Artificial intelligence has become an integral part of many workplaces, assisting employees with document drafting, research, meeting summarization, and automating routine tasks. However, experts warn that simply increasing the frequency of AI use does not guarantee increased labor productivity.
Ziyanda Gshuma, a Scrum Master at Strider Digital, emphasized that companies and employees should shift the focus from how often they apply AI to whether it genuinely improves the quality and efficiency of their work. According to her, daily use of the tool does not automatically mean high productivity.
Gshuma noted that measuring frequency is easy—by counting the number of prompts entered—whereas measuring productivity is much more complex. She believes that the greatest value of AI lies in eliminating repetitive administrative load, allowing specialists to dedicate more time to solving complex problems, collaborating, and applying their expertise.
In Gshuma's opinion, one of the most common misconceptions about artificial intelligence is that it automatically saves time. She explained that creating useful content with AI often requires multiple iterations: entering prompts, checking, and refining before the result is suitable. People who have started using AI tools more frequently quickly realize that the results require frequent correction, and prompts need constant improvement to achieve the desired outcome.
This process can take minutes or even hours, sometimes taking longer than completing the task independently. Gshuma insists that AI should never be accepted unconditionally. The technology is capable of misinterpreting requests or generating inaccurate information, so human knowledge, experience, and contextual understanding remain critically important for obtaining reliable results.
Instead of viewing AI as a replacement for human expertise, Gshuma urges specialists to perceive it as a collaborative partner. She shares her experience, saying she uses AI as an assistant in research and sometimes as a critic of her own work to identify overlooked points. Furthermore, she has found that the best application of these tools is not in getting ready-made answers, but in learning how to ask better questions.
Gshuma admits that developing effective prompts and evaluating AI responses can interrupt the workflow, but she argues that this stage is necessary to ensure relevance and accuracy. In a team environment, context takes on even greater importance. Asking AI to generate recommendations without sufficient background information can lead to suggestions that are difficult to justify or that do not fit the situation.
She is convinced that critical thinking remains one of the most valuable competencies in an AI-supported work environment. It is necessary to assess the reliability of the information provided by AI, consider one's own context, and challenge any assumptions it makes. Gshuma stresses that AI is intended not to replace human thought but to enhance it, acting as a conversationalist and creative partner in the dialogue led by the human.
She also warns that excessive dependence on AI can gradually weaken important professional skills if employees stop practicing them independently. Heavy reliance on AI can degrade skills such as analysis, writing, and problem-solving if we cease training them ourselves. However, interestingly, with proper use, this can have the opposite effect. Based on her own experience, she explained that AI assistance in achieving sprint goals unexpectedly helped her restore writing skills from early in her career because checking AI-generated content required heightened attention to language, structure, and tone.
As artificial intelligence becomes more deeply integrated into workflows, Gshuma believes that the most successful specialists will be those who can combine AI capabilities with sound judgment and human experience. Ultimately, good judgment will be the deciding factor. The strongest users will know when to use AI, when not to, how to check results, how to provide context, and how to merge AI assistance with human expertise.
Artificial intelligence (AI) tools have demonstrated great potential to increase productivity in various areas. However, recent research suggests that indiscriminate use of these technologies can lead to the weakening of individuals' cognitive abilities.
Studies analyzed indicate that excessive dependence on these systems can diminish users' aptitude for problem-solving without technological support. This warning arose from investigations conducted with students and professionals, where participants who used AI showed immediate improvements in performance but faced difficulties when performing the same tasks autonomously.
Experiments with workers and students confirmed that AI can optimize results when applied to activities aligned with their competencies. In a study involving hundreds of consultants from the Boston Consulting Group, researchers from the Wharton School noted an increase in the number of completed tasks and a decrease in time spent by those who had access to the tool.
This survey, published in 2026 in the journal Organization Science, revealed that employees assisted by AI produced higher quality work in functions where the technology was most competent. The most significant progress was observed among professionals who had a more modest initial performance.
A similar finding was found in a study conducted by Grace Liu of Carnegie Mellon University, focusing on mathematical problem-solving. By comparing students with and without access to AI, the study identified superior performance among those who could use the resource during exercises.
Despite the instant gains, researchers detected adverse effects after the removal of technological assistance. Individuals accustomed to AI began to show lower performance than those who had never used the resource and demonstrated less resilience in the face of difficulties.
Additionally, another study investigated how excessive trust in responses generated by AI systems affects decision-making. Steven Shaw and Gideon Nave evaluated over 1,300 participants and identified a phenomenon called 'cognitive surrender,' characterized by the user abandoning their own evaluation in favor of the conclusion provided by the machine.
Researchers argue that AI can operate as a third cognitive mechanism, complementing the traditional methods of fast thinking and detailed analysis described by Daniel Kahneman. The problem arises when this tool ceases to be a complement to human reasoning and begins to replace it entirely.
Experts emphasize that the crucial skill in the age of AI will be discerning which tasks should remain under human control and which can be delegated to automated systems. Collaboration is most effective when the user understands the limits of the technology and can judge its outputs.
An analysis from 2024, published in the journal Nature Human Behavior and based on 106 AI experiments, showed that joint performance between humans and machines is optimized when each party acts in its area of greatest advantage. However, when the system demonstrates superiority, the human difficulty in knowing whether to trust or contest the tool can compromise the final result.
The interviewed experts highlighted that processes such as initial idea conception, text writing, and knowledge generation require active human participation. For them, AI is most valuable in phases of review, questioning arguments, and refining existing work.
The concern extends to the educational environment. The cited studies indicated that students may learn less by using AI solely to speed up school assignments. On the other hand, when the tool is used to gain clarification, formulate questions, and assimilate concepts, the damage to the learning process is minimized.
Judy Hanwen Shen and Alex Tamkin, from Anthropic, reported in their research with developers learning a new programming library conceptual difficulties, code reading, and debugging when AI was used as a shortcut to obtain ready-made solutions. The researchers' recommendation is to reconfigure AI as a tool for deepening understanding, stimulating questioning and expanding analytical capacity, rather than simply eliminating the mental effort required to learn.
