A recent study from UC Berkeley Haas School of Business suggests that the introduction of generative AI tools in the workplace may not reduce workloads as previously anticipated. Instead, researchers found that AI technology can lead to more intense work patterns among employees.
The eight-month ethnographic research was conducted at a U.S. technology company with about 200 employees. Doctoral student Xingqi Maggie Ye, together with associate professor Aruna Ranganathan, observed and interviewed workers to understand how generative AI changed their daily routines.
“…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,” Ye and Ranganathan wrote about their findings. The study indicated that what started as enthusiasm for new technology quietly resulted in heavier workloads over time.
Ye explained that the project did not begin with an assumption about whether AI would increase or decrease work. “We simply wanted to understand, in a grounded way, how generative AI was shaping everyday work practices. As I spent time observing and talking to people, a pattern around work effort began to emerge that didn’t quite align with the dominant narrative. That was the point when we realized there was something interesting to theorize,” she said.
Describing their methodology, Ye noted: “Our study is based on an eight-month ethnography at a technology company where employees had broad access to generative AI tools. I was on site regularly, observing work in real time—how people structured their days, how they moved between tasks, which tools they used for different kinds of work, how those tools fit into their routines, and so on. I attended meetings and participated in everyday conversations to understand how AI was being discussed, normalized, or debated within the organization. In addition, I conducted more than 40 semi-structured interviews across functional groups. In those interviews, I asked people to walk me through their workflows and reflected with them on what changed after AI entered the picture, including what they now attempted that they wouldn’t have before, how they allocated their time, and how they felt at the end of the day.”
The research identified three main ways in which work intensified: expanding job roles beyond original responsibilities; allowing work tasks to extend into former breaks or downtime; and enabling workers to manage multiple tasks simultaneously by using several AI agents at once.
“In our study, intensification took three main forms in practice. First, people began taking on work that previously would have belonged to someone else or might not have been attempted at all. The scope of what counted as ‘my job’ widened. Second, because AI makes it easy to start and continue tasks, work seeped into moments that used to function as pauses… Third, workers increasingly kept multiple threads alive at once… This created a rhythm where both the human and the machine were constantly in motion,” Ye said.
Some organizations might view increased output as positive; however Ye cautioned against this perspective: “If employees are proactively taking on more and moving faster…that can look like the productivity promise being realized. The challenge is that what appears to be a productivity boost in the short run can become harder to sustain…Over time constant switching and reduced recovery can impair judgment and increase errors…”
She added that there was often a disconnect between employees’ feelings during short bursts of activity compared with overall well-being: “What surprised me most was the contrast between how people described their moment-to-moment engagement…But when they stepped back…they described feeling busier…or less able to fully disconnect…”
To address these issues before they become entrenched habits within organizations using generative AI tools broadly available today (as reported by UC Berkeley Haas), Ye proposed developing intentional workplace practices such as scheduled pauses for reflection or grouping non-urgent updates together instead of reacting immediately—measures aimed at ensuring sustainable productivity rather than unchecked acceleration.
“When we talk about building an ‘AI practice,’ we mean being intentional about the rhythm and boundaries of AI-enabled work rather than simply accelerating because the technology makes it possible…The intention is not to slow innovation but ensure that productivity gains remain aligned thoughtful and sustainable over time,” she concluded.
