Digital assistants can improve workplace productivity on tasks that fit their capabilities, but the gains are not automatic or universal. The strongest cited field result is a 14% average increase in customer-support issues resolved per hour at one company; other findings vary by role, task familiarity, and how productivity is measured.
What workplace studies actually show
The evidence points to task-level gains in some settings, not a guaranteed productivity boost for every worker or organization. The results below come from different methods and outcomes, so they should not be treated as directly comparable.
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| Study and setting | Finding | How to interpret it |
|---|---|---|
| Customer-support agents at one Fortune 500 software company; staggered introduction of an assistant suggesting responses | 14% average increase in issues resolved per hour among 5,179 agents; novice and lower-skilled workers saw a reported 34% improvement, while experienced and highly skilled workers saw minimal impact | A field result for a specific tool, company, and customer-support workflow—not an estimate for all jobs. NBER study |
| Copilot survey of 885 enterprise users with more than three weeks’ use, collected up to February 1, 2024 | Average agreement with “When using Copilot I am more productive” was 4.2 for customer service, 3.97 for sales, and 3.0 for legal, on a five-point scale | Self-reported perceptions, not measured output or proof that Copilot caused a productivity change. The report notes potential self-selection, response bias, and unmeasured factors. Microsoft Research report |
| Lab experiment with 23 Java developers | Participants saved 36% of the time on a task using familiar components; researchers found no substantial difference on a less familiar task | A small experiment whose result should not be generalized to software developers overall. Microsoft Research report |
The customer-support study also reported improvements in customer sentiment and employee retention, and suggested that the assistant may help newer workers learn. Those outcomes are specific to the studied workplace and do not establish the same effects elsewhere. The study examined a staggered rollout of a conversational assistant that proposed responses to customer queries.
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An assistant is more likely to help when the work and the tool align. Microsoft Research’s July 2024 synthesis of more than a dozen workplace studies says outcomes vary by role, function, organization, adoption, and utilization. Its findings also illustrate why task familiarity matters: an assistant reduced time on one familiar coding task but did not produce a substantial difference on a less familiar one. Read the report’s overview.
#1 Best Overall
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- Repeatable work: Drafting routine replies or handling familiar steps may be easier to assist than novel work that requires substantial judgment.
- Worker experience: The support study’s largest gains were among novice and lower-skilled agents. An average can conceal who benefits most—or who sees little change.
- Workflow fit: Suggestions are useful only if they integrate into the work rather than creating extra review, correction, or handoff steps.
- Adoption and use: Providing access does not show that employees use an assistant regularly or that it improves outcomes.
How to tell whether an assistant helps your team
Evaluate a defined task, not a broad promise that AI will make the workplace more productive. Compare the workflow with and without the assistant, and include quality alongside speed.
- Choose a specific task and outcome. Define what the assistant is meant to change, such as time to complete a routine response or issues resolved per hour.
- Record a baseline. Measure the existing process before introducing the tool so that any change has a meaningful comparison.
- Track quality and downstream work. Alongside speed or volume, measure accuracy, defects, rework, customer outcomes, and escalations. Faster output is not a gain if it creates more corrections or harms service.
- Check results across workers and tasks. Look for differences by experience, skill, and task familiarity instead of relying on a single team average.
- Account for adoption and training. Observe actual use and whether employees know how to incorporate suggestions. Microsoft Research identifies training as a possible way to integrate AI into developer workflows; it does not establish that a particular course or provider will improve productivity. Microsoft Research report
- Review sensitivity and governance. For regulated or sensitive work, consider the consequences of errors and check current vendor privacy and security documentation before using a tool.
Keep the comparison grounded in the same task and operating conditions where possible. The reviewed studies do not establish a universal assistant ranking or a guaranteed return on investment.
Rank #2
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Workplace use is not proof of productivity
A U.S. survey paper estimated that, in late 2024, 23% of employed respondents had used generative AI for work at least once in the previous week, while 9% used it every work day. These figures describe adoption, not productivity gains. NBER, “The Rapid Adoption of Generative AI”.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThe available evidence has limits: the prominent field result comes from one company and one kind of work; the Microsoft report combines workplace data, surveys, and experiments; and some experiments are small. Treat each result according to its method and setting rather than assuming it predicts what will happen in a different team.
Quick Recap
Best Value
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Rank #4
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Rank #3
- Clear and Powerful Sound: Experience clear sound quality with strong bass. The smart speaker provides a rich, immersive sound experience with 10W output for dynamic listening.
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