AI use lengthens workdays by up to 90 minutes, study

ActivTrak has published research on AI use at work based on digital activity from 9,140 employees at 467 companies. The analysis found that workers using AI can have workdays up to 90 minutes longer than those of non-users.

The study examined knowledge workers' activity from April to September, comparing workday length, application switching and focus time across heavy, regular, light and non-users of generative AI tools. It also assessed whether employees used AI through browser-based services or tools built into the software they already use.

Workers classified as AI users had workday spans of 8.4 to 9.1 hours, compared with 7.6 hours for non-users. Heavy users also spent more time working after hours, adding 18 minutes a day despite having a similar share of after-hours activity.

Heavier AI use was also linked to more frequent switching between applications. Heavy users averaged 1.1 switches an hour, compared with 0.95 for non-users, while their uninterrupted focus sessions averaged 37.8 minutes, versus 48.2 minutes for light users.

Meeting time moved in the opposite direction. Formal meetings accounted for 3% of work time for heavy AI users, compared with 5% for non-users.

Workflow differences

The contrast was sharper when the data was broken down by how people accessed AI tools. Employees using AI embedded in their main work applications averaged 0.6 app switches an hour, while those using browser-based tools recorded between 1.7 and 4.2 switches an hour.

This gap points to the operational cost of moving between a core task and a separate AI window. ActivTrak described it as a possible “swivel-chair tax”, with browser-based AI users switching applications more than four times as often as non-users.

The findings also varied by role. Software Engineers, who were more likely to use embedded AI tools, averaged 0.6 application switches an hour and spent 78.5% of their active time in core coding applications. Operations Managers, who were more likely to rely on browser-based AI tools, spent 75.5% of their active time in core applications and averaged 1.1 switches an hour, rising to 4.1 during periods of heavy coordination.

Management questions

The analysis adds to the wider debate over whether AI saves time in practice or simply redistributes effort across the working day. While AI tools may help with individual tasks, the data suggests any gains can be offset if employees repeatedly move between systems or if AI use extends work beyond scheduled hours.

For technology leaders, the results suggest the method of deployment may matter as much as adoption rates. A tool built into an existing workflow appears less likely to interrupt concentration than one accessed through a browser tab or separate interface.

The study defined heavy users as employees who used AI for more than 45 minutes and in at least five sessions a day. Light users spent less than 10 minutes a day on AI, while regular users spent between 10 and 45 minutes.

After-hours activity was defined on weekdays as productive screen time outside scheduled start and end times. On weekends, the study counted any instance of less than two hours of productive screen time as after-hours work, on the basis that longer periods could reflect a regular weekend schedule.

The analysis was based on observed digital work patterns rather than employee surveys or self-reporting, aiming to measure how AI use relates to day-to-day work habits rather than attitudes to the technology.

Gabriela Mauch, Chief Customer Officer and Head of the ActivTrak Productivity Lab, said the findings showed a gap between using AI and integrating it into routine work. “AI may help employees complete tasks faster, but our research shows it contributes to longer workdays, more application switching and shorter focus sessions,” Mauch said.

“For leaders, the goal can't simply be AI usage; they need to treat AI usage as a means to measurable gains in productivity, efficiency and business performance,” she added.