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Yes—digital assistants can improve workplace productivity, but the gains depend on the task, the worker, and whether the tool is actually used. In one study of 5,179 customer-support agents at a single company, access to an AI assistant was associated with 14% more customer issues resolved per hour. That is evidence of a gain in a particular workflow, not a guarantee that every tool or team will see the same result.
What the evidence says about workplace productivity
The strongest cited field result comes from a staggered introduction of an AI conversational assistant at one Fortune 500 software company. The assistant suggested responses to customer queries. Researchers Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond reported an average 14% increase in issues resolved per hour among 5,179 customer-support agents. The study was issued as an NBER working paper in 2023, revised in November 2023, and later published in the Quarterly Journal of Economics in 2025. Read the study details from the NBER.
The average concealed a meaningful difference between workers: the authors reported a 34% improvement for novice and lower-skilled agents, while experienced and highly skilled agents saw minimal impact. The result suggests that an assistant may help workers who have less experience with a task, but it does not establish that every novice will gain 34%. The authors also reported improved customer sentiment and employee retention, and suggested that the tool may help newer workers learn.
Other studies point to the importance of role and familiarity. Microsoft Research’s July 2024 synthesis of more than a dozen workplace studies says that results vary by role, function, organization, adoption, and utilization. It brings together different evidence types, so reported perceptions, operational data, and experiments should not be treated as interchangeable. See the Microsoft Research report.
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Why results vary by role and task
Routine support work
The customer-support study measured issues resolved per hour in a workflow where the assistant proposed responses. That outcome is useful for understanding throughput in this specific setting. It does not directly predict gains for work with different tasks, customers, or quality requirements.
Familiar versus unfamiliar work
In a lab experiment involving 23 Java developers, Microsoft Research found 36% time savings with Copilot on a coding task using familiar components. Researchers found no substantial difference on a less familiar task. Because the experiment was small and task-specific, its percentage should not be generalized to software development overall. The report PDF describes the experiment.
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Survey perceptions by job
A Microsoft Copilot survey analyzed 885 enterprise users who had used the tool for more than three weeks; responses were collected through February 1, 2024. On a five-point agreement scale, respondents’ average agreement with “When using Copilot I am more productive” was 4.2 among customer-service workers, 3.97 among sales workers, and 3.0 among legal workers. These are self-reported perceptions, not measured output or proof that Copilot caused productivity gains. The report notes that self-selection, response bias, and unmeasured factors make causal conclusions challenging. Review the survey methods and findings.
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A nationally representative U.S. survey paper from the NBER reports that, in late 2024, 23% of employed respondents had used generative AI for work at least once in the previous week, and 9% had used it every work day. These figures describe adoption, not productivity effects; they do not show that users became faster or produced better work. The paper was issued in September 2024 and revised in February 2025. See the NBER paper on generative AI adoption.
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Likewise, purchasing licenses or making an assistant available does not establish that employees use it regularly or that the workflow improves. Microsoft Research identifies training as one possible way to integrate AI into developer workflows, but this is a practical suggestion rather than evidence that a particular course or training provider raises productivity. The report discusses integration and training.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an assistant on your team
Run a task-specific evaluation before treating a productivity claim as a business case. Choose a task the team performs often enough to measure, and compare work with and without the assistant under reasonably similar conditions.
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- Define the task and outcome. Specify the work being evaluated and a relevant measure, such as issues resolved per hour, turnaround time, or completed work accepted without rework.
- Measure quality alongside speed. Track accuracy, defects, customer outcomes, escalations, and rework as well as time or volume. A faster first draft is not a productivity gain if it creates more correction work.
- Separate results by experience and role. An overall average can hide who benefits or struggles. Compare meaningful worker groups and task types without assuming the customer-support study’s subgroup effects will recur in your team.
- Check real adoption. Measure whether people use the assistant on the chosen workflow and whether they have enough training and support to use it appropriately. Availability alone is not evidence of impact.
- Use evidence suited to the claim. Operational metrics and controlled or quasi-experimental comparisons can address output changes; surveys can reveal worker perceptions. Label each correctly rather than presenting self-reported agreement as an objective productivity measure.
- Review sensitive-use constraints. For regulated or sensitive work, assess the relevant privacy and security requirements and check current official vendor documentation before relying on product-specific claims. The studies summarized here do not establish current vendor terms or features.
There is no universal productivity percentage or guaranteed return on investment established by these studies. The defensible conclusion is narrower: assistants can improve output on some tasks, and the effect may differ substantially across workers and workflows.
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