AI productivity data shows contradictory results for employees

It is still early in the adoption cycle of AI-assisted work, and opinions remain divided on whether artificial intelligence saves time or creates more work. Data from ActivTrak appears to swing between these two realities: their latest research indicates that employees who use AI heavily work up to 90 minutes longer each day, whereas a previous report found they were actually working less.

Contradictory findings on time and focus

ActivTrak’s current data comes from behavioral observations of 9,140 knowledge workers across 467 enterprises over five months (April 1 to September 25, 2026). This sample is significantly smaller than the previous report which tracked 443 million hours of work by 163,638 employees across 1,111 organizations over three years (January 1, 2023 through December 31, 2025).

The latest results show heavy AI users working between 8.42 and 9.08 hours daily, compared to 7.58 hours for those not using AI. This stands in direct contrast to ActivTrak’s prior finding that average workdays had shrunk by roughly 9 minutes (a 2% decline) thanks to AI.

On the productivity side, heavy AI users have 22% shorter focus sessions than light users (37.8 minutes versus 48.2), indicating their work is more fragmented due to AI use; additionally, they switch apps 15% more frequently.

“Over time, those interruptions add friction to high-value work that requires concentration, critical thinking, and sustained attention,” Sarah Altemus, manager of the ActivTrak Productivity Lab, said in a blog post describing the latest findings.

ActivTrak’s earlier report had found that over the period of the earlier study, focus efficiency—the percentage of work time spent in focused, uninterrupted activity—fell to a three-year low of 60% with AI use, and average focused session time in 2025 declined by 9% from 2023. However, that same report found that daily productive time increased by 19 minutes, or 5%, to 6 hours and 36 minutes, with AI in the workplace. Furthermore, productive sessions grew by 13%.

The swivel-chair tax and management challenges

The company’s latest report also tracked productivity differences between users of tools with embedded AI and users of web-based AI tools. Those using embedded AI switched apps 0.6 times an hour, while those using AI on the web switched between 1.7 and 4.2 times per hour. That is up to 4x more switching than by employees who do not make use of AI at all, which creates what ActivTrak describes as a “swivel-chair tax”—the additional effort spent by employees due to movement between apps.

Similarly, in its previous report, ActivTrak had found that this multitasking rose 12%, or 10-plus minutes, with AI.

Interestingly, according to the latest research, light users spend more active work time in core job-specific apps (81.4% compared to 78%). “In other words, more frequent AI engagement doesn’t necessarily translate into more focused work,” Altemus pointed out.

The larger issue is how organizations manage the additional capacity that AI may create, Altemus noted. “When employees complete tasks faster, managers may assume they have room for more responsibilities,” she said. Without clear workload boundaries, time savings may be redirected into additional work that can be taxing.

Analysts caution on measurement limitations

ActivTrak offers a useful view of how work moves across applications, noted Sanchit Vir Gogia, chief analyst at Greyhound Research, but he also pointed out that the company sells the capability used to produce these measurements.

Recorded use depends on what the instrumentation can detect, he observed. For example, an employee can use AI at work and still appear in the non-user group, and work on an unmanaged personal device sits outside the measured estate. In addition, an API or a background agent can finish work without producing a desktop session, and passive screen time, an offline meeting captured through a calendar, and paid hours are also different measures. Furthermore, switching apps is easier to observe than switching attention within an application.

Ultimately, he noted, those judging the value of AI need to establish what happened after the first task became faster. “An improvement per paid hour can conceal effort left off the timesheet,” said Gogia. “The [enterprise] board should be able to say whose day improved and whose work made that improvement possible.”