Smarter tools, harder grind

As AI’s power grows, so does the intensity of our workday

AI MIGHT NOT be making your job any easier — in fact, it might be making it more intense, according to some new research from a pair of researchers from UC-Berkeley.

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“The promise of generative AI lies not only in what it can do for work, but in how thoughtfully it is integrated into the daily rhythm,” wrote Aruna Ranganathan and Xingqi Maggie Ye, in the Harvard Business Review.

That has been the crux of its appeal in the business world — it promises to reduce the burden of some work, usually repetitive or boring work, while allowing workers more time to focus on high-value tasks. But only if that is what is actually happening. “We discovered that AI tools didn’t reduce work, they consistently intensified it,” the researchers noted, adding that “AI makes it easier to do more, but harder to stop.”

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By tracking the work habits and internal communications channels at a medium-sized tech company, the researchers found that work was intensifying in three main ways. First, it was causing task expansion — so, rather than rely on a teammate who is better equipped to tackle a problem, workers were feeding those questions into a generative AI tool, meaning they “absorbed work that might previously have justified additional help or headcount.”

Second, they found that it was causing people to work more. “Workers slipped small amounts of work into moments that had previously been breaks,” they found, perhaps writing a few prompts at lunch or while waiting for files to load. “These actions rarely felt like doing more work, yet over time they produced a workday with fewer natural pauses and a more continuous involvement with work.”

And third, they were multi-tasking more often. “Many workers noted that they were doing more at once — and feeling more pressure — than before they used AI, even though the time savings from automation had ostensibly been meant to reduce such pressure.”

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The authors argue what work will need in the future is a clear sense of norms and structures to the way AI is used. “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,” they told a UC Berkeley blog. “That might include building in intentional pauses — brief, structured moments before major decisions to surface a counterargument or explicitly link a choice to organizational goals, so speed doesn’t crowd out reflection.”

This, they suggest, might allow workplaces to harness AI more effectively, rather than be purely reactive to it.

“The question facing organizations is not whether AI will change work, but whether they will actively shape that change — or let it quietly shape them.” Kieran Delamont