Francisco has been working at a financial consulting firm for three years and, a few months ago, his company incorporated artificial intelligence under an almost idyllic promise: to be able to do the same, but in less time. But that premise soon collided with a different reality: the pressure to perform better led Francisco to work longer hours and take on tasks that he previously delegated to others, because he thought that, now, with “AI he would do it faster.”
The incorporation of artificial intelligence generated several changes in the work dynamics of the 200 employees studied: upon learning about the capacity of this technology, workers began to feel that certain tasks were more accessible than they thought. Thus, they began to absorb, little by little, functions that were previously carried out by others and that could have even justified hiring additional staff.
This type of technology also blurred the lines between work and rest: because AI made starting a task so easy, workers incorporated small amounts of work into times that were previously breaks: from leaving it to work during lunch to using it in meetings or while waiting for a file to upload. Over time, these small actions ended up generating a workday with fewer natural breaks, which extended into the afternoons or early mornings without deliberate intention.
Finally, the study found that because workers felt they had a “buddy” (AI) who could help them manage their workload,increased multitasking at work. This led to a constant shift of attention, frequent review of AI results, and an increasing number of open tasks. “This created a cognitive load and a feeling of constant juggling, even when the work seemed productive. Many workers noticed that they were doing more things at once and felt more pressure than before using AI, even though the time savings brought about by automation were supposedly intended to reduce that pressure.”adds the study.
In this sense, Silvia Renata Figiacone, PhD in Psychology, Lic. in Psychopedagogy, professor at the Faculty of Biomedical Sciences of the Austral University and director of NeuroEduca, explains that what neuropsychology suggests is that there is no such thing. multitasking: “Attentional capital is divided into several tasks at the same time, but attention does not work with the same power in all channels. This illusion of multitasking deteriorates productivity”.
“All of this created a self-reinforcing cycle: AI sped up certain tasks, which increased expectations for speed; greater speed made workers more dependent on it. This greater reliance expanded the scope of what workers attempted, and broader scope further expanded the amount and density of work. Several participants noted that while they felt more productive, they did not feel less busy and, in some cases, even felt busier than before.”summarizes the study.
The research alludes to something that has been frequently heard in other moments of technological advances: the productivity paradox. In other words, although in the short term, it seems to give good results, over time, overwork can lead to an accumulation of fatigue, exhaustion, impair judgment, increase the probability of errors and generate an unsustainable work pace.
Pernice agrees with the article that some teams in companies have become saturated after incorporating AI, but explains it with a different interpretation: “What I see, both in academia and in conversations with companies, is not so much a saturation imposed from above, but a voluntary self-expansion of the scope of work. People, upon discovering that they can do things that they previously delegated or avoided, naturally take on more.”explains Pernice.
Figiacone agrees and explains that the human mind has free will. “The brain works the same as it did 300 thousand years ago and needs breaks. The mind, with its freedom, can decide not to take those pauses; But I probably won’t survive.”explains the psychopedagogue.
At the same time, Pernice considers that everything that is happening is a natural phase of evolution, when adopting a new technology: “This is not a failure of AI nor necessarily a problem: it is the labor market seeking a new balance. Every time a technology releases constraints, people explore the new limits. Initial saturation is part of that exploration, not its final destination”.
Figiacone agrees with Pernice and alludes to a principle of mental health: “What causes the impact is not the stressor, but the relationship between the stressor and the person”. He explains that many people will appear who, based on certain technologies, will feel overwhelmed, pressured and distressed, “but that has nothing to do with the tool, but with the relationship between the person and the stressor, with the ability to adapt to significant changes”. The specialist points out that, even after the attack on the twin towers, there were people who were there and did not have post-traumatic symptoms, but there were those who only saw it on television and did have them.
/ Alina Dichkova
In its conclusions, the research explains that both people and companies should adopt a “AI practice”: a set of intentional rules and routines that structure how this technology is used, when it is appropriate to stop it, and how the work should or should not be expanded in response to new capabilities.
Pernice believes that, today, the focus of organizations is almost exclusively on adoption and productivity, not on sustainability. That is why he points out that, In most organizations, AI training is still not balanced with the prevention of work stress. “This is understandable – we are in an accelerated exploration phase – but insufficient”he adds.
In that sense, he shares the advice conveyed by the researchers in the article, although he adds one more: “Literacy in limitations”. In a nutshell: “Train people not only how to use AI, but when not to use it, when their judgment is superior to the model output, and when speed is not the correct metric. Stress largely comes from not knowing when to stop and that is trained”.
In relation to changes in workflows, Pernice explains that the key is not to stop AI, but to redesign the architecture of work. Among his advice, he encourages promoting what the article calls “human grounding”: “Moments where the team deliberates without AI, because creativity and strategic judgment often emerge from the contrast between human perspectives, not from iterating with a model”.
He also points out that Organizations need to redefine what “productivity” means in the age of AI: measure quality final outputs, not volume of activity. At this point, he warns that it is important to analyze what the incentives are like within the company, since, if speed and volume are rewarded, saturation is an inevitable consequence. By rewarding the impact and quality of decisions, AI becomes what it promises to be: a tool that amplifies the human instead of depleting it.
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