Does AI actually save employees time? Data is contradictory

It’s early days for AI-assisted work, and sentiments are mixed about its benefits: Does AI actually bring value and save employees time, or does it bog them down and add unnecessary activities?

Data from ActivTrak seems to vacillate between those two potential realities: The latest research from the productivity tracking software company says employees who use AI most heavily are working up to 90 minutes longer each day, yet its previous research, published earlier this year, found that they are actually working less.

The discrepancy may be due in part to the timespan and sample sizes of the data collection for the two reports, but it indicates that enterprises should still be wary of vendor claims about AI-related productivity gains. Measuring them remains incredibly difficult.

AI use compared

ActivTrak’s current findings are based on behavioral work data gathered from 9,140 knowledge workers across 467 enterprises over a five month period (April 1 to September 25, 2026). This sample is much smaller than that of its previous report based on 443 million hours of work performed by 163,638 employees across 1,111 organizations over a three year period (January 1, 2023 through December 31, 2025).

According to the latest findings, heavy AI users are working between 8.42 and 9.08 hours per day, compared to 7.58 hours for those not using AI. This contradicts ActivTrak’s previous finding that average workdays had decreased by roughly 9 minutes (a 2% decline) thanks to AI.

On the productivity front, heavy AI users have 22% shorter focus sessions than light users (37.8 minutes versus 48.2), suggesting their work is more fragmented due to AI use; further, 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 previous 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 company’s latest report also tracked the 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’s up to 4x more switching than by employees who don’t make use of AI at all, which creates what ActivTrak describes as a “swivel-chair tax” — the additional effort spent by employees due to the 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 the way 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.

The numbers often don’t tell the full story

ActivTrak has a useful view of how work moves across applications, noted Sanchit Vir Gogia, chief analyst at Greyhound Research, but he also pointed out that it sells the capability that produces 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.”

This article originally appeared on CIO.com.ComputerworldRead More