AI should make jobs faster and easier. But some experts say this also poses unexpected and sometimes overlooked challenges for employees.
Several leaders at major companies are requiring their employees to incorporate AI into their workflows, with Shopify CEO Tobias Lütke calling it a “fundamental expectation” for employees. A September 2025 study by AI Resume Builder found that 24% of companies said the use of AI is required across all roles, based on responses from approximately 1,300 business leaders.
But there’s a disconnect between leaders’ enthusiasm and expectations for AI and employees’ actual experiences, said Dennis Stoll, director of applied psychology at the American Psychological Association.
A January survey from AI consulting firm Section found that 74% of executives felt “excited” about AI, while 68% of individual contributors reported feeling “anxious or overwhelmed.”
Producing quality results with AI requires a lot of human effort and oversight, some employees told CNBC Make It, not to mention the time and effort employees put into learning how to use the tools in the first place. Additionally, recent research shows that the use of AI itself can cause a type of mental strain and fatigue called “brain flies.”
Employees may blame themselves for struggling to adopt AI, Stoll says. “Is this my fault? Am I doing it wrong?” — but the real question is, “We have never designed the workplace for these kinds of tools.”
For now, “we’re just pushing it on people,” he says.
Not a “magic bullet”
According to a January survey by HR software platform Workday, the majority of employees (85%) said they saved between 1 and 7 hours each week by using AI in the workplace. However, employees also reported that 40% of their efficiency gains were lost because they had to revise, rewrite, edit, and fact-check AI-generated content.
So did Linda Lee, a recruiter in Austin, Texas. “Everyone talks about increasing productivity with AI, but what they don’t talk about is how much time we spend babysitting our produce,” she says. “It certainly speeds up some things, but it’s not the silver bullet that people think.”
Lee, 27, who currently works as an inbound sourcing specialist at a workforce management software company, said that in her previous job, she and her fellow recruiters used various AI models to source, screen and evaluate candidates.
Although she noticed that AI saved her time on certain tasks, such as quickly building a list for LA-based software engineers, she estimates that she spent almost half of that time revising and fact-checking results.
We think AI is efficient, but it was only efficient because there were people operating it. There was someone behind the scenes making sure the AI didn’t make any mistakes.
According to Lee, the AI software would sometimes claim that a candidate’s resume matched 95% of the job description, but upon closer inspection, it found that it only matched 30%. Similarly, AI often flagged qualified candidates as unsuitable for the job, Lee said. To fix these mistakes, she says, “I always had to go back and redo everything.”
Stoll said knowing that AI is error-prone “adds to the anxiety of employees,” and some may fear repercussions from managers if they miss mistakes caused by AI or hallucinations. From an employee perspective, “not only do we need to manage this AI, but we don’t really know how much we can trust it,” he says.
Sundar Pichai, CEO of Alphabet and Google, told the BBC in November 2025 that AI models are “prone to error.” He advised users to fact-check their results, saying, “People have to learn to use these tools for what they’re good at and not blindly believe everything they say.” Google’s Gemini, like other AI tools, reminds users of this in its interface. Below the prompt box you’ll see “Gemini is an AI and can make mistakes.”
Le feels that employers often overlook the amount of extra work employees have to do to produce high-quality results using AI. “They think AI is efficient, but it’s efficient because there were people operating the AI,” she says. “There was someone behind the scenes making sure the AI didn’t make any mistakes and doing everything.”
Learn AI “on your own time”
AI training is also a big time expense for employees.
Devin Boudreau, a digital PR strategist based in Boise, Idaho, says he has spent the past year and a half training several custom AI models to help with various aspects of his work, such as generating campaign ideas and creating survey questions. Although using AI can save time overall, Boudreau says the initial output from the model is typically not “that great.” To produce better results, it is necessary to “constantly communicate what is right and what is wrong.”
Before training the AI to assist him, Boudreau first had to teach himself how to work with the model. Boudreau, 38, said he felt pressure from upper management to use AI at his previous job, but the company didn’t provide training sessions or set aside time for employees to become familiar with the tools. Instead, a friend taught Boudreau most of his AI skills outside of work.
In Boudreau’s view, companies are encouraging employees to “be proficient in AI,” but “they want them to do it on their own time.” The section’s research found that only 27% of independent contributors said they had received AI training from a company, and only 32% said they had “clear access” to AI tools.
According to Stoll, employer expectations for AI can create an unsustainable cycle for workers. “Employees feel they have to be as busy as possible during the workday and then spend their evenings learning about AI so they can ‘catch up and make the next day even busier.’”
People are juggling multiple different tools and outputs, and they feel that if they stop paying attention to any of them, something will fall.
Dennis Stoll
American Psychological Association Chair of Applied Psychology
It’s no wonder some workers feel overwhelmed by the need to learn how to use AI, said Ben Smithman, a licensed psychologist and senior vice president at human resources and human resources firm LHH. Now, he says, there is “an exponential amount of technology evolving every day,” and adapting to these new tools “is going to be taxing on us as humans.”
The most important factor in successfully implementing these tools is that employers don’t put undue pressure on workers to provide “instant productivity,” Smytheman said. He said workers will not be able to master AI if they are “overburdened or overburdened” or “situated in situations where they are unprepared or unskilled.”
“Brain fly” phenomenon
Employees feel burdened by integrating AI into their work. A recent study by Boston Consulting Group found that workers who use AI frequently experience increased mental fatigue, which they refer to as “AI brain fly.” These workers are more likely to make mistakes, feel overwhelmed, mentally foggy, and struggle with decision-making.
Julie Bedard, managing director at BCG and co-author of the study, said one of the main factors that causes workers to develop “brain flies” is having to supervise multiple AI tools at the same time.
The study found that employees who use three or more AI agents in their workflows are more likely to experience negative effects such as mental fog than those who use one or two, with tasks that involve high levels of supervision requiring 14% more mental effort and 12% more mental fatigue.
Stolle likens the effectiveness of using multiple AI tools to “spinning plates.” “People are juggling multiple different tools and outputs, and they feel like if they stop paying attention to one of them, something will fall off,” he says. “There’s a build-up of a background fear that something is going to fall through the cracks.”
Although they may not use the term “brain fly,” even some AI leaders say they feel the strain associated with surveillance. Francesco Bonacci, founder of Cua AI, wrote about X in February: “Every day ends with you exhausted, not by the work itself, but by managing the work.” Ben Wigler, co-founder of LoveMind AI, told Agence France-Presse AFP that using AI creates a “completely new kind of cognitive load” resulting from the need to “babysit” the model.
Another key driver of the “brain fly” is that AI often increases the depth and breadth of an employee’s workload, Bedard says.
Based on observations and interviews with employees at a 200-employee U.S.-based technology company, researchers at the University of California, Berkeley found that employees who used AI tools “worked at a faster pace, took on a wider range of tasks, and extended their workday for longer, often without being asked.”
As AI’s capabilities improve, Boudreau says he feels pressure to increase his output accordingly. He says that while no one is saying today’s companies have to use AI, in his view it would be difficult to achieve current success metrics without it.
Looking to the future
Stoll says the “brainfly” effect points to a “sustainability issue” in how AI is implemented in the workplace. Employers should keep in mind that figuring out how to best implement and integrate AI will take time, he says.
Executives and senior leaders are responsible for “setting the culture” around AI within their organizations and need to be “purposeful” about how they ask their teams to adopt these tools, Bedard says.
She says it’s important for employers to identify scenarios where AI helps employees rather than burdening them. For example, Bedard points out that workers in the BCG study she co-authored reported reduced burnout when they used AI to replace “routine or repetitive tasks.”
AI technology is “evolving at a very fast pace,” Bedard said. Therefore, it is difficult to predict how these challenges will evolve and to determine how to solve them at this stage.
Ultimately, the goal of bringing AI to your enterprise is “not just to do more with AI,” Stolle says. “The goal should be to create more quality jobs in a sustainable way, so that five years from now we still have a thriving organization filled with happy employees.”
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