Right now, many companies are worried about how to get more employees to use AI. After all, the promise of AI reducing the burden of some work—drafting routine documents, summarizing information, and debugging code—and allowing workers more time for high-value tasks is tantalizing.

But are they ready for what might happen if they succeed? While leaders are focused on promised productivity gains, they may find themselves surprised by the complex reality, and may not see what these gains are costing them until it’s too late.

In our in-progress research, we discovered that AI tools didn’t reduce work, they consistently intensified it. In an eight-month study of how generative AI changed work habits at a U.S.-based technology company with about 200 employees, we found 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. Importantly, the company did not mandate AI use (though it did offer enterprise subscriptions to commercially available AI tools). On their own initiative workers did more because AI made “doing more” feel possible, accessible, and in many cases intrinsically rewarding.

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. Once the excitement of experimenting fades, workers can find that their workload has quietly grown and feel stretched from juggling everything that’s suddenly on their plate. That workload creep can in turn 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.

This puts leaders in a bind. What should they do? Asking employees to self-regulate isn’t a winning strategy. Rather, companies need to develop a set of norms and standards around AI use—what we call an “AI practice.” Here’s what leaders need to know, and what they can do to set their employees up for success.

How Generative AI Intensifies Work

From April to December last year, we studied how generative AI tools changed work habits at the tech company. We did this through in-person observation two days a week, tracking internal communication channels, and more than 40 in-depth interviews across engineering, product, design, research, and operations.

We identified three main forms of intensification.

Task expansion.

Because AI can fill in gaps in knowledge, workers increasingly stepped into responsibilities that previously belonged to others. Product managers and designers began writing code; researchers took on engineering tasks; and individuals across the organization attempted work they would have outsourced, deferred, or avoided entirely in the past.

Generative AI made those tasks feel newly accessible. These tools provided what many experienced as an empowering cognitive boost: They reduced dependence on others, and offered immediate feedback and correction along the way. Workers described this as “just trying things” with the AI, but these experiments accumulated into a meaningful widening of job scope. In fact, workers increasingly absorbed work that might previously have justified additional help or headcount.

There were knock-on effects of people expanding their remits. 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. This oversight often surfaced informally—in Slack threads or quick desk-side consultations—adding to engineers’ workloads.