The promise of artificial intelligence in the workplace has long been a lighter workload, freeing up employees for more creative and high-value tasks. However, new research suggests the opposite is happening. Instead of reducing work, generative AI tools are intensifying it, leading employees to work faster, longer, and across a wider range of responsibilities.
An eight-month study conducted at a U.S. technology firm revealed that as employees voluntarily adopted AI, their work habits changed significantly. This surge in activity, while initially appearing as a productivity boom, is raising concerns about the long-term risks of cognitive fatigue and burnout.
Key Takeaways
- A study found that generative AI consistently intensifies employee workloads rather than reducing them.
- Workers are expanding their job roles, taking on tasks previously outside their expertise, a phenomenon known as task expansion.
- AI is blurring the lines between work and personal time, as employees use it during breaks and outside of normal hours.
- The use of AI encourages multitasking, which increases cognitive load and the pressure to work faster.
- Experts warn this trend could lead to widespread burnout and a decline in work quality if not managed properly.
The Productivity Paradox
For years, the narrative around AI has been one of liberation. Automation was meant to handle routine tasks—summarizing documents, writing code, drafting emails—giving human workers more time to think, strategize, and innovate. Yet, a recent in-depth study paints a different picture.
Researchers spent eight months, from April to December of last year, observing work habits at a technology company with approximately 200 employees. The company provided access to commercial AI tools but did not mandate their use. What the researchers found was a voluntary and enthusiastic adoption of AI that led not to more leisure time, but to a more demanding work environment.
Employees didn't just work the same amount in less time. They used the newfound efficiency to do more. The study identified a self-reinforcing cycle: AI accelerates tasks, which raises expectations for speed, which in turn makes workers more reliant on AI to keep up.
About the Research
The findings are based on an eight-month observational study at a U.S.-based technology company. Researchers conducted over 40 in-depth interviews with employees across various departments, including engineering, product, and design, while also tracking internal communications to understand how work habits evolved with the introduction of generative AI.
Three Ways AI Is Making Work Harder
The intensification of work observed in the study manifested in three distinct ways. These changes occurred organically, without direct pressure from management, as employees explored what was possible with their new AI assistants.
1. Task Expansion
One of the most significant changes was that employees began to take on responsibilities that were previously outside their job descriptions. Because AI can fill knowledge gaps, it made complex tasks feel more accessible.
For example, product managers and designers started writing code, and researchers took on engineering tasks. Individuals across the company attempted work they would have previously outsourced or avoided. This expansion had knock-on effects. Engineers, for instance, found themselves spending more time reviewing and correcting AI-assisted code from non-engineering colleagues, adding an informal coaching layer to their existing duties.
"You had thought that maybe, oh, because you could be more productive with AI, then you save some time, you can work less. But then really, you don’t work less. You just work the same amount or even more."
2. Blurred Work-Life Boundaries
Generative AI makes starting a task incredibly easy. The friction of a blank page is gone, replaced by a simple prompt. This accessibility has led to work seeping into moments that were once breaks.
Many employees reported prompting an AI during their lunch break, in between meetings, or just before leaving the office for the day. These small actions rarely felt like “work,” but they accumulated over time, creating a workday with fewer natural pauses. The conversational nature of interacting with an AI made it feel less like a formal task and more like a chat, making it easier for work to spill into evenings and mornings.
The End of Downtime
Some study participants realized in hindsight that as prompting AI during breaks became a habit, their downtime no longer felt restorative. Work became more ambient—an activity that could always be nudged forward, making it harder to mentally disconnect.
3. A New Rhythm of Multitasking
AI has introduced a new workflow where employees manage multiple active threads simultaneously. They might write code manually while an AI generates an alternative, or run several AI agents in parallel to tackle different problems.
This creates a sense of having a “partner” that helps them move through their workload with greater momentum. However, the reality is a state of constant context-switching. Employees are frequently checking AI outputs and juggling a growing number of open tasks. This continuous mental juggling increases cognitive load, even if the work feels productive in the moment.
The Risk of Unsustainable Intensity
From a management perspective, employees voluntarily working more and faster might seem like an unqualified success. It appears to be the productivity explosion that AI has long promised. However, this informal expansion of work carries significant risks.
The initial excitement of using a new tool can mask a quiet increase in workload and cognitive strain. Because the extra effort is voluntary, leaders may overlook how much additional pressure their teams are under. Over time, this can lead to serious consequences:
- Cognitive Fatigue: Constant multitasking and reduced downtime can impair judgment and critical thinking.
- Increased Errors: As employees become overworked, the quality of their output may decline, negating initial productivity gains.
- Burnout and Turnover: An unsustainable pace can lead to employee burnout, dissatisfaction, and higher turnover rates.
Creating a Sustainable AI Practice
Simply asking employees to self-regulate is not an effective strategy. Instead, organizations are being urged to develop an intentional "AI practice"—a set of norms and standards that guide how these powerful tools are used.
Experts suggest several strategies to counteract AI-driven work intensification. These include introducing intentional pauses to allow for reflection before major decisions, sequencing tasks to protect focus time and reduce constant interruptions, and promoting human grounding through scheduled team check-ins and dialogue to counter the isolating effects of solo work with AI.
The challenge for companies is not whether AI will change work, but how to actively shape that change. Without clear guidelines, the natural tendency of AI is not to lighten the load, but to fill every available moment with more work. Building a thoughtful framework around its use will be critical for harnessing its benefits without paying the high price of a burned-out workforce.