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Sometimes—but not yet for everyone. Many workers surveyed by the OECD said AI improved their performance and enjoyment, but those reports do not prove that AI has raised productivity, pay, or job quality across the workforce. Whether AI creates a better working life depends on how employers deploy it, involve workers, and share its benefits.
What the evidence says—and who it covers
The most direct evidence on workers’ experience comes from OECD surveys in finance and manufacturing. The OECD’s 2024 summary reports that four in five surveyed workers said AI improved their performance at work, while three in five said it increased their enjoyment. Those are workers’ perceptions, not measured productivity results or proof that AI caused the improvements.
The underlying OECD survey, published in 2023, collected data in early 2022 from 5,334 workers and 2,053 firms in Austria, Canada, France, Germany, Ireland, the United Kingdom, and the United States. Its findings do not represent every occupation or country—and they predate the widespread workplace use of later generations of generative AI tools. The OECD’s report, The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers, also describes concerns about work intensity, job stability, wages, and possible job loss alongside its broadly positive findings among AI users.
A separate statistic is often cited as evidence of AI’s workplace reach: Microsoft and LinkedIn reported in their 2024 Work Trend Index that 75% of surveyed knowledge workers used AI at work. The survey covered 31,000 people across 31 countries and was supplemented with other company data. It measures reported use among a different group, not whether AI improved their working lives. It is a dated, company-sponsored statistic, not a current universal adoption rate.
| Evidence | What it found | What it can—and cannot—show |
|---|---|---|
| OECD worker and employer surveys; data collected in early 2022, findings published in 2023 and summarized in 2024 | Four in five surveyed workers said AI improved their performance; three in five said it increased their enjoyment. | Reports worker perceptions in finance and manufacturing across seven countries. It does not establish measured productivity gains or describe today’s generative-AI use across all jobs. |
| Microsoft and LinkedIn, 2024 Work Trend Index | 75% of surveyed knowledge workers said they used AI at work. | Reports use in a company-sponsored survey of 31,000 people across 31 countries, supplemented by other company data. It does not measure improved job quality or represent all workers. |
Why reported time savings do not settle the productivity question
Saving time on a task can make work feel easier or leave room for other responsibilities. But it does not automatically mean a worker, team, or business produces more valuable output—or that employees receive higher pay or more secure jobs.
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The International Labour Organization’s 1 June 2026 review, The impact of GenAI on jobs, productivity and work organization: a review of the empirical evidence, finds that reported time savings amount to a few per cent of working hours in the evidence it reviews. It says those savings have not yet translated broadly into higher measured output, earnings, or employment. The review draws on experiments, firm-level data, platform studies, and worker surveys; it characterizes gains as real but uneven and often unverified. Its summary is a caution against treating a reported productivity boost as proof of economy-wide improvement.
That distinction matters to workers as well as employers. If AI saves time but the saved time becomes a faster pace, more monitoring, or extra workload, the change may not feel like an improvement. If it reduces tedious tasks while preserving workers’ discretion and giving them a say in how the saved capacity is used, it may contribute to a better day. Those are different workplace outcomes, even if both are described as efficiency.
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A better working life is about more than speed
AI’s effects should be judged across several parts of work, not by task speed or adoption alone. OECD and ILO materials raise questions about:
- Workload and pace: Does AI reduce repetitive effort, or does it increase targets, work intensity, or the amount expected in a day?
- Autonomy and oversight: Can workers understand, challenge, or override AI-supported decisions that affect their tasks or treatment?
- Job quality and well-being: What happens to satisfaction, fairness, physical and mental health, and coordination between colleagues?
- Security and pay: Are jobs, wages, and access to opportunities maintained as tasks change, and who benefits from any gains?
- Privacy and data use: What information is collected about workers, how is it used, and can they understand or contest its use?
- Access and distribution: Do effects differ by occupation, age, gender, or income context, and who is included in training and new opportunities?
The available evidence does not establish one universal scorecard or a single answer for every workplace. These questions help separate a genuinely better job from one that is merely faster, more closely monitored, or reorganized around the technology.
AI exposure does not mean a job will disappear
Concern about job loss is understandable, but exposure to AI is not the same as a role being automated away. The ILO’s 2025 paper, Artificial intelligence adoption and its impact on jobs, says AI is more likely in many roles to augment human capabilities and enhance productivity than to produce widespread automation. It also stresses that impacts vary by occupation and demographic group; this broad synthesis is not a guarantee about any particular job.
The ILO paper also points to algorithmic management and the data labor that supports AI systems. These issues broaden the question beyond whether a tool replaces a task: AI can change how work is assigned, evaluated, or controlled, and can affect workers whose contributions are less visible. A sound assessment therefore considers both job quantity and the quality and conditions of the work that remains or emerges.
Workplace decisions can shape the outcome
The OECD’s AI and work overview reports that training and worker consultation are associated with better worker outcomes in its surveys. That is an association, not proof that either measure alone causes the improvement. Still, it points to choices employers can make when introducing AI: prepare people for changing skill requirements and include affected workers in decisions about how tools are used.
For workers trying to judge an AI rollout, useful questions include:
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- What tasks will the tool assist with, and which decisions will remain with people?
- What training is available, and is there time to learn the tool as part of paid work?
- How will the employer measure success—worker experience and job quality as well as speed or output?
- What worker data will be collected, who can access it, and how can an employee challenge an AI-supported decision?
- How will the organization handle errors, uneven effects across roles, or changes to workload and job security?
These questions do not guarantee a positive outcome, but they make clear what a rollout should be accountable for. A rise in AI use is not, by itself, evidence that workers are better off.
So, is AI creating a better working life for the future?
It can help some workers do their jobs and enjoy them more, and AI may augment human capabilities in many roles. But the current evidence does not establish that it has improved working life overall: reported benefits are not the same as measured gains in output, earnings, or employment, and the effects are uneven. The future will depend not just on what AI can do, but on how workplaces manage workload, job quality, privacy, worker agency, training, consultation, and the distribution of gains.
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