It’s a familiar refrain: Artificial intelligence tools, tech evangelists say, will one day free workers from mundane tasks so they can focus on higher-level, strategic endeavors. But even if that does prove to be true, is the modern workforce ready for such a shift?
A new in-progress study by two researchers at the University of California, Berkeley, raises fresh questions about the promise and purpose of new AI tools in the workplace.
In an eight-month study of generative AI usage at an unnamed U.S.-based technology company with 200 employees, the researchers said they observed that employees “worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so.” But there’s a catch: “While this may sound like a dream come true for leaders, the changes brought about by enthusiastic AI adoption can be unsustainable, causing problems down the line.”
For instance, the expansion of individual employees’ workloads could ultimately lead to “cognitive fatigue, burnout and weakened decision-making.”
“The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems,” researchers Aruna Ranganathan and Xingqi Maggie Ye wrote in an article for the Harvard Business Review.
And as employees tried out generative AI tools for more and more tasks, it appeared to create more work for employees in other departments. Researchers said they observed workers increasingly stepping into responsibilities that “previously belonged to others.”
“There were knock-on effects of people expanding their remits,” the researchers wrote in the article. “For instance, engineers, in turn, spent more time reviewing, correcting, and guiding AI-generated or AI-assisted work produced by colleagues. These demands extended beyond formal code review. Engineers increasingly found themselves coaching colleagues who were ‘vibe-coding’ and finishing partially complete pull requests.”
Ranganathan and Ye didn’t immediately respond on Tuesday to requests for comment on the study.
Another unintended side effect of AI use: Working on breaks. In 40 interviews “across engineering, product, design, research and operations,” the UC researchers found that many workers “prompted AI during lunch, in meetings or while waiting for a file to load.”
While such actions “rarely felt like doing more work,” they ultimately led to workdays with “fewer natural pauses and a more continuous involvement with work,” the researchers wrote.
“Some workers described realizing, often in hindsight, that as prompting during breaks became habitual, downtime no longer provided the same sense of recovery,” they wrote. “As a result, work felt less bounded and more ambient—something that could always be advanced a little further. The boundary between work and non-work did not disappear, but it became easier to cross.”
To avoid such issues, the researchers recommended that both employees and the organizations they work for adopt “norms and routines that structure how AI is used, when it is appropriate to stop and how work should and should not expand in response to newfound capability.” That includes protecting time for “human connection” with other workers, and ensuring that workers are indeed taking structured breaks as scheduled.
Researchers also suggested developing norms that “deliberately shape when work moves forward, not just how fast.”
“This includes batching non-urgent notifications, holding updates until natural breakpoints and protecting focus windows in which workers are shielded from interruptions,” they said.
Notably, employees at the tech firm observed by Ranganathan and Ye were not mandated to use AI and used it only as they saw fit. That’s in contrast to consulting firm McKinsey & Company, which recently drew headlines for requiring job candidates to use an internal AI assistant during interviews.
The UC researchers wrote that generative AI’s promise “lies not only in what it can do for work, but in how thoughtfully it is integrated into the daily rhythm.”
“What looks like higher productivity in the short run can mask silent workload creep and growing cognitive strain as employees juggle multiple AI-enabled workflows,” they cautioned.
