‘AI fatigue’ is settling in as companies’ proofs of concept increasingly fail. Here’s how to prevent it 

Against the backdrop of more than two years of rapid AI development and the pressure that has come with it, some company leaders facing repeated AI failures are starting to feel fatigued. Employees are feeling it, too: According to a study from Quantum Workplace, employees who consider themselves frequent AI users reported higher levels of burnout (45%) compared to those who infrequently (38%) or never (35%) use AI at work. 

Failure is of course a natural part of R&D and any technology adoption, but many leaders describe feeling a heightened sense of pressure surrounding AI compared to other technology shifts. At the same time, weighty conversations about AI are unfolding far beyond the workplace as AI takes center stage everywhere from schools to geopolitics. 

“Anytime [that] a market, and everyone around you, is beating you over the head with a message on a trending technology, it’s human nature—you just get sick of hearing about it,” said Erik Brown, the AI and emerging tech lead at consulting firm West Monroe.

Failure and pressure drive “AI fatigue”

In his work supporting clients as they explore implementing AI, Brown has observed a significant trend of clients feeling “AI fatigue” and becoming increasingly frustrated with AI proof of concept projects that fail to deliver tangible results. He attributes a lot of the failures to businesses exploring the wrong use cases or misunderstanding the various subsets of AI that are relevant for a job—for example, jumping on large language models (LLMs) to solve a problem because they’ve become popular, when machine learning or another approach would actually be a better fit. The field itself is also evolving so rapidly and is so complex that it creates an environment ripe for fatigue. 

In other cases, the pressure and even excitement about the possibilities can cause companies to take too-big swings without fully thinking them through. Brown describes how one of his clients, a massive global organization, corralled a dozen of its top data scientists into a new “innovation group” tasked with figuring out how to use AI to drive innovation in their products. They built a lot of really cool AI-driven technology, he said, but struggled to get it adopted because it didn’t really solve core business issues, causing a lot of frustration around wasted effort, time, and resources.

“I think it’s so easy with any new technology, especially one that’s getting the attention of AI, to just lead with the tech first,” said Brown. “That’s where I think a lot of this fatigue and initial failures are coming from.”

Eoin Hinchy, cofounder and CEO of workflow automation company Tines, said his team had 70 failures with an AI initiative they were working on over the course of a year before finally landing on a successful iteration. The main technical challenge was around ensuring the environment they were building for the company’s clients to deploy LLMs would be sufficiently secure and private, so they absolutely had to get it right.

“There were certainly moments when we felt like we’d cracked it and, yes, this is it. This is the feature that we need. This is going to be the big-step change—only for us to realize, actually, no, we need to go back to the drawing board,” he said.

Aside from the team that was actually working out the technical solutions, Hinchy said other parts of the organization were also fatigued by the ups and downs. The go-to-market team in particular was trying to do its job in a competitive sales environment where other vendors were releasing similar offerings, yet the pace of getting to the finalized product was out of their hands. Aligning the product and sales team turned out to be the biggest challenge from an organizational standpoint, said Hinchy. 

“There had to be a lot of pep talks, dialogue, and reassurance with the engineers, product team, and our sales folks saying all this blood, sweat, and tears up front in this unglamorous work will be worth it in the end,” he said.

Let functional teams take charge

At cybersecurity company Netskope, chief information security officer James Robinson has felt his fair share of disappointment, describing feeling underwhelmed by agents that failed to deliver on various technical tasks and other investments that didn’t deliver after he got his hopes up. But while he and his engineers have largely stayed motivated by their own inner desires to build and experiment, the company’s governance team is really feeling the fatigue. Their to-do lists often read like work that’s already been completed as they have to race to keep up with approving new efforts, the latest AI tool a team wants to adopt, and everything in between. 

In this case, the solution was all in the process. The company is removing some of the burden by asking specific business units to handle the initial governance steps and setting clear expectations for what needs to be done before approaching the AI governance committee. 

“One of the things that we’re really pushing on and exploring is ways we can put this into business units,” said Robinson. “For instance, with marketing or engineering productivity teams, let them actually do the first round of review. They’re more interested and more motivated for it, honestly, so let them take that review. And then once it gets to the governance team, they can just do some specific deep-dive questions and we can make sure the documentation is done.”

The approach mirrors what West Monroe’s Brown said ultimately helped his client recover from its failed “innovation lab” effort. His team suggested going back to the business units to identify some key challenges and then seeing which might be best suited for an AI solution. Then they broke into smaller teams that included input from the relevant business unit throughout the process, and they were able to experiment and build a prototype that proved AI could help solve one of those problems within a month. Another month and a half later, the first release of that solution was deployed.

Overall, his advice for preventing and overcoming AI fatigue is to start small. 

“There are two things you can do that are counterproductive: One is to just succumb to the fear and do nothing at all, and then eventually your competitors will overtake you. Or you can try to do too much at once or not be focused enough in how you experiment [with] embedding AI in various parts of your business, and that’s going to be overwhelming as well,” he said. “So take a step back, think through in what types of scenarios you can experiment with AI, break into smaller teams in those functional areas, and work in small chunks with some guidance.”

The point of AI, after all, is to help you work smarter, not harder.