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AI can help people complete some work faster or produce more, but there is no reliable universal percentage for company-wide savings—and exposure to AI does not mean a job will disappear. Experiments show gains on particular tasks; surveys show mixed, self-reported business effects; and current evidence does not establish a single net effect on jobs or costs across the economy.
Does AI improve productivity?
Sometimes, especially on tasks with clear objectives. But a gain measured on one task is not a forecast for every worker, occupation, or company. Productivity means more output or better-quality output for a given amount of input; time saved is related, but it is not the same outcome.
| Study | Setting and measure | Finding | What it does—and does not—show |
|---|---|---|---|
| Shakked Noy and Whitney Zhang, Stanford SCALE Initiative, 2023 | Preregistered experiment with 453 college-educated professionals completing midlevel writing tasks; completion time and output quality were measured. | ChatGPT reduced average completion time by 40% and increased output quality by 18%. | Evidence of a substantial gain on the assigned writing tasks, not an estimate of the productivity effect across all jobs or firms. |
| Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, NBER, 2023; published in the Quarterly Journal of Economics in 2025 | Study of 5,179 customer-support agents using a generative-AI assistant; issues resolved per hour were measured. | Agents resolved 14% more issues per hour on average. The measured gain was 34% for novice and lower-skilled agents; experienced, highly skilled agents saw minimal measured effect. | Shows that outcomes can differ by worker experience within one support operation; it does not establish the same effect in other workplaces. |
The OECD’s review of generative AI and productivity also finds that gains are especially pronounced on well-defined tasks with clear objectives. Whether people can use AI effectively depends in part on their understanding of the tool, trust, skills, and their organization’s ability to integrate it into work. Results from writing or customer support therefore should not be multiplied across a company’s labor costs as if every activity received the same boost.
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It can reduce the time or labor needed for some work, but the evidence here does not support a universal return on investment or a standard percentage of net cost savings. Potential applications include writing, summarizing, editing, translation, coding, marketing content, sales, supply-chain management, and customer service. Those are possible routes to efficiency, not proof that a business will spend less overall.
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A company’s net result depends on what the saved time is used for and what it costs to put AI into practice. Tool expenses, training, human review, security, and changes to workflows can all affect whether an apparent efficiency becomes a financial saving. The distinction is important: faster task completion is a gross efficiency; lower total costs after adoption and oversight is a net saving.
What small-business surveys report
An OECD representative survey of more than 5,000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea, and the United Kingdom was conducted in late 2024 and published in 2025. Among SMEs using generative AI, 65% said it improved employee performance, about one third said it reduced workload, and 14% said reliance on external contractors fell. These are firms’ reported experiences, not independently audited causal estimates or a quantified common rate of realized savings.
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The survey offers evidence of operational changes, but it does not show that every adopter saved money. Reduced workload or contractor reliance may contribute to lower costs, yet the findings do not calculate net savings after tool, training, review, security, and integration costs.
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Not automatically. A field experiment by Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica, and Christopher T. Stanton across 66 firms and 7,137 knowledge workers found that, in the second half of a six-month trial, tool users spent two fewer hours on email each week. Researchers did not detect changes in the quantity or composition of tasks from individual-level access to the tools.
This result illustrates why hours saved should not be reported as equivalent to extra output, reduced headcount, or money saved. A firm could use freed time for other work, leave output unchanged, or realize no net financial saving; the measured reduction in email time alone cannot determine which occurred. The International Labour Organization’s June 2026 synthesis of experiments, firm data, platform studies, and surveys likewise says worker-reported time savings have not yet translated into higher measured output, earnings, or employment in the evidence it reviewed.
Will AI take people’s jobs?
AI can change tasks within jobs, but estimates of occupational exposure are not counts of jobs that have been or will be eliminated. The ILO’s 2025 global index estimates that one in four workers are in an occupation with some generative-AI exposure; 3.3% of global employment falls in its highest exposure category. Clerical occupations are especially exposed, and exposure differs by gender and income level.
The index estimates potential effects on occupational tasks; it does not measure layoffs or predict future net employment. Because occupations contain different kinds of tasks and many still require human input, the ILO concludes that transformation of jobs is more likely than full redundancy. Its June 2026 synthesis describes large-scale displacement as limited in the evidence reviewed so far. That is a statement about observed evidence, not proof that future displacement will not occur.
What surveyed SMEs say about staffing
In the OECD’s late-2024 survey across the seven countries, 83% of SMEs reported no change in staff need, 9% said the need had decreased, and 6% said it had increased. The reported shares do not total 100%; the survey summary does not state the response category for the remainder. These are survey responses from SMEs, not a census of employers or a measure of economy-wide employment.
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Staffing totals also do not capture shifts in the kinds of skills employers need. In the same survey, 20% of SMEs said generative AI increased their need for highly skilled workers, while 9% said it decreased that need. The figures suggest that task and skill requirements can change even when an employer reports no overall change in staff need.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a business judge whether AI is paying off?
Evaluate the workflow in question rather than applying a headline productivity result to the whole organization. A useful assessment separates task performance, time, quality, and total cost, and compares like with like.
Quick Recap
- Choose a specific workflow. Identify the task and the workers doing it. Results from bounded writing or support tasks do not automatically apply to other work.
- Record a baseline. Before introducing a tool, note the time required, amount of work completed, quality or error measures that matter for that task, and relevant costs.
- Measure the same outcomes after adoption. Track whether time changes alongside output and quality. A time reduction without a corresponding output or quality change is not, by itself, evidence of higher productivity.
- Account for the full cost of using the tool. Include the costs relevant to the workflow, such as tool access, training, human review, security, and integration. Compare the resulting total with the baseline rather than treating gross time saved as net savings.
- Check whether the result varies across workers or tasks. The customer-support study found a larger measured gain for novice and lower-skilled agents than for experienced, highly skilled agents. A team-wide average can conceal differences that matter for implementation.
- Keep the conclusion within the evidence. Separate measured outcomes from employee or employer reports, and do not describe a task-level result as a company-wide return unless the company has measured that broader result.
What the evidence does—and does not—establish
- Task performance: Experiments show that generative AI can improve speed, quality, or throughput in particular settings, with effects that vary by task and worker experience.
- Company cost savings: Some SMEs report lower workload or reduced contractor reliance, but the available figures do not establish a common net-savings percentage.
- Employment: Occupational exposure indicates where tasks may be affected, not how many jobs will disappear. Surveyed staffing changes and reviewed displacement evidence remain limited in scope and should not be read as a definitive forecast.
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