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How could AI help workers regain lost productivity?
UNITED STATES, JUN 18 – A survey of 2,000 knowledge workers finds AI can recover over three hours of weekly lost productivity by automating repetitive tasks and enhancing career growth potential.
- Between May 13 and 19, 2025, Talker Research conducted an online survey of 2,000 U.S. office employees for Grammarly to explore workplace productivity and the role of AI.
- The survey found workers lose over three and a half hours weekly to repetitive tasks, and younger employees express more frustration toward these tasks than older ones.
- Respondents report that 62% want to use AI at work to automate tedious tasks and that only 38% of companies currently have clear AI policies guiding its use.
- Heather Breslow of Grammarly noted that professionals are increasingly adopting AI-powered solutions that seamlessly integrate into their current processes to handle routine duties, allowing them to concentrate on more valuable and creative aspects of their work.
- The findings imply organizations must invest in upskilling workers and integrating AI to maintain competitiveness and support employees eager to leverage AI for growth.
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Just 16% Of Workers View AI As Threat To Their Job In New Survey
Your best work happens at exactly 11 a.m. on Monday morning. By Friday afternoon at 12:06 p.m., you've hit rock bottom for workplace efficiency. Between those two points, you're losing more than three and a half hours every week to tasks that completely kill your momentum. The post Just 16% Of Workers View AI As Threat To Their Job In New Survey appeared first on Study Finds.
How could AI help workers regain lost productivity?
American workers’ productivity peaks at 11 a.m. on Mondays, according to new research. The survey of 2,000 knowledge workers revealed when respondents are most productive — and when they’re least productive, which was found to be Fridays at 12:06 p.m.…
Coverage Details
Total News Sources19
Leaning Left3Leaning Right1Center8Last UpdatedBias Distribution67% Center
Bias Distribution
- 67% of the sources are Center
67% Center
L 25%
C 67%
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