The rise of AI has become unstoppable, but with it a less visible problem is also becoming increasingly urgent: AI fatigue. Why does it develop and how can it be prevented? What signs are worth paying attention to, and how can AI be integrated into organizational operations in a way that truly helps, rather than becoming another burden?

In recent years, artificial intelligence (AI) has become a central topic in almost every industry. New solutions, whether it’s automated document summarizers, chat-based customer service, or predictive analytics systems, have seeped into corporate operations at an astonishing rate. The initial enthusiasm was understandable: AI promised to automate monotonous tasks, make decisions more informed, and make work more efficient and faster.

But as more and more AI-based solutions emerged in organizations, the often forced, rapid adaptation also brought with it an interesting side effect. This was the exhaustion caused by the ever-newer AI tools and the constant pressure to adapt, which has recently been called AI fatigue. This phenomenon is not simply a case of people getting tired of the pace of technological change. Often, deeper resistance is also apparent: growing distrust, decreasing motivation, and sometimes even outright disillusionment with new systems. Technology, which was originally supposed to make work easier, has often had the opposite effect: it has created additional burdens, confusion, and dissatisfaction.

There are several interrelated factors behind the development of AI fatigue. The first and perhaps most important reason is the gap between expectations and reality. AI systems are often surrounded by exaggerated marketing promises: “it never makes mistakes”, “it provides human-like understanding”, “it solves the most complex problems in moments”. In practice, however, the AI systems used – no matter how advanced they are – still operate within (reasonable) limits. They make mistakes, hallucinate, draw incorrect conclusions and often do not fully understand the instructions or context.

When users are confronted with these limits every day, they become frustrated. Another “revolutionary” system that would make decisions for them or make their work easier, in the end only generates new problems, the solution of which is also on their shoulders.

The second reason is overload. Many organizations implement multiple AI tools at once in a short period of time: customer service chatbots, internal search engines, document generators, predictive CRM solutions and so on. Each system requires new learning, new procedures, and new responsibilities from its users. Employees often do not receive enough time or support to truly use these tools confidently and effectively, so new solutions can easily become a source of stress rather than some help.

The third, no less significant factor is the erosion of trust. When a system makes a mistake, for example, it misinterprets a document, makes misleading recommendations, or simply does not work stably—users quickly lose their trust. What is particularly problematic is that trust in AI systems is inherently more fragile than trust in humans: even a minor error can cause disproportionately large disappointments, and once trust is broken, it is much harder to restore than when it comes to human error. All of this means that AI systems must not only perform well but also be consistently reliable to achieve long-term adoption.

Finally, the lack of transparency is also a serious problem. If employees do not understand how an AI system works, the decision logic, or when its output can be wrong, fear or skepticism can easily develop. People are naturally averse to systems that operate as “black boxes,” especially when legal, financial, or reputational risks depend on the results.

AI fatigue, as a problem, not only affects the individual but also affects the company. If employees lose faith in AI tools, it directly reduces the effectiveness of the implemented systems and ultimately jeopardizes the return on investment. A system that would speed up work or improve decision-making on paper will slow down processes if employees choose to bypass it.

More important, however, is the loss of trust at an institutional level. Once an organization develops a general feeling that AI systems are untrustworthy or dangerous, it is very difficult to reverse. This skepticism is not only projected onto the specific tool but can also hinder all future digital developments. Employees become more resistant and managers more cautious, even if a new solution would better suit their needs.

Another aspect is the issue of data security and compliance. As a result of fatigue, users may tend to ignore prescribed protocols. For example, they may mishandle personal data or, on the contrary, overly trust AI-generated documents without thoroughly checking them. This poses a direct legal risk, especially in a strict environment with GDPR or other industry regulations.

Finally, the impact on workplace culture should not be underestimated. General cynicism and fatigue surrounding technological developments can be particularly dangerous in an age where continuous learning and adaptability are considered key competencies. An organization that is uncertain will have a much harder time responding to market or technological changes. In such an environment, it is particularly important that the introduction of new technologies does not deepen the crisis of trust. To truly integrate AI systems in a value-creating way, people’s expectations and fears must be consciously addressed.

The first and perhaps most important step is to set realistic expectations. Many organizations make the mistake of introducing new AI tools as a panacea, with expectations that are impossible to meet today. Instead, it is worth communicating clearly at the time of introduction: AI does not replace human decision-making but supports it. Both the strengths and limitations of the tools should be part of internal communication so that employees do not feel cheated later.

The second key factor is gradual, targeted introduction. It is not worth flooding the entire organization with new systems at once. It is much more effective to start with pilot projects: making new AI solutions testable for a single department or workflow. This provides an opportunity to learn, collect feedback, and fine-tune the tools before a wider implementation.

Also essential, perhaps most importantly, is the right support. One of the main reasons for AI fatigue is that employees feel left alone with complex new systems. We can avoid this by offering them appropriate training, internal knowledge bases, and easily accessible support channels. It is important that these trainings focus not only on technical handling, but also introduce potential errors, correct application methods, and risk management aspects.

Finally, it is also a good idea to build human checkpoints into all critical AI-based processes. It should be clear when human approval or review is required before automated decisions are made. This is important not only for compliance reasons, but also for psychological reasons. People are more likely to accept AI tools if they can be sure that the final decisions remain in human hands and that AI is merely providing support.

The phenomenon of AI fatigue is an important reminder that AI is not just a technological issue, but also an organizational and human one. It is not enough to introduce a new tool – a new culture must also be created around it. A culture that treats AI as a valuable helper but does not set unrealistic expectations for it. A culture that is aware of the limitations of AI and builds in controls accordingly. And above all: a culture that puts people at the center, not the algorithm itself.

The greatest competitive advantage of the future will not simply be who uses more artificial intelligence – but who can use it humanely, consciously and sustainably.

István ÜVEGES, PhD is a Computational Linguist researcher and developer at MONTANA Knowledge Management Ltd. and a researcher at the HUN-REN Centre for Social Sciences. His main interests include the social impacts of Artificial Intelligence (Machine Learning), the nature of Legal Language (legalese), the Plain Language Movement, and sentiment- and emotion analysis.