Our findings suggest that without intention, AI makes it easier to do more—but harder to stop. An AI practice offers a counterbalance: a way to preserve moments for recovery and reflection even as work accelerates

Human grounding.

As AI enables more solo, self-contained work, organizations can benefit from protecting time and space for listening and human connection. Short opportunities to connect with others—whether through brief check-ins, shared reflection moments, or structured dialogue—interrupt continuous solo engagement with AI tools and help restore... See more

Rather than reacting to every AI-generated output as it appears, sequencing encourages work to advance in coherent phases. When coordination is paced in this way, workers experience less fragmentation and fewer costly context switches, while teams maintain overall throughput.

Sequencing.

As AI enables constant activity in the background, organizations can benefit from 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.

Instead of responding passively to how AI tools reshape workplaces, both individuals and companies should adopt an “AI practice”: a set of intentional 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. Without such practices, the natural... See more

For workers, the cumulative effect is fatigue, burnout, and a growing sense that work is harder to step away from, especially as organizational expectations for speed and responsiveness rise.

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. Because the extra effort is voluntary and often framed as enjoyable experimentation, it is easy for leaders to overlook how much additional load workers are carrying. Over time, overwork... See more

All of this produced a self-reinforcing cycle. AI accelerated certain tasks, which raised expectations for speed; higher speed made workers more reliant on AI. Increased reliance widened the scope of what workers attempted, and a wider scope further expanded the quantity and density of work. Several participants noted that although they felt more... See more

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.