Short answer: Workplace AI, algorithmic-management software and digital-surveillance systems can intensify monitoring, reduce autonomy and increase pressure on workers. But the available evidence does not establish that CEOs as a group deploy these systems with an intent to “terrorize” employees.
The documented issue is more specific: employers are adopting tools that assign work, track activity and evaluate performance, sometimes using sensitive data. Official reviews and workplace studies find possible security and health benefits alongside credible risks to privacy, mental health, job quality and physical safety.
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What “AI terrorizing workers” gets right—and wrong
The phrase points to a real workplace conflict, but it collapses different technologies and motives into one claim. Three categories should be kept separate.
AI systems
These are tools that use artificial intelligence. The International Labour Organization says AI is reshaping the psychosocial work environment in ways that differ profoundly from traditional management, with emerging risks to workers’ mental and social well-being. Its 30 April 2026 account discusses intrusive surveillance, work intensification, reduced autonomy and concerns about privacy and data use. Read the ILO account.
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Algorithmic management
The OECD uses this broader term for software that automates or supports managerial work. Such systems can be sophisticated without being AI-powered. Calling every automated scheduling or performance tool “AI” overstates what the evidence shows.
Digital surveillance
This includes cameras, microphones, monitoring software, location tracking, apps and wearable devices. Some tools use AI; others do not. The practical question is what data is collected, how it affects decisions and whether a person remains accountable.
What these systems do at work
The OECD groups algorithmic-management functions into instruction, monitoring and evaluation. The same employer may use all three, with very different consequences for workers.
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| Function | Typical uses | Data that may be collected | Possible consequence |
|---|---|---|---|
| Instruction | Assigning jobs, routes, shifts or schedules | Availability, location, qualifications, task status | Work allocation, scheduling and access to assignments |
| Monitoring | Tracking completion, time, speed, communications, location, fatigue or health | Activity logs, GPS, messages, video, biometric or wellness signals | Alerts, interventions, productivity pressure or safety responses |
| Evaluation | Setting targets, ranking performance, rewarding or sanctioning workers, maintaining leaderboards | Output measures, error rates, attendance and behavioral scores | Targets, bonuses, discipline, schedules, pay or continued work |
These categories come from the OECD’s survey of algorithmic management; the publication cautions that the systems counted are not necessarily AI-powered. See the OECD methodology and findings.
How widespread is algorithmic management?
The OECD surveyed more than 6,000 mid-level managers in France, Germany, Italy, Japan, Spain and the United States. It found substantial adoption, but the results are not a global estimate of AI surveillance and cannot be compared as if they measured the same workforce or technology everywhere.
| Surveyed location | Finding | Qualification |
|---|---|---|
| United States | 90% of firms had adopted at least one surveyed algorithmic-management tool | Employer-survey result; tools were not necessarily AI-powered |
| France, Germany, Italy and Spain | 79% average adoption | Average across the four surveyed European countries |
| Japan | 40% of firms had adopted such tools | Employer-survey result using the OECD’s broad definition |
Because the countries, samples and definitions are specific, these percentages should not be combined into one worldwide prevalence figure.
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What workers and studies report about the effects
Benefits stakeholders identify
The U.S. Government Accountability Office’s 2024 review examined 217 public comments from 211 stakeholders submitted to the White House Office of Science and Technology Policy in May and June 2023. Stakeholders described uses in trucking, warehousing, office work and health care. Some said monitoring can improve security, document incidents, allocate work or identify signs of illness. The comments represent stakeholder perspectives, not a representative survey of all workers. Read GAO’s 2024 report.
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The same review records reports of stress, anxiety, depression, fear, lower morale, privacy loss and biased or inaccurate assessments. A dashboard that measures speed or time can turn ordinary variation into a disciplinary signal; location or communications monitoring can also collect personal information beyond what a worker expects. These are reported concerns, not proof that every monitoring system causes each outcome.
Health and safety can move in opposite directions
GAO’s 2025 report reviewed 122 studies published from 2020 through 2024 and interviewed stakeholders. It found that digital surveillance may help identify potential health problems, while pressure to meet productivity targets may push workers to move faster and increase injury risk. The report assesses physical health and safety, mental health and employment opportunities, and notes limits in the underlying studies. It was published on 2 September 2025, publicly released on 24 November, and reissued with revisions on 10 December 2025. Read GAO’s revised 2025 report.
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Sector and country context matters
An ILO–European Commission Joint Research Centre study published 19 February 2024 examined logistics and health-care workplaces in Italy, France, India and South Africa. It identified efficiency gains as well as possible deterioration in job quality and intrusive surveillance. Those findings apply to the sectors and locations studied, not automatically to every industry or country. Read the ILO/JRC study.
Why accountability is a central warning sign
Algorithmic management does not remove management responsibility. In the OECD survey, nearly two-thirds of managers who used these tools reported at least one concern. The most common was unclear accountability when a system makes a wrong decision, followed by difficulty understanding the system’s logic and inadequate protection of worker health.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence cannot prove about CEOs
GAO reviews, the OECD survey and ILO publications describe technologies, adoption, reported effects and governance problems. They do not establish a general intention among CEOs to terrorize employees, nor do they prove that a particular executive intended a specific psychological or physical harm.
Intent, legality and causation are separate questions. A system can produce severe pressure even when executives describe it as a safety or efficiency measure; conversely, a reported harm still requires evidence about the system, the workplace conditions and the individual impact. Avoiding a claim about motive is not a dismissal of workers’ experiences—it is a boundary on what these sources establish.
A practical way to judge whether a system is coercive
Workers, managers and regulators can assess a tool without relying on marketing language. These questions are an analytical checklist, not a universal legal test.
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- Data: Does it collect only task information, or also location, communications, health, fatigue or other sensitive signals?
- Consequences: Can a score change targets, pay, schedules, discipline or access to work?
- Transparency: Do workers know the tool is operating and what factors it uses?
- Contestability: Can a worker see, correct and challenge an erroneous record or decision?
- Human accountability: Is a named person responsible for reviewing exceptions and correcting the system?
- Health safeguards: Does the design reduce hazards, or does it reward speed in ways that encourage unsafe work?
NELP, an advocacy organization, uses the term “bossware” for these surveillance and automated-decision practices and argues for stronger limits and worker protections. Its recommendations are policy advocacy, not findings by a government regulator. Read NELP’s 2025 policy agenda.
What workers can do when monitoring affects them
- Record the facts. Note what is being measured, when monitoring occurs, which decisions follow and any visible errors or health effects.
- Ask for the employer’s explanation. Request the purpose of the system, categories of data collected, retention period, decision rules and the person who reviews disputes.
- Separate policy from practice. Compare written notices with what the app, camera, vehicle device or scheduling platform actually does.
- Seek local help. Worker rights and remedies depend on the country, state or other jurisdiction, the sector and the specific facts. A union, labor agency, privacy authority or qualified employment adviser can explain which rules apply.
- Protect sensitive information. Do not publish coworkers’ personal data or confidential employer records while documenting a concern.
There is no single worldwide answer about whether a particular monitoring practice is lawful or what remedy is available. Any legal conclusion requires jurisdiction-specific, current advice.
How to read the evidence responsibly
The strongest conclusion is not that every AI tool harms workers or that every CEO has the same motive. It is that algorithmic management and digital surveillance are now common enough in several surveyed markets to warrant scrutiny, and credible reviews document both possible benefits and serious risks. The technology’s function, data, consequences and oversight determine what workers actually experience.
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