While the growing use of AI in business environments can be largely successful at eliminating repetitive work and alleviating traditional sources of burnout, it also introduces new complexities and fails to reduce overall stress. A recent Appknox publication, the AI and Developer Burnout Report 2025, shows that burnout is no longer about doing too much, but about adapting too fast to advancing technological trends. The AI explosion is adding an “invisible layer” to operations, shaping expectations, pace, and emotional experience at work.
Productivity Gains That Raise the Bar
Adopting AI comes with certain benefits in productivity and efficiency, but these very advantages create new challenges. By automating certain repetitive and time-consuming tasks, AI can help workers perform and meet deadlines faster. However, this increased efficiency and productivity quietly resets what “normal output” looks like, shifting the baseline.
When the kind of speed provided by AI tools becomes the expected level of output, it can have a negative psychological toll on employees. Teams feel pressured to deliver more to keep up with growing expectations and constantly increasing demands without a corresponding reduction in workload that would enable them to continually outperform prior benchmarks.
From Physical Exhaustion to Cognitive Overload
In spite of some seeing and presenting it as the solution to all problems, AI helping to shoulder the weight of tedious tasks, unfortunately, does not eliminate worker exhaustion. While it can help to reduce the mental load of repetitive and time-consuming work, it introduces its own challenges as well. The constant tool updates and model changes associated with new technologies and processes can lead to learning fatigue in workers.
The increased adoption of AI tools also demands significant oversight to ensure accuracy and effectiveness, as AI output is often unreliable. Newer technologies are especially fallible, requiring increased mental effort from employees to validate, interpret, and correct AI outputs. The Appknox report shows that respondents are concerned about the hidden tax on attention and focus posed by context switching.
Identity, Mastery, and the Fear of Falling Behind
On top of introducing new challenges, AI adoption can also have the effect of rendering previously acquired skills useless or obsolete in certain contexts, undermining workers’ efforts. The growth of AI usage in enterprise environments raises concerns regarding the skills that experts have built up over time—the introduction of new tools and processes can curb senior professionals’ ability to benefit from their hard-earned expertise.
At the same time, early-career workers struggle to accommodate the combination of accelerated learning and anxiety about developing “real” skills. It can be difficult to balance the skills required to operate and manage AI with the skills required to operate and manage without it. AI has a significant impact on how workers at all levels perceive their own value and long-term relevance.
AI as a Silent Second Manager
While AI alleviates much of the work of repetitive tasks, it also adds a layer of perceived surveillance that can add to the mental toll. The influx of automated alerts, recommendations, and monitoring creates a sense of continuous oversight, and some point to AI as a potentially beneficial alternative to traditional corporate power structures.
However, this approach brings challenges with it as well, with the fallibility of AI tools and the cultural preference for typical workplace dynamics posing challenges. The oversight of AI tools can create near-perfect expectations, which feel heavier in areas like security, DevSecOps, and high-stakes roles. The emotional weight of AI-assisted decision-making is more difficult to manage when every mistake feels amplified.
The Leadership Blind Spot
The toll taken by AI is worsened by leadership treating AI as a speed lever, unintentionally amplifying burnout. Those in charge of making the decision to implement new tools often fail to think of the potential fallout and additional challenges created for workers. The increase in tool sprawl, lack of training, and unclear expectations accelerates advancements while adding to the exhaustion faced by workers. There is a growing gap between organizations that adopt AI humanely, with their employees’ needs and capabilities in mind, and those that don’t.
Rethinking Sustainable AI Adoption
In order to effectively implement AI tools without simply adding to the hardships faced by workers, it is crucial to ensure that they are adopted and managed properly. Implementing sufficient guardrails is just as important as tools’ capabilities, as adopting tools with advanced functionality can do more harm than good without the right measures in place. Structured training, realistic productivity expectations, and tool consolidation are important factors in implementing AI tools without adding to employee burnout.
Organizations should shift their perspective and goals from “AI everywhere” to using AI only in places where it can actually help to reduce cognitive load. It is also important to prioritize secure and effective implementation and management in order to reap benefits from AI adoption. “Organizations must focus on implementing models that drive accuracies of detection and data analysis to help uplift teams, enabling security teams to prioritize higher-level strategic efforts, such as improving cyber resilience,” says Nicole Carignan, Senior Vice President, Security & AI Strategy, and Field CISO at Darktrace.
The Future of Burnout in an AI-First Workplace
The Appknox report signals important things for 2025-26 and beyond, highlighting significant trends that organizations must pay attention to in order to implement AI tools smartly. The burnout felt by employees is a problem of leadership and the systems in place, not an individual failure on the part of the worker. The next competitive challenge for organizations using AI will not be faster tools, but healthier adoption, approaching advanced tools with balance and consideration.
