We’re seeing more and more workplaces turn to AI, but there seems to be a disconnect: many teams are struggling to keeeo up with how it’s changing everyday workflows. In fact, research from Studio Graphene found that &8% of UK businesses now use AI some how. The number goes up to 85% for mid-sized businesses which is the highest of any group.
But even with that increase and high numbers, only 31% of those businesses said they had seen a positive return on investment. Another 18% said projects had not delivered what they expected, and 16% said it was too early to judge. Only about 41% said they had a clear idea of what success even looks like.
Ritam Gandhi, director and founder of Studio Graphene, explained the confusion. He said: 鈥淢any organisations are at a critical point in their AI journey. Adoption has skyrocketed in the past year, particularly among mid-sized businesses, but our research clearly shows just how much progress is required for AI projects to be successful.鈥�
He added: 鈥淭here has been a rush to adopt AI amidst huge hype and a proliferation of new tools 鈥� this is certainly true of private equity-backed mid-sized companies looking to AI for automation, scalability and competitive edge.鈥�
This rush is starting to affect how people think and work. Instead of making tasks easier, AI is adding new layers of thinking, checking and managing tools. That is where the idea of 鈥渂rain fry鈥� begins.
What Exactly Is 鈥淎I Brain Fry鈥�?
Harvard Business Review actually wrote a great piece with Julie Bedard, Matthew Kropp, Megan Hsu, Olivia T. Karaman, Jason Hawes and Gabriella Rosen Kellerman that puts a name to this feeling.
They describe 鈥淎I brain fry鈥� as mental fatigue caused by excessive use or oversight of AI tools beyond what the brain can handle. It is not the same as burnout. It is more immediate and cognitive, hitting attention, memory and decision making.
One early example came from a user testing an AI coding system. They wrote: 鈥淸T]here鈥檚 really too much going on for you to reasonably comprehend. I had a palpable sense of stress watching it. Gas Town was moving too fast for me.鈥�
Another engineer, Francesco Bonacci, described the feeling in a post titled 鈥淰ibe Coding Paralysis: When Infinite Productivity Breaks Your Brain.鈥� He wrote: 鈥淚 end each day exhausted – not from the work itself, but from the managing of the work. Six worktrees open, four half-written features, two 鈥榪uick fixes鈥� that spawned rabbit holes, and a growing sense that I鈥檓 losing the plot entirely.鈥�
These accounts show a similar thing: the work itself is not the problem but it is the management side of it.
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What Is Happening Inside The Brain?
The Harvard Business Review study looked at 1,488 full-time workers in the United States across different roles and industries. It found that heavy AI oversight increases mental strain in measurable ways.
Workers who had to monitor AI tools used 14% more mental effort and they also reported 12% more mental fatigue and 19% more information overload. This happens when people must track and check the outputs of multiple tools.
Participants described the feeling quite clearly, with one senior engineering manager saying: 鈥淚 had one tool helping me weigh technical decisions, another spitting out drafts and summaries, and I kept bouncing between them, double-checking every little thing. But instead of moving faster, my brain just started to feel cluttered. Not physically tired, just鈥� crowded.鈥�
A finance director shared a similar experience: 鈥淚 had been back and forth with AI reframing ideas, synthesising data, forming and organising the flow of pillars and work鈥 couldn鈥檛 even comprehend鈥f what I had created even made sense鈥ust couldn鈥檛 do anything else and had to revisit the next day when I could think.鈥�
The study also tracked how many tools people used at once. Productivity went up when workers used two or three tools. After that, it went down. This matches what is already known about multitasking. The brain struggles when attention is split too many ways.
Does AI Fatigue Affect Business Outcomes?
The effects go past just simply feeling tired. The study found that workers experiencing AI brain fry reported 33% more decision fatigue which means poorer choices and slower thinking over time.
Error rates also went up as those with brain fry reported 11% more minor mistakes and 39% more major mistakes compared to those who did not experience it. These are not small changes, especially in jobs where accuracy is of utmost importance…
There is also an effect on staff retention because among workers who did not report brain fry, 25% said they planned to leave their job. That number came up to 34% among those who did – a 39% increase in intent to quit.
On the other hand, the research shows a different side to AI. When workers used it to handle repetitive tasks, burnout scores went down by 15%. This shows that the way AI is used might be more of a deciding factor over how much of it is used.
So, maybe AI itself isn’t the cause and it’s more so about how the work is organised around it. When people spend more time managing tools than thinking clearly, it’ll the brain that has to pay for it.
