Key Notes
- ActivTrak’s analysis of 9,140 knowledge workers links AI use with workday spans up to 90 minutes longer than those of non-users.
- The figures measure the interval between first and last computer activity and do not establish that AI caused employees to work longer.
- Separate Berkeley research describes how taking on more tasks with AI can gradually raise expectations and make work harder to switch off.
AI users have workdays stretching up to 90 minutes longer than those of colleagues who do not use the tools, according to ActivTrak. The finding raises a difficult question for employers buying AI to save time: where do those saved minutes actually go?
The workforce analytics company reported average workday spans of 8.4–9.1 hours for AI users, compared with 7.6 hours for non-users. Its October 29 analysis covered digital activity from 9,140 knowledge workers across 467 enterprises between April 1 and September 25, 2026.
The word “span” matters. ActivTrak defines it as the interval between the first and last computer activity. A longer span can include breaks and gaps; it does not mean an employee spent every additional minute actively working or sitting in an office.
The comparison also establishes an association, rather than proving that AI caused the longer days. Employees with heavier workloads may use AI more, and differences between roles can affect both tool use and working patterns.
More Tools Can Mean More Interruptions
Heavy AI users had shorter uninterrupted focus sessions than light users: 37.8 minutes versus 48.2 minutes. How workers accessed AI also mattered. Tools embedded in primary work applications were associated with less application switching than browser-based alternatives.
That distinction makes the practical problem easier to see. An assistant inside a document editor can help finish a task in place. Moving material into a separate chatbot, checking its answer and transferring it back can introduce more steps. The value of a faster answer depends partly on how much effort it takes to put that answer to use.
When Extra Capacity Becomes the New Baseline
A separate study by UC Berkeley Haas researchers Xingqi Maggie Ye and Aruna Ranganathan described how AI can expand work even without an explicit instruction from a boss. Over eight months at a 200-person technology company, the researchers observed employees taking on a wider range of tasks and extending work into more parts of the day.
People used AI during breaks and kept several tasks running in parallel. What initially felt like experimentation could gradually reset expectations, turning additional effort into standard performance. These observations offer a possible mechanism for work intensification, although they do not explain every difference in ActivTrak’s separate dataset.
The distinction between activity and useful output is also familiar from the AI usage rankings we examined at Amazon. Counting prompts or tokens can reward more activity without showing whether a team delivered better work.
A more useful test for employers is whether a defined task gets completed to the same standard with less total effort. That means accounting for review, corrections and handoffs as well as generation time. If an AI-assisted report takes less time to draft but creates another round of checking, the first saving is only part of the result.
For employees, the stakes are whether greater capacity produces a manageable workload or a permanently expanding queue. Finishing one task faster creates room for a choice about what happens next; it does not automatically bring the end of the working day closer.
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