Productivity is up. But so is burnout. GenAI may be helping us move faster—but at what cognitive cost?
What if productivity was the wrong prize?
GenAI promised to save us time—and it’s delivering. But instead of getting space to breathe, we’re just taking on more. More meetings. More tabs. More mental strain. The result? A knowledge workforce that’s overstretched, cognitively fried, and increasingly unsure whether they’re getting ahead or just staying afloat.
Generative AI was supposed to free us from drudgery—drafting emails, summarizing reports, debugging code—all in a fraction of the time it used to take. And indeed, many knowledge workers are now accomplishing more in less time than ever. But instead of lighter to-do lists, they’re often handed even more tasks to fill the gap. If AI helps us get more done, why does work keep expanding? From an executive’s perch, squeezing extra projects into the day sounds like productivity nirvana. Yet on the ground, this “do more with less” ethos may carry hidden costs: sloppy work, endless task-switching, mounting cognitive load, and exhausted employees. It begs the question – at what point do productivity gains backfire, and how do we recognize when a knowledge worker has reached critical mass?
GenAI’s Productivity Boom – and the Workload Paradox
Companies are eagerly embracing generative AI in hopes of efficiency gains. Early data is encouraging: A Thomson Reuters survey found the average knowledge worker expects AI to save about 4 hours per week, effectively “adding” a half-day of capacity. By 2030, those savings could triple to 12 hours a week. In theory, that means fewer late nights and breathing room for higher-level work. Indeed, over half of workers in one study said AI has improved their ability to do their jobs.
Ironically, however, many workers aren’t getting to relax – they’re getting busier. In a 2024 Wrike survey, employees reported their workloads have grown by 31% in the last year, and managers estimated it even higher at 46%. Much of this uptick comes from “workload creep” – as AI automates tasks, new responsibilities rush in to fill the void. Tech and finance employees, for example, have had to absorb duties from laid-off colleagues, leaving them “struggling under the weight” of expanded roles. Business leaders openly acknowledge raising the bar: 81% of executives admit they increased demands on workers in the past year.
This mindset is reinforced by recent headlines—from companies mandating AI training with expectations of immediate performance gains, to leaders slashing staff assuming AI can fill in. The message: do more, faster. As Harvard economist Lawrence Katz observed, “Things look like a speed-up for the knowledge worker. There is a sense that the employer can pile on more stuff.”
The High Cost of Constant Task-Switching
The human brain, unlike a computer, can’t truly multitask. When we try to juggle numerous tasks or rapidly switch between them, we pay a heavy cognitive penalty. Researchers call it context switching (rapidly shifting focus between tasks), and it quietly leeches our productivity, increases stress, and drains our energy. Every ding of a notification or shift from one project to another forces our brain to change gears. We might not notice it at first, but by day’s end many of us feel surprisingly exhausted “even if we feel we didn’t accomplish much.”
Studies show that the mental lag from interruptions is very real. On average, a person needs about 23 minutes to fully regain focus after a distraction. Think about that – a two-minute IM chat could cost you over twenty minutes of lost concentration. Multiply that by the dozens of pings, emails, and task swaps a typical worker experiences daily, and the hours evaporate. In fact, just three context switches in a day can sap an hour of effective work time. It’s no surprise, then, that people who frequently toggle between tasks suffer up to a 40% drop in productivity compared to those who focus on one at a time. As one analysis bluntly put it, switching tasks and “swapping between different assignments wastes a great deal of time,” contributing to an estimated $450 billion in lost productivity globally each year.
The damage isn’t just in output – quality takes a hit too. When our attention is scattered, mistakes multiply. A famous statistic attributed to journalist David Brooks drives this home: “A person who is interrupted while performing a task takes 50% more time to complete it and makes 50% more errors.” Sloppy work and overlooked details become inevitable when you’re constantly catching up to where you left off. In complex knowledge work, these errors can be costly – from code defects to strategic misjudgments – essentially erasing the gains of extra productivity.
Perhaps most concerning, chronic task-switching impairs our cognitive abilities over time. Neuroscientists have found that heavy multitaskers show statistically significant declines in working memory capacity. In one Stanford study, habitual multitaskers performed worse on memory tasks and had trouble filtering out irrelevant information. Other research shows multitasking can actually alter brain structure: frequent task-switchers had noticeably less gray matter in the brain’s attention-regulating region than those who focus deeply. In plain terms, the more we force our brains to constantly refocus, the worse we become at concentrating at all. It creates a self-reinforcing spiral: fragmented attention becomes a habit, making it even harder to sustain focus in the future.
What Leaders Can Do About It
It’s becoming clear that productivity isn’t just a technical equation of output per hour – it’s also a human equation of quality, creativity, and sustainability per hour. Generative AI can indeed unlock incredible efficiency. But unless we deliberately manage how that freed-up time is used, we risk falling into a trap where every minute saved is a minute filled with something else. The endgame of that approach is a workforce that’s perpetually “on,” mentally scattered, and nearing burnout – a far cry from the innovative, high-performing teams leaders envision.
To truly harness GenAI’s potential, organizations might need a mindset shift—and a moment of reflection: Are we empowering people, or just automating them into exhaustion? What would it look like to build a culture that values focus over frenzy, and sustainability over speed? from “Do more” to “Do better.” This means recognizing human cognitive limits and respecting them as we integrate AI. It might mean setting policies to discourage after-hours ping-pong just because AI made work 24/7 accessible, or training managers to distribute work in a way that balances speed with recovery time. Research and common sense both tell us that well-rested, focused employees produce higher-quality work than frazzled, overloaded ones. As one analysis noted, even short rest breaks during the workday help consolidate memory and restore motivation, ultimately boosting performance. In the AI era, protecting that mental recovery time may be more important than ever.
Leaders can support sustainable productivity by:
- Limiting context-switching and prioritizing deep work
- Clarifying “must-do” vs. “nice-to-have” projects
- Respecting digital boundaries and recovery time
- Tracking not just speed, but cognitive load
So here’s the real question:
What if the promise of GenAI isn’t just about doing more—but doing less, more meaningfully?
Maybe the future isn’t filled with more tasks. Maybe it’s filled with better ones.
Know someone drowning in ‘productivity’? Share this with your coworker, manager or anyone feeling pressure to do more with less.
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