When artificial intelligence burst onto the corporate scene, executives hailed it as a panacea for inefficiency, promising to automate rote tasks and liberate workers for higher-level thinking. Companies invested billions, expecting productivity surges that would reshape workflows and boost bottom lines. Yet, as adoption accelerates, a counter-narrative is emerging: Far from easing burdens, AI is often amplifying them, leaving employees more exhausted than ever.

This paradox is vividly captured in a recent piece from Afterburnout.co, which argues that AI tools, designed to free up time, are instead ramping up cognitive demands and eroding true productivity. The article details how workers are spending hours tweaking prompts, verifying outputs, and integrating AI into daily routines—tasks that add layers of mental effort without the anticipated relief.

The Hidden Costs of Cognitive Overload

Industry experts echo this sentiment, warning that the constant shift to complex problem-solving, enabled by AI handling basics, can lead to what some call “AI exhaustion.” A blog post from Visier highlights how employees, deprived of mental breaks from simpler duties, face heightened burnout risks. Without those respites, the brain’s capacity for sustained focus diminishes, turning AI’s promise into a productivity trap.

Similarly, a BBC report from last October explores how AI rollouts in workplaces are contributing to employee fatigue, with workers reporting increased stress from over-reliance on imperfect tools. The piece notes that as AI automates mundane work, it pushes humans toward relentless high-stakes decisions, exacerbating overwork rather than alleviating it.

Evidence from Studies and Corporate Realities

Recent studies lend data to these anecdotes. A Fortune analysis published two weeks ago examined software developers and found that AI, despite hype around efficiency gains, actually hampered their output in controlled experiments. Participants using AI tools completed tasks slower and with more errors, as the technology introduced new variables like debugging AI-generated code.

Compounding this, a ZDNet study from last month revealed a surprising link: Heavy AI users at work experienced higher burnout rates, even as their raw productivity ticked up. The psychological toll—stemming from constant adaptation and fear of obsolescence—outweighed the benefits, suggesting AI’s efficiency is illusory for many.

Navigating AI Without the Burn

Not all views are pessimistic. Josh Bersin’s January post on his site envisions “superworkers” empowered by AI, arguing it creates opportunities for skill elevation rather than job elimination. Yet, even he acknowledges the need for thoughtful integration to avoid overload.

To mitigate risks, experts recommend strategies like setting boundaries on AI use. The Afterburnout.co article advises treating AI as a collaborator, not a crutch—limiting sessions to specific times and combining them with human oversight to reduce cognitive strain. McKinsey’s January report on “superagency” in the workplace urges companies to invest in training, ensuring employees aren’t left to navigate AI’s complexities alone.

Toward a Balanced Future

In blue-collar sectors, AI shows promise for safety and efficiency, as a Newsweek piece from two weeks ago details its role in manufacturing to cut risks and retain staff. But across industries, the lesson is clear: Without addressing human factors, AI could deepen burnout epidemics.

Ultimately, as a Training Industry article from four days ago suggests, leveraging AI for targeted training can combat fatigue by building resilience. For industry insiders, the key is recalibrating expectations—viewing AI not as a silver bullet, but as a tool requiring human-centric design to deliver on its original vow of ease.