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Yes, AI can make people more productive—but only for particular tasks, workers and implementations. A 2025 study of 5,172 customer-support agents found 15% more issues resolved per hour after a generative-AI assistant was introduced. Other evidence finds workers reporting faster performance and more enjoyment. Those results do not establish a universal productivity boom, a rise in employee engagement, or an economy-wide increase in output. They also coexist with risks involving work intensity, privacy, inequality, autonomy, surveillance and job security.
The practical question for an employer is therefore not whether “AI” works in the abstract. It is which tool changes which work, who receives the benefit, what happens to quality and discretion, and whether workers have training, consultation and meaningful safeguards.
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What the evidence can—and cannot—say
AI at work covers several different technologies: generative systems that draft or summarize text, recommendation engines that support decisions, software that schedules or allocates work, and monitoring systems that score activity. A result from one category should not be treated as evidence for all the others. Algorithmic-management software, for example, can include tools that are not powered by AI at all.
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| Evidence type | What it can show | What it cannot establish by itself |
|---|---|---|
| Operational data or controlled experiment | Change in speed, output or quality for a defined task and group | A universal gain in every occupation, firm or economy |
| Worker survey | Perceived performance, enjoyment, stress or satisfaction | Causation or a standardized engagement score |
| Employer survey | Reported adoption, governance and perceived risks | Whether a measure works in practice or protects every worker |
| Policy or evidence review | Patterns across studies and conditions that may affect scaling | A precise forecast for an individual workplace |
| Stakeholder comments | Concerns and experiences raised by participating organizations or workers | Representative prevalence or causal effects |
Keeping those distinctions visible prevents two common errors: treating a time saving as firm-level productivity, and treating enjoyment in a survey as proof that engagement improved.
Measured productivity gains are real, but highly uneven
A customer-support experiment with a 15% average gain
The clearest numerical result in the available evidence comes from “Generative AI at Work,” published in the Quarterly Journal of Economics in May 2025. Researchers analyzed 5,172 customer-support agents who used a generative-AI conversational assistant. Issues resolved per hour rose by an average of 15%.
That average hides an important distributional pattern. Less-experienced and lower-skilled agents improved both speed and quality. The most experienced and highest-skilled agents had small speed gains but small declines in quality. The researchers also found evidence of worker learning and improved English fluency, particularly among international agents. The finding is therefore best read as a result for this customer-support deployment, not as a 15% raise for every profession or company.
Task-level gains in the wider evidence
An International Labour Organization review published in June 2026 synthesized experiments, firm data, platform studies, and worker and firm surveys from Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom and the United States. Across its evidence base, it summarized typical task-level productivity gains in a range of 10% to 70%.
That range describes tasks under particular conditions, not a forecast for a typical employee. Results depend on whether the work is text-intensive, how structured the task is, the quality of the data, the system’s accuracy, the worker’s experience and the way the workflow is redesigned. The ILO characterized gains as real but uneven and often unverified. In several studies, workers reported saving a few percent of their working time, yet those savings had not translated into higher measured output, earnings or employment.
Why individual time savings do not automatically raise company productivity
A worker who finishes a draft in half the time has created capacity, not necessarily additional economic output. The saved time may be absorbed by more assignments, waiting for approvals, rework, meetings or demand that does not exist. Quality checks can also consume the time that an initial AI draft appeared to save.
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The ILO’s May 2026 brief, The Aggregation Paradox of AI, reports no clear AI-driven productivity growth yet in official sectoral or macroeconomic statistics. Firm-level evidence is mixed. Measurable effects are concentrated in larger, digitally advanced enterprises, while many firms report little impact beyond pilot projects. Broader diffusion, complementary investment in skills and work reorganization, and institutional conditions all affect whether a local improvement scales.
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A useful evaluation therefore follows the chain from task to economy:
- Task: Did completion time, error rate or quality change?
- Worker: Did the person handle more valuable work, learn, or experience greater pressure?
- Team and firm: Did throughput, customer outcomes, costs or revenue change after accounting for review and implementation work?
- Sector and economy: Did the change persist and spread widely enough to appear in aggregate statistics?
Skipping the middle steps turns a local demonstration into an unsupported macroeconomic claim.
What workers report about performance and enjoyment
The OECD’s 2024 paper, Using AI in the workplace: Opportunities, risks and policy responses, found that four in five surveyed workers said AI improved their performance at work. Three in five said it increased their enjoyment of work.
These are important indicators of reported experience, but they are not direct output measurements. They are also not a validated, cross-industry employee-engagement score. Enjoyment can overlap with morale or job satisfaction, while engagement commonly includes commitment, energy and willingness to contribute. A worker may enjoy an AI tool that removes repetitive tasks while still distrusting the employer’s data practices or fearing job loss.
The survey figures do not prove that AI caused the changes, either. People who volunteer to use a tool may already be more confident, better supported or assigned to work where automation fits. Perceptions can also change before an organization has measured quality, workload or retention. The strongest interpretation is that many surveyed workers perceive benefits, not that every implementation improves engagement.
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How productivity can coexist with worse job quality
Work intensification
If an employer treats every saved minute as capacity for another assignment, AI can increase the pace of work rather than reduce effort. Targets may rise after a tool is introduced, leaving workers with less recovery time even when output increases. A faster system can therefore improve a dashboard metric while making the job feel more pressured.
Privacy and surveillance
The U.S. Government Accountability Office’s August 2024 report, Digital Surveillance of Workers: Tools, Uses, and Stakeholder Perspectives, reviewed 217 comments from 211 stakeholders submitted to the White House Office of Science and Technology Policy in May and June 2023. Commenters described cameras, microphones, computer-monitoring software, geolocation, tracking applications and wearable devices.
Because this was a review of submitted comments rather than a representative survey or causal experiment, it cannot show how common each experience is. It does show the range of concerns stakeholders raised. Workers and unions described stress, lower morale, distrust, privacy concerns and possible bias, including failures to account for disability-related needs. Some stakeholders questioned whether surveillance measures productivity accurately. Other commenters saw potential productivity benefits. The disagreement itself is a reminder that measurement design matters.
Autonomy and accountability
When software recommends schedules, evaluates performance or allocates tasks, workers may have less control over how work is done. If a recommendation is wrong, responsibility can become unclear: the worker may be blamed for following it, while the manager may defer to a system that cannot explain its reasoning.
An OECD 2025 report on algorithmic management found that nearly two-thirds of managers using such tools reported at least one trustworthiness concern. The most frequently reported concerns were unclear accountability for wrong decisions (28%), difficulty following the logic of decisions or recommendations (27%), and inadequate protection of workers’ physical and mental health (27%). These statistics concern algorithmic management as a category; the tools are not necessarily AI-powered, so they should not be presented as AI-specific prevalence rates.
Inequality and job stability
Benefits can be distributed unevenly. In the customer-support study, less-experienced workers gained more than the most experienced workers, while quality declined slightly for the highest-skilled group. In another setting, a tool might increase the value of specialized expertise or reduce demand for it. The same technology can help one group, intensify another group’s work and change bargaining power across the organization.
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The OECD estimates that occupations at highest risk of automation, when AI is taken into account, represent about 27% of employment in OECD countries. “Risk” or “exposure” does not mean that 27% of jobs will disappear. Jobs contain multiple tasks, and organizations may redeploy workers, redesign roles or create new work. The figure signals where disruption may be concentrated, not a timetable for layoffs.
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There is no single cross-industry causal estimate in the evidence reviewed that answers this question. The available measures are narrower: reported enjoyment, perceived performance, job satisfaction in particular studies, autonomy, stress, morale and trust. They should be named rather than collapsed into one claim about engagement.
A tool is more likely to support a good employee experience when it removes low-value repetition, gives workers useful assistance and leaves room for professional judgment. The same tool can undermine that experience when it is paired with unrealistic quotas, opaque scoring or constant monitoring. Engagement is therefore shaped by implementation and management choices as much as by model capability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What responsible implementation looks like
Define the outcome before choosing the tool
Specify whether the objective is shorter handling time, fewer errors, better customer outcomes, reduced administrative burden, learning or something else. Record a baseline and decide how quality will be checked. A speed metric without a quality metric invites harmful optimization.
Map who does what after deployment
List the tasks the system performs, the decisions that remain with people, the data it receives and the situations in which it can fail. Identify which workers gain time, which workers inherit review work and which roles face changed performance expectations. Test the workflow with different experience levels rather than relying on an average.
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Training should cover how to use the system, how to verify outputs, how to protect confidential information and how to escalate an unsafe or biased result. The OECD’s 2024 analysis associates training with better worker outcomes, but that association is not a guarantee. Training cannot compensate for impossible targets or an undisclosed surveillance program.
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Consult affected workers before targets change
The OECD’s 2025 analysis argues that consultation gives workers a channel to raise concerns and helps address changes to staffing, wages, work organization and training. It can also support acceptance of new technology. Consultation is meaningful when workers can influence design, evaluation and safeguards—not merely receive notice after launch.
Govern data, decisions and appeals
Explain what data is collected, why it is needed, who can access it, how long it is retained and whether it is used for individual evaluation. Assign a human decision-maker for consequential actions. Provide a way to challenge an inaccurate score, recommendation or automated decision, with records showing how the issue was resolved.
Measure distribution, not only averages
Track output and quality by experience level, role, location and other relevant groups. Monitor workload, overtime, absence, turnover, reported stress and autonomy alongside productivity. An average gain can conceal a quality decline for experts or a heavier burden for workers who must correct system errors.
How much governance do employers currently report?
Nearly 90% of managers surveyed for the OECD’s 2025 report said their firms had at least one measure to govern algorithmic-management tools. Guidelines were among the most common measures, and almost two-thirds of firms reported worker consultation.
Those figures describe reported practices, not verified effectiveness. The report says more work is needed to determine which governance measures work. They also cover algorithmic management broadly, including tools that may not use AI. A policy document on a shelf is not evidence that workers understand the system, can contest an outcome or experience less stress.
A decision framework for employers
| Question | Evidence to collect | Warning sign |
|---|---|---|
| Does the tool improve the target task? | Baseline and post-launch speed, quality and error data | Only speed rises, while rework or complaints are unmeasured |
| Who benefits? | Results by experience, skill, role and other relevant groups | An average gain hides losses for a particular group |
| What happens to saved time? | Workload, staffing, customer demand and task allocation | Every saving becomes a higher quota automatically |
| Can workers exercise judgment? | Override rights, escalation routes and appeal records | People are held responsible for recommendations they cannot question |
| Is data use trusted? | Collection limits, retention, access and worker understanding | Monitoring expands without a clear purpose or notice |
| Will the change scale? | Adoption costs, workflow redesign, skills and sustained firm results | A successful pilot is treated as proof of economy-wide growth |
What workers should ask
- What task is the system meant to change, and how will quality be measured?
- Will the tool be used to set quotas, assess performance or make employment decisions?
- What information is collected about me, who sees it and how long is it kept?
- Can I override or challenge an output, and who makes the final decision?
- What training is provided, and what happens when the system is wrong?
- How will productivity gains affect staffing, workload, pay and time available for learning?
Clear answers do not eliminate risk, but they make it possible to distinguish an assistive tool from a monitoring regime and to identify where accountability sits.
The bottom line
Current evidence supports a conditional conclusion. AI produces measurable productivity gains for some tasks and workers, including a 15% average increase in issues resolved per hour in one large customer-support deployment and a wider 10%–70% range of task-level gains synthesized by the ILO. Those results are uneven, and they have not yet produced clear AI-driven growth in official aggregate statistics.
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Workers often report better performance and enjoyment, but those perceptions are not the same as measured output or employee engagement. At the same time, faster work can be paired with intensified targets, intrusive data collection, opaque decisions, reduced autonomy and unequal job risks. Training, consultation, transparent data rules, human accountability and outcome measures that include quality and worker well-being are evidence-informed safeguards. They improve the odds of a beneficial deployment; they do not guarantee one.
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