AI Fatigue: Is the backlash against AI already here?

Overwhelmed? Tick. Confused? Tick. Saturated? Tick. Exhausted? Tick. Disillusioned? Big Tick. If you identify with any (or all) of those sentiments when it comes to artificial intelligence (AI) then don’t worry, as you’re definitely not alone.

That’s because AI fatigue is fast-becoming a real condition, experts believe, and many employees and businesses appear to be already suffering from it.

Some 71% of office workers believe new AI tools are appearing faster than they can learn how to use them, according to research from digital adoption platform WalkMe. The Opinium study of more than 1,200 office-based professionals also found nearly half (47%) felt they should be excited about using AI, but instead reported feelings of worry.

One reason for this AI fatigue might be too much talk about it and not enough action from it, according to David Midgley, founder of Squared.io.

“The challenge we have is oversaturation of discourse on the ‘potential’ of AI, which makes it almost impossible to distinguish between hype and reality,” he says.

“Those who are heavily invested in AI are over-promising, which leads to many businesses feeling as though their AI partners are under-delivering. We need to stop talking about the potential of AI and start bringing the receipts of what it can actually achieve today.”

The problem of AI fatigue is inevitable, but also to be expected, according to Dr Clare Walsh, director of education at the Institute of Analytics (IoA).

“For those working in digital long enough. They know there is always a period after the initial excitement at the launch of a new technology when ordinary users start to see the costs and limitations of the latest technologies,” she says.

“After 10 years of non-stop exciting advancements – from the first neural nets in 2016 to RAG solutions today – we may have forgotten this phase of disappointment was coming. It doesn’t negate the potential of AI technology – it is just an inevitable part of the adoption curve.”

Job fears give way to AI fatigue

With seemingly constant predictions that AI will be responsible for swathes of job losses, it could be the case that AI fatigue will go hand-in-hand with that fear among workers and their bosses.

A survey from digital transformation business ArvartoConnect discovered that a quarter (26%) of UK contact center agents from a sample of 1,000 were considering quitting their roles due to AI-related anxiety. However, this was not because of the automation itself; it was because of poor communication, weak support, and unclear career paths.

“Our research reveals the emotional toll AI rollouts are having on the very people they’re meant to support, “ says Debra Maxwell, ArvatoConnect’s CEO.

“But this isn’t about stopping innovation; it’s a call to lead with empathy. The organizations that thrive will be those that build confidence, not just capability. Leaders have a real opportunity to turn uncertainty into empowerment.”

Holding back the tide of AI fatigue is also about not presenting it as the only solution to every problem, warns Claus Jepsen, Unit4’s CTO.

“It is absolutely critical the IT team is asking the right questions and thoroughly interrogating the brief from the business,” he explains.

“Quite often, AI is not the right answer. If you foist AI onto the business when they don’t want or need it, you’ll get a backlash. You can avoid the threat of AI fatigue if you listen carefully to your team and really appreciate how they want to interact with technology, where its use can be improved, and where it adds absolutely no value.”

Research by Opinium from optimization firm ABBYY also supports this idea. It spoke to 1,200 senior managers or above in companies of 100+ employees in Australia, France, Germany, Singapore, the UK, and the US. Nearly a third (31%) admitted to finding the training of GenAI models harder than expected.

At a time when boardrooms are awash with ideas for AI implementation, hesitation and fatigue might be setting into investment plans. Indeed, the majority of those questioned stated that budgets for AI would only increase by 16-20% in the next year. Just one in 10 (11%) said the rise would be 50% or more.

A lack of balance

Corey Keyser, head of AI at Ataccama, a provider of data trust software, speaks to the high expectations set around AI, saying: “The problem isn’t that AI is being adopted too quickly; it’s that it’s being adopted without balance. To avoid fatigue, businesses need a barbell strategy.”

“On one end of the barbell is employee-led adoption. AI shouldn’t be forced into workflows but made available, accessible, and encouraged in ways that feel natural. On the other end, businesses must invest in a small number of focused, executive-backed AI projects,” he adds.

“These initiatives should be tied directly to organizational KPIs and built by dedicated teams or consultants who can ensure they are robust, trustworthy, and scalable. Focus on a handful of initiatives with clear ROI and executive alignment. This makes adoption purposeful rather than performative.”

Speed without structure creates strain, according to Oana Beattie, vice president of data and AI at IT infrastructure firm Kyndryl.

“AI fatigue is not just a productivity issue; it is a board-level risk,” she says.

“When workflows are interrupted, or systems overlap, trust in technology erodes, driving disengagement, errors, and higher attrition. AI fatigue shows up as declining curiosity and decision fatigue. Left unchecked, this may undermine belonging, morale, and ultimately innovation.”

So-called initiative overload is a major problem that will most likely manifest first among workers before rising through the hierarchy to the leadership. But Gavin Guinane, head of EMEA solution engineering at AI platform Glean remains – perhaps unsurprisingly – optimistic about where we are right now.

“We haven’t reached a saturation point for AI, but we have reached a saturation point for fragmentation,” he says.

“The challenge businesses face today isn’t an overabundance of AI itself, but rather a proliferation of disjointed tools that fail to connect across systems, teams, and workflows.

“When every department adopts its own standalone solution, organizations incur a hidden ‘AI tax’ of duplicated effort, misaligned data, and mounting security risk.

This is where the fatigue sets in - not from AI, but from the friction of managing disconnected experiences that don’t scale.”

Perfect is the enemy of good

Boosting the level of AI literacy among workforces is one way to stave off AI fatigue and help with decision paralysis over the technology’s implementation.

This is especially important given the huge number of AI tools there are to choose from now, many of which have similar functions and features, and this can bring duplication and confusion.

Ensuring businesses are on top of evolving AI regulations – often different country by country – is another key aspect to get right if companies are to retain workforce engagement and interest.

There are three common traps companies fall into, according to John Thompson, a senior vice president at consulting firm The Hackett Group and a professor at the University of Michigan.

“Waiting to find the perfect use case, not knowing where to start or how to proceed, and mistaking technological progress as a reason to wait until the level of activity slows down,” he says.

“None of these are good reasons for not moving forward, but they are often cited as reasons why progress cannot commence. The C-Suite should set forth a mandate that the organization will begin its AI journey and set broad goals and objectives that can be met and measured. IT can, and should, set the standards, frameworks, and architecture the enterprise leverages,” adds Thompson, who is also the author of ‘The path to artificial general intelligence.’

He further warns: “What happens when lots of money has been spent on AI transformation by the business, but it's interrupting workflows and productivity rather than assisting with it? It becomes very costly.”

There is certainly room for improvement among the examples of AI fatigue. Sally Winston, director of XM strategy at Qualtrics – which has a database of human sentiment from 18,000+ customers – points to it being a “natural evolution rather than true AI saturation”.

She says: “The market has shifted from the initial ‘shiny toy’ phase, where organizations rushed to experiment with every new AI tool, to a more mature stage requiring better alignment to business strategy.

“Qualtrics research shows that while 45% of employees use AI weekly, nearly half receive no formal training or guidelines. This lack of enablement, coupled with leaders overestimating employee trust, leads to employee optimism about AI's potential quickly turning into exhaustion.”

Winston advises leaders to stop saying “yes” to every AI request and instead better connect AI initiatives to direct business outcomes. “The solution isn't necessarily fewer AI tools,” she adds, “it's a better AI strategy. AI will achieve the best results when it serves clear purposes with proper support.”