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Workplace technology can track workers, shape their schedules, set performance targets and feed decisions about rewards or sanctions. But the evidence supports a more precise claim than the headline’s accusation: these systems can create serious risks to privacy, autonomy, job quality and health, while the reports do not prove that CEOs generally use AI with the intent to terrorize employees. It also matters that not every monitoring or management tool is AI.
What workplace systems are we talking about?
“Bossware” is a common label for workplace surveillance and automated decision systems, but it can blur distinct technologies. The OECD describes algorithmic management as tools that automate or support managerial work. Some use AI; others do not. Digital surveillance is broader still: it includes ways of collecting information about workers, whether or not AI analyzes it.
The OECD groups algorithmic-management functions into three categories:
- Instruction: assigning tasks, allocating work or setting schedules.
- Monitoring: tracking work completion, time, speed, communications, location, fatigue or health.
- Evaluation: setting targets, assessing performance, maintaining leaderboards, or influencing rewards and sanctions.
A tool’s function, the data it collects and the decisions it can affect are more informative than calling it “AI” on its own. The OECD cautions that the tools in its analysis are not necessarily AI-powered. (OECD, 19 December 2025)
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How widespread is algorithmic management?
An OECD employer survey of more than 6,000 mid-level managers across France, Germany, Italy, Japan, Spain and the United States found substantial—but uneven—adoption. These figures refer to firms using at least one surveyed algorithmic-management tool, not to AI surveillance worldwide.
| Survey population | Adoption reported |
|---|---|
| United States | 90% of surveyed firms had adopted at least one algorithmic-management tool. |
| France, Germany, Italy and Spain | 79% average adoption across the four surveyed European countries. |
| Japan | 40% of surveyed firms had adopted algorithmic-management tools. |
Because the survey uses a broad definition that includes tools not necessarily powered by AI, its figures should not be read as the share of firms using AI to monitor workers. Nor do the country findings amount to a global estimate. (OECD, 19 December 2025)
What does the evidence say about effects on workers?
The evidence describes both possible benefits and serious concerns; it does not establish that every system causes the same outcome. In its 2024 report, the U.S. Government Accountability Office (GAO) reviewed 217 public comments from 211 stakeholders. The comments had been submitted to the White House Office of Science and Technology Policy between May and June 2023. Stakeholders described uses in trucking, warehousing, office work and health care, and raised concerns including stress, anxiety, depression, fear, morale, privacy and bias. They also argued that monitoring can help improve security or prevent illness. These comments capture stakeholder perspectives, not a representative survey of all workers. (GAO, 28 August 2024)
A later GAO review assessed 122 studies published from 2020 through 2024, as well as stakeholder interviews. It found that digital surveillance may affect physical and mental health in positive or negative ways: some systems may help identify potential health problems, while productivity pressure can raise injury risk if workers are pushed to move faster. The review also considered effects on employment opportunities and noted limitations in the studies, so its findings are not proof that a particular tool will harm every worker. (GAO, published 2 September 2025, publicly released 24 November 2025 and reissued with revisions 10 December 2025)
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Research by the International Labour Organization (ILO) and the European Commission’s Joint Research Centre examined algorithmic-management practices in logistics and health care in Italy, France, India and South Africa. It identified potential efficiency benefits alongside risks of deteriorating job quality and intrusive surveillance. Those findings are specific to the sectors and locations studied, not a verdict on every workplace. (ILO and European Commission Joint Research Centre, 19 February 2024)
In a 30 April 2026 news account of an ILO working paper, the organization wrote: “The paper examines how artificial intelligence (AI), which functions in ways profoundly different from traditional management, is reshaping the psychosocial work environment and highlights emerging risks to workers’ mental and social well-being.” The account discusses surveillance, work intensification, reduced autonomy, and privacy or data-use concerns. (ILO, 30 April 2026)
Why can employers see benefits while workers face risks?
The same system can serve an operational purpose and still expose workers to harm. Monitoring may help an employer coordinate work, spot a safety concern or respond to a problem. But when collected data also informs pace, targets, schedules or performance decisions, workers may have less control over their work and more at stake when data is incomplete, misleading or difficult to challenge. Whether a tool is beneficial or harmful depends partly on what it measures, how managers use its outputs and what oversight exists.
Managers themselves report concerns. In the OECD survey, nearly two-thirds of managers who used algorithmic-management tools reported at least one concern about their use. The most commonly reported was unclear accountability when a tool makes a wrong decision, followed by difficulty understanding how a system works and inadequate protection of worker health. These are managers’ reported concerns, not a direct measure of worker outcomes. (OECD, 19 December 2025)
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How can a worker assess a system’s impact?
Whether a specific tool is fair or safe cannot be determined from its label alone. A practical assessment starts with its purpose, the information it collects, the consequences attached to its output and the routes for human oversight. Useful questions include:
- Purpose: Is the system allocating work, monitoring activity, evaluating performance or serving more than one of those functions?
- Data: Does it collect work-related information such as time and task completion, or more sensitive information such as location, communications, health or fatigue?
- Consequences: Can its output affect targets, rewards, sanctions, scheduling, pay or access to work, or is it used only for operational support?
- Transparency and challenge: Do workers know the tool is in use, understand how it influences decisions and have a way to question an outcome?
- Accountability: Is a person responsible for reviewing errors, and are worker health and safety considered when the system is used?
These questions are a way to understand a workplace system, not a statement of universal legal rights. The OECD recommends worker consultation and attention to existing legislation and enforcement when algorithmic-management tools are used. (OECD, 19 December 2025)
Does the evidence prove that CEOs are using AI to terrorize employees?
No. The reports document workplace tools, their uses, stakeholder concerns and possible effects. They do not establish that CEOs as a group intend to terrorize employees, and they do not prove an individual executive’s motive. The term “terrorize” is therefore an editorial framing, not a finding established by these sources.
NELP’s 2025 report, When ‘Bossware’ Manages Workers, presents an advocacy organization’s policy agenda focused on digital surveillance and automated decision-system abuses. It is useful to distinguish that kind of policy analysis from GAO’s review of studies and stakeholder perspectives, or the OECD’s employer survey. (National Employment Law Project, 15 July 2025)
What about workers’ rights or remedies?
There is no single answer that applies across countries, industries and situations. The reports discussed here cover multiple jurisdictions and broad policy questions; they do not establish a universal right or remedy for any particular monitoring practice. A worker seeking advice should consult current information for their own jurisdiction and circumstances, such as a relevant labor or data-protection authority or a qualified local adviser.
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