separated when an undisclosed pace metric is embedded in daily work.

California’s response also illustrates a wider movement from privacy-only rules toward workplace-specific governance. Legislators and regulators are beginning to ask what these systems do to time, pace, breaks, health and discipline—not merely whether data were lawfully collected.

Case four: Europe turns “human in control” into a policy agenda

The European debate entered a new phase in 2026. On July 20, the Commission opened a second-stage consultation of social partners on a possible Quality Jobs Act. Its supporting analysis devotes a full section to algorithmic management and AI at work. The document does not pre-announce final legislation, and the available options remain subject to consultation and impact assessment. Still, the direction is material for boards and CHROs.

The Commission reports that 84% of Europeans believe AI at work requires careful management; 74% support a ban on fully automated workplace decision-making. Opposition is even stronger for particular decisions: 78% oppose automatic firing, while 63% view AI-based worker monitoring negatively. These are public attitudes from the Commission’s 2024 Eurobarometer, not evidence that every monitored worker has suffered harm. They are, however, evidence of the legitimacy environment into which employers are deploying these systems.

The policy documents also recognize the benefits: consistency, coordination, safety monitoring, higher-value work and productivity. The debate is not technology versus people. It is about the allocation of decision rights. Who knows what is being measured? Who can challenge a classification? Which decisions must remain human? What level of burden is proportionate for an SME? How do existing rules under data protection, AI, health and safety, information and consultation, and platform work fit together?

Senior management should not wait for every answer to become a statutory clause. A company that cannot inventory its systems, name its decision owners or demonstrate a functioning appeal process is not waiting for legal certainty. It is operating without management control.

The balanced case: what the systems can do well

It would be a mistake to romanticize human management. People are inconsistent, rushed and biased. They overlook patterns, favor familiar colleagues, forget earlier cases and struggle to coordinate complex operations. Properly designed systems can improve scheduling, reduce arbitrary variation, surface safety risks, distribute work more evenly and give employees faster access to knowledge.

In the OECD survey, 60% of managers using algorithmic-management tools perceived an improvement in decision quality. The same share reported a greater need for analytical skills, and 32% reported a greater need for social skills such as listening, conflict resolution, empathy and communication. The tool does not necessarily eliminate the manager. It can increase the value of a manager who knows how to interrogate data and understand people.

Research by EU-OSHA, Eurofound and the JRC makes a similarly useful distinction. Their analysis of digital management technologies associated data analytics used for process improvement with stronger performance, training, job complexity and autonomy. Monitoring-focused use was associated with somewhat lower workplace well-being, while technologies determining pace or monitoring performance were associated with more reported psychosocial risks, particularly time pressure and long or irregular hours.

These are associations, not proof that the technology alone caused every outcome. Better-performing firms may adopt better systems; poorly organized firms may use monitoring to compensate for deeper problems. But the pattern fits a sensible managerial principle: analytics used to improve the process tend to distribute benefits; analytics used primarily to intensify scrutiny concentrate power.

Where well-intentioned governance fails

The OECD found that nearly 90% of surveyed managers said their firm had at least one governance measure in place, such as guidelines, audits, impact assessments, an ethics function, a complaint channel or worker consultation. That sounds reassuring until the second part of the finding: the OECD says more research is needed to understand the content and efficacy of those measures.

A policy is not the same as a control. Four common gaps deserve executive attention.

The inventory gap

Organizations often catalogue formal AI projects but miss algorithmic features embedded in ordinary workforce, scheduling, CRM, security or productivity software. If the inventory begins with the technology label instead of the employment decision, consequential systems remain invisible.

The procurement gap

A vendor can explain model performance without explaining how the tool changes work. Procurement may validate security, privacy and uptime while nobody tests workload, false alerts, accessibility, local consultation duties or the ability to export evidence for an appeal.

The reviewer gap

A manager receives an alert but not the data lineage, uncertainty, comparison group or alternative explanation. The organization can then say that a human reviewed the case even though the human saw no more than the system’s conclusion.

The incentive gap

Managers are told to override when appropriate but are measured on adherence, processing time or uniformity. Employees are invited to challenge decisions but fear that doing so will mark them as difficult. Formal discretion cannot survive incentives that punish its use.

Nearly two-thirds of managers using the tools told the OECD they had at least one concern. The leading concern was unclear accountability when a decision is wrong (28%), followed by inability to follow the system’s logic (27%) and inadequate protection of physical and mental health (27%). Those are not three separate problems. Together they describe an operating system that can act faster than the organization can explain or correct it.

What leaders should do now

1. Inventory decisions, not products. Map every system that influences recruitment, work allocation, scheduling, instructions, monitoring, evaluation, pay, promotion, discipline, safety or dismissal. Record the vendor, data, affected population, decision owner and downstream consequence. Include traditional rules-based software.
2. Separate assistance from authority. For each use, state whether the system informs, recommends, defaults, decides or triggers action. A “recommendation” that managers follow 99% of the time should be governed as a decision, whatever the interface says.
3. Tier by consequence and reversibility. A reversible routing suggestion is not equivalent to a pay adjustment, disciplinary record or termination flag. Increase explanation, testing, human review and appeal as the effect on a person becomes more serious or difficult to reverse.
4. Write the human oversight role like a real job. Specify competence, access, workload, response time, override authority and accountability. Train reviewers on the system’s limits and on the actual work. Protect them when they depart from the model for documented reasons.
5. Consult before configuration hardens. Workers and front-line managers know where data are misleading, where exceptions arise and which incentives will distort behavior. Engage them while objectives and thresholds can still change, not after the contract is signed.
6. Test work design, not only model accuracy. Examine pace, breaks, autonomy, accessibility, workload, distributional effects, false alerts, psychosocial risk and opportunities to learn. A statistically accurate system can still create a harmful operating environment.
7. Build a reasoned appeal. Give the affected person a named human contact, the material basis of the result, a way to add context, a response deadline and a documented outcome. Aggregate appeals and overrides to detect poor data, brittle rules and recurring organizational exceptions.
8. Review the ratchet. Productivity gains often become the next target. Before raising standards, determine whether improvements came from learning, temporary novelty, easier cases, unrecorded work or unsustainable intensity. Do not turn the benefit of assistance into permanent acceleration by default.
9. Put the issue before the board in managerial language. Report which employment decisions are automated or supported, who can stop them, what harms are monitored, what challenges occur and what has changed as a result. Compliance belongs in the report, but accountability should not end there.

Conclusion: keep the manager where judgment lives

The strongest case for algorithmic management is not that machines manage better than people. It is that software can help people see patterns, coordinate complexity and learn faster. The strongest case against it is not that every metric is surveillance. It is that a metric can acquire managerial authority without accepting managerial responsibility.

Organizations should resist two comforts. The first is the belief that a final human click makes a system human-led. The second is the belief that banning automation from consequential decisions would restore some golden age of fair management. Human managers also need standards, evidence and review.

The real work is institutional design: preserving the benefits of consistency and speed while ensuring that context can enter, discretion can operate, harm can be detected and a person can obtain a reasoned correction. That requires more than an ethics statement. It requires a role, a workload, authority and a record.

The algorithm may already be performing part of the manager’s job. Leadership begins by deciding which part it must never be allowed to perform alone.

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Supporting materials

Key evidence

• 90% / 79%: share of surveyed firms using at least one algorithmic-management tool in the United States / the average across France, Germany, Italy and Spain. OECD, December 19, 2025.
• One-quarter to one-third: the European Commission’s conservative estimate of EU workers subject to some form of algorithmic management, synthesized from several surveys with differing definitions. European Commission, July 20, 2026.
• 15%: average increase in issues resolved per hour after access to a generative-AI assistant in a study of 5,172 customer-support agents. Brynjolfsson, Li and Raymond, February 4, 2025.
• Nearly two-thirds: managers using algorithmic-management tools who reported at least one concern; accountability, explainability and worker health led the list. OECD, December 19, 2025.
• $5,901,700: amount of the California Labor Commissioner’s June 2024 citation against Amazon for alleged Warehouse Quotas law violations at two sites; the company disputed that its peer-to-peer system constituted a fixed quota. California Department of Industrial Relations, June 18, 2024.

Glossary

Algorithmic managementAutomated or semi-automated systems that perform or support managerial functions such as direction, monitoring, scheduling, evaluation and discipline.Automated decision-makingA process in which a system makes a decision without meaningful human involvement. Legal definitions and consequences vary by jurisdiction.Automation biasThe tendency to favor a system’s output even when contrary evidence or professional judgment is available.Data lineageThe record of where data originated, how they were transformed and how they contributed to an output or decision.Human oversightActive supervision by a person who can understand the system’s role, evaluate context, intervene, override and correct outcomes.Psychosocial riskA feature of work design or organization—such as excessive pace, low autonomy, unclear expectations or constant monitoring—that may harm psychological or physical health.

References and further reading

Official and institutional sources

1. European Commission. Analytical Document Accompanying the Second-Phase Consultation of Social Partners on Possible EU Action—Quality Jobs Act. Commission Staff Working Document SWD(2026) 239 final, July 20, 2026.
2. European Parliament. Digitalisation, Artificial Intelligence and Algorithmic Management in the Workplace—Shaping the Future of Work. Resolution of December 17, 2025; Official Journal publication May 6, 2026.
3. OECD. How Widespread Is Algorithmic Management in Workplaces? OECD Publishing, December 19, 2025.
4. International Labour Organization and OECD. Compendium of Best Practices for a Human-Centered Development and Use of Artificial Intelligence in the World of Work. G7 technical paper, 2025; Government of Canada publication page updated March 18, 2026.
5. Rani, Uma; Annarosa Pesole; and Ignacio González Vázquez. Algorithmic Management Practices in Regular Workplaces: Case Studies in Logistics and Healthcare. European Commission Joint Research Centre and International Labour Organization, 2024.
6. California Department of Industrial Relations. “Labor Commissioner Cites Amazon Nearly $6 Million for Violating California’s Warehouse Quotas Law.” June 18, 2024.
7. California Division of Labor Standards Enforcement. Warehouse Quotas Frequently Asked Questions. Current guidance on Labor Code sections 2100–2112.

Academic and theoretical works

8. Brynjolfsson, Erik; Danielle Li; and Lindsey Raymond. “Generative AI at Work.” The Quarterly Journal of Economics, vol. 140, no. 2, May 2025, pp. 889–942; published online February 4, 2025.
9. Kellogg, Katherine C.; Melissa A. Valentine; and Angèle Christin. “Algorithms at Work: The New Contested Terrain of Control.” Academy of Management Annals, vol. 14, no. 1, 2020, pp. 366–410.
10. Ravid, Daniel M.; David L. Tomczak; Jerod C. White; and Tara S. Behrend. “EPM 20/20: A Review, Framework, and Research Agenda for Electronic Performance Monitoring.” Journal of Management, vol. 46, no. 1, 2020, pp. 100–126.

Authoritative comparative and workplace sources

11. Urzì Brancati, Maria Cesira; Maurizio Curtarelli; Sara Riso; and Santo Milasi. How Digital Technology Is Reshaping the Art of Management. European Commission Joint Research Centre, EU-OSHA and Eurofound, 2022.
12. Doellgast, Virginia; Shruti Appalla; Dina Ginzburg; Jeonghun Kim; and Wen Li Thian. Global Case Studies of Social Dialogue on AI and Algorithmic Management. ILO Working Paper 144, International Labour Organization, July 10, 2025.
13. González Vázquez, Ignacio; Enrique Fernández-Macías; Sally Wright; and Davide Villani. Digital Monitoring, Algorithmic Management and the Platformisation of Work in Europe. European Commission Joint Research Centre, 2025.

Source and methodology note

This article was researched through August 28, 2026. Priority was given to legislation, official enforcement materials, European Union, OECD and ILO publications, and peer-reviewed research. The OECD employer survey and the EU worker- and establishment-level sources measure different populations and use different definitions; their prevalence figures are therefore presented side by side, not combined. The European Parliament resolution and the Commission’s July 2026 consultation documents indicate policy direction but do not constitute a final new EU law on algorithmic management. The Amazon example is described as an enforcement citation; no final judicial finding is implied. Productivity findings from the customer-support study concern one company and occupation and should not be generalized to all workplaces. Causal language is used only where the cited study design supports it; other relationships are described as associations or interpretations.

Suggested internal links

• How Companies Can Engage Responsibly on AI Regulation — for the external regulatory and board-governance context.
• The Future of Customer Support: AI’s Role — for the distinction between augmentation and replacement in service operations.
• Skills-Based Organization: The Future of Work, Talent Management, and AI Workforce Transformation — for workforce redesign and the capabilities managers will need.
• Authentic Leadership: A Practical Guide to Building Trust and Influence — for the trust, explanation and listening disciplines behind credible human review.
• How Ethical Leadership Builds Workplace Integrity — for accountability when systems and incentives create pressure.

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