Workers using AI are putting in significantly longer days than those who do not, according to new observational data from workplace-productivity software vendor ActivTrak. The company analyzed digital work activity from 9,140 knowledge workers across 467 enterprises between April 1 and September 25, 2026. The central finding cuts against a common promise: that AI would hand workers back their time.
The Numbers Behind the Gap
The gap between AI users and non-users reaches up to 90 minutes of additional digital work per day.
AI users averaged between 8.42 and 9.08 hours of digital work per day, compared with 7.58 hours for non-users, a difference of up to roughly 90 minutes. ActivTrak defined heavy AI users as employees engaging with AI tools for more than 45 minutes across at least five sessions daily. That group showed the most pronounced patterns.
Heavy users logged 18 extra after-hours minutes, switched applications 15.2% more often than non-users, and averaged only 37.8 minutes of uninterrupted focus versus 48.2 minutes for light users. They also spent less time in formal meetings, at 3% of their workday compared with 5% for non-users. That detail makes simple meeting-load growth a less likely explanation for the longer hours, though other factors such as job role, seniority and company culture could also account for the difference.
Correlation, Not Cause
The ActivTrak data reveal an association between AI use and longer workdays, not a causal relationship.
The data show an association, not a causal relationship. Two competing explanations exist: employees who already work longer hours may be more likely to adopt AI tools, or AI may enable workers to absorb more tasks and responsibilities than they otherwise would. ActivTrak’s dataset is observational and drawn from its own customer base, so results may not generalize to hourly workers, unionized environments, or organizations using different monitoring systems.
What Berkeley’s Researchers Found on the Ground
The qualitative picture is starker: workers at one technology company took on more tasks, not fewer, after adopting AI.
UC Berkeley Haas researchers Xingqi Maggie Ye and Aruna Ranganathan spent eight months embedded at a technology company of roughly 200 employees. They completed more than 40 semi-structured interviews as part of an ethnographic study of AI adoption in practice. Their findings, paraphrased here, indicate that generative AI did not free up employee time. Instead, workers moved faster, took on broader responsibilities, and stretched work into hours previously treated as downtime, often without being directed to do so.
The Berkeley team identified three specific intensification patterns: workers expanded the scope of their roles, used AI during former break periods, and maintained multiple simultaneous AI-assisted workstreams. The researchers also raised a sustainability concern: short-term productivity gains may be difficult to sustain if cognitive demands and performance expectations keep rising in parallel.
Who Actually Captures the Gains
The policy debate surrounding these findings centers on who benefits from AI-driven productivity, not whether a benefit exists.
Sen. Bernie Sanders has proposed a 32-hour workweek at no loss of pay or benefits, arguing that the financial benefits of AI and automation should reach working families rather than flow primarily to employers and corporate executives. The ActivTrak and Berkeley findings suggest that, at present, expanded output and longer workdays are more visible outcomes than reduced hours. The real question is whether AI-driven efficiency translates into more output demanded of the same workforce, or into fewer hours required from it.
The Task-Workload Gap
Faster task completion offers limited relief when the overall task load expands at the same time.
Finishing individual tasks faster means little if the task list grows faster still. The Berkeley and ActivTrak findings, taken together, point to a gap between task-level efficiency and worker-level workload. That gap is where the promise of AI-driven efficiency translates into more output demanded of the same workforce, or into fewer hours required from it, and where the most visible collision with organizational practice occurs.
