A recent study by researchers at UC Berkeley Haas School of Business suggests that the use of generative artificial intelligence (AI) in the workplace may be increasing, rather than decreasing, the amount of work employees perform. The research was conducted by doctoral student Xingqi Maggie Ye and associate professor Aruna Ranganathan through an eight-month ethnographic study at a U.S. technology company with about 200 employees.
The researchers observed how AI tools were integrated into daily workflows and conducted over 40 interviews across different functional groups. According to Ye, “We did not start with a fixed hypothesis about whether AI reduces work or increases it. 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.”
Ye explained their methodology: “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 findings indicate that instead of freeing up workers’ time as often promised by advocates of workplace automation technologies like AI—such as those described in reports from institutions like UC Berkeley Haas—the introduction of these tools led employees to take on more tasks voluntarily.
“In our study,” Ye said regarding intensification of work practices,”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.” She continued: “Second, because AI makes it easy to start and continue tasks, work seeped into moments that used to function as pauses… This dissolved some of the natural stopping points in the workday.” Finally: “Third, workers increasingly kept multiple threads alive at once… This created a rhythm where both the human and the machine were constantly in motion.”
Ranganathan added context for why this trend could be concerning for employers: “I can see why some organizations might see this as a win. 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…”
One notable aspect highlighted by Ye is how workers experienced these changes: “What surprised me most was the contrast between how people described their moment-to-moment engagement and how they described their overall experience… They described feeling busier more stretched or less able to fully disconnect.”
To address these issues Ye proposes organizations adopt intentional strategies she calls an “AI practice.” She explained: “When we talk about building an ‘AI practice,’ we mean being intentional about rhythm and boundaries of AI-enabled work rather than simply accelerating because technology makes it possible…” Such measures could include scheduled pauses before decisions batching non-urgent updates protecting focused working windows—and ensuring regular check-ins among team members.
More details about this research are available from UC Berkeley Haas School’s newsroom (link).
