Burnout in the tech industry has nearly doubled in the past year, with 46% of workers expressing feeling burnt out and almost 25% saying they’re very burned out, according to recent data from Dice. Alongside that uptick, daily AI use has quadrupled, layoffs have impacted nearly two-thirds of the workforce, and overall confidence in the long-term future of tech dropped from 80% to 60%.

Tech employees most likely to experience burnout are millennials, those with 10 to 19 years of experience, or those at small companies with fewer than 250 employees already worried about layoffs. 

These growing frustrations arrive on the heels of several years of ups and downs in the industry, so it’s critical that employers demonstrate stability for employees. That means emphasizing AI governance and transparency, financial health, clear policies, and transparency from leadership acknowledging market strains, according to Dice.

“You can identify AI burnout the same way as failed AI value, by looking at rework and outcomes,” says Laura Stash, EVP at iTech AG. “If error rates are rising, review cycles are increasing, or employees are spending more time validating outputs, that’s a sign AI is creating more work.”

Where AI-induced burnout crops up

Burnout surrounding AI is typically tied to friction rather than traditional overwork, as well as usage patterns, says Paul Farnsworth, president of Dice. Daily AI users are more likely to express higher levels of burnout, with over half of AI users reporting burnout compared to only a third of those who never use AI, according to Dice.

“Increased exposure to AI without the right support can amplify rather than reduce workplace stress,” says Farnsworth. “In an AI setting, burnout tends to appear as increased rework, lower confidence in outputs, and frustration tied to unclear expectations or lack of training. If employees spend more time correcting or validating work than benefiting from efficiency gains, that’s usually the earliest and clearest signal.”

AI also contributes to more subtle forms of burnout tied to the constant change and uncertainty of AI. This can create a new type of fatigue that employees experience switching between multiple tools, feeling pressure to keep up with new AI capabilities, and the need to recheck outputs.

“These challenges are compounded in environments where expectations are unclear or evolving quickly,” adds Farnsworth. “Over time, that combination can lead to disengagement if employees feel the pace is unsustainable.”

Stash agrees that a lot of AI burnout starts to show up where there isn’t clear guidance on how to use AI tools. You’ll find employees switch between different tools, or reuse outputs across systems, repeating unnecessary work, and therefore possibly lose important context between different applications, she says.

Companies should rather focus on embedding AI tools directly into the day-to-day tools, services, and software employees already use. That way, they become part of the workflow instead of another tool that requires constant re-prompting and context switching.

“The goal shouldn’t be to give employees more AI tools, but simplify the experience,” says Stash. “Fewer tools, clearer use cases, and AI embedded into existing workflows is what reduces friction and prevents burnout.”

Increased expectations ramp up burnout

A report from the Upwork Institute found that around 71% of full-time employees say they are burned out and 65% report struggling with employer demands on their productivity. And executives seem aware of this shift, with 81% of C-suite leaders saying they acknowledge they’ve increased their demands on employees over the past year, and 96% saying they expect AI tools will boost productivity in the organization.

However, nearly half of all employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say AI tools have decreased their productivity and added to their workload.

“A common issue is that AI is introduced faster than it’s operationalized,” says Farnsworth. “When employees are expected to navigate multiple tools without clear guidance, it adds complexity.”

Respondents in the Upwork survey say they now spend more time reviewing and moderating AI-generated content (39%), investing time into learning new AI tools (23%), and are still being asked to do more work than before (21%). Overall, 40% say they feel their company is asking too much of them when it comes to AI.

Farnsworth suggests that leaders focus on narrowing toolsets, defining specific use cases, and providing role-based training to help reduce that burden, as well as emphasizing and setting the expectation that AI is meant to improve how work gets done, not simply increase the volume or pace of output.

Expectations vs reality for AI productivity

Executives express high confidence around employee skills, with 37% of C-suite leaders at companies that use AI saying their workforce is highly skilled and comfortable with AI tools. But this perception doesn’t match the 17% of workers who say they feel skilled and comfortable using AI tools.

Additionally, 38% of employees say they feel overwhelmed about using AI at work and that it’s adding to their workload, suggesting too many leaders are moving forward implementing AI without realistic expectations of what workers can do, especially without proper training and upskilling.

And while that 96% of C-suite executives say they expect AI tools to boost productivity, only 26% say they have proper AI training programs in place, and only 13% say they have a well-implemented AI strategy, according to Upwork.

Data from Upwork also reveals further imbalances in executive perception and employee experience, with 69% of C-suite leaders admitting they’re aware of the current struggles employees face regarding productivity demands, and 84% are adamant their organizations value employee well-being over productivity. But only 60% of full-time employees say their employer prioritizes that despite mostly agreeing their employers provide flexibility and greater clarity on strategic goals. In addition, the report points out that employees who perceive their company to value productivity over well-being report higher rates of feeling overwhelmed by their workload.

AI burnout can quickly lead to disengagement and even trigger an exodus of talent. So leaders need to take stock of AI strategies and ensure they align with realistic training and upskilling opportunities for employees. Expectations around AI should be delivered to employees clearly and timely, without leaving room for question or interpretation.

“This kind of change management is not new, and we should use tools and techniques that have helped before to help mitigate burnout,” says Farnsworth. “Creating cross-functional working teams, highlighting best practices, reducing redundancy in tools, and understanding the goals of an organization and then applying tools on top are all ways to help tech professionals who struggle with AI burnout.”

In 2024, when cloud-based software company BlackLine implemented its Buckie AI agent, a knowledge base that employees could ask HR- or IT-related questions, the company didn’t expect to move away from the tool within a year.

“The technology was moving so fast,” CIO Sumit Johar says, and the company needed a different system to scale for the future.

By June the following year, BlackLine had migrated to Google Gemini enterprise, and today, employees organization-wide have built nearly 300 AI agents themselves.

The rapid clip at which organizations are adopting AI is compounding challenges for CIOs. And for employees, being bombarded with new tools and processes is leading to AI fatigue, a feeling of burnout from added workflows and unmet promises of time savings.

At the same time, corporate boards are putting increased pressure on CEOs to deploy AI and deliver results. So CIOs are caught in the middle, balancing board and leadership expectations with employee reality on the ground. They’re pressured to move quickly — a strategy that, in reality, often backfires, according to Doug Gilbert, CIO and chief digital officer at global business technology consultancy Sutherland. He says AI implementations currently have up to a 90% failure rate. “Doing AI right may sound slower, but in the long run, it’s going to be faster,” he says.

Why employee fatigue happens

Riley Stricklin, founder and chief strategy officer at AI integration firm Cadre AI, agrees that AI fatigue is a growing problem across companies. It’s not necessarily because employees are anti-AI, but rather they’re overwhelmed with new tools, new expectations, and constant change, he says.

The initial steps to implement AI take time, temporarily adding to employee workloads before delivering promised time savings, a common complaint Johar hears. Then, the moment teams feel they’re settling in with a new technology, understanding how they can organize their business processes and maximize value, something new comes up that changes everything. “That’s why there’s exhaustion, because things are moving so fast,” he says.

Gilbert adds that AI fatigue most commonly arises when AI is clunky, when organizations bolt AI on top of an existing process, rather than implement it as an in-line solution. Employees could be asked, for instance, to copy and paste data from their programs into a separate LLM like ChatGPT. But the method doesn’t take. “You’re frustrating the heck out of the employee,” Gilbert says.

On top of that, he adds that when AI isn’t properly integrated with a company’s data, or it lacks broader organizational context, the LLM can hallucinate, delivering outputs that, as he puts it, are kind of crap.

Stricklin also says when AI is an added layer instead of an integrated solution, it compounds friction when the purpose is to reduce it.

So the most successful CIOs don’t simply plug AI into existing systems and expect transformation, he says. They rethink the entire workflow and build AI into operations. And in the most seamless AI integrations, Gilbert says employees don’t really think about AI; they simply use a process and get better, faster results.

CIOs pressured from all sides

Gilbert says the clunky approach often happens because of a top-down push that ripples throughout the organization. Boards and CEOs may see case studies or articles of what other companies are doing related to AI, and want to jump on the bandwagon. The AI request trickles to the CIO, who then feels pressure to deploy a solution quickly, rather than take the time to develop an in-line system.

“The reality is you’ll never meet the false expectations they have in their heads,” Gilbert says, adding that boards and CEOs often have a utopian mindset of AI capabilities. Likewise, company investors often expect AI to slash costs, which pressures leadership to demonstrate immediate ROI from AI, Johar adds.

“They don’t always understand that you have to incur the cost before you save any cost,” he says.

In fact, a recent McKinsey survey shows that of the companies that participated, only 39% reported AI‑related impact on their earnings at the enterprise level, suggesting the majority of AI programs have yet to deliver meaningful financial results.

In addition to top-down pressure, sometimes CIOs are feeling stress from the ground up. Despite employee fatigue around AI, Johar’s team at BlackLine has seen requests for AI-based tools from other departments increase by up to 25%.

The higher volume of requests creates fatigue for the IT team itself, as they evaluate myriad tools. Increasing the challenge, the fast pace of change with AI means the team’s processes to evaluate technology have to evolve, too. By the time IT makes a decision to procure a technology or select a supplier, it’s possible the tech is already obsolete, Johar says.

BlackLine has also trained employees on how to build their own agents for specific departmental functions, and to date, employees have built nearly 300 AI agents. CIOs and their teams bear the responsibility of bringing governance and structure to the flood of agents, as Johar puts it, ensuring they meet corporate policies around data privacy or security.

As tech features such as vibe coding continue to gain traction, Johar anticipates additional questions will arise for CIOs related to software oversight.

Framing the AI narrative

Delivering business value continues to be a top priority for tech leaders, and Stricklin says the most successful CIOs establish clear business objectives — whether it’s increasing revenue or margins, or reducing cycle time — before an AI deployment.

But when persuading employees to embrace AI that ultimately creates business value, CIOs may need a different tact than touting the benefits.

Johar says CIOs should frame AI’s benefits as compelling from an employee point of view, like helping employees do their jobs more effectively and building skillsets. “Once you position it that way, employees become a lot more accommodating to invest their time,” he says.

In this kind of climate, Gilbert says CIOs need to reassure employees that AI isn’t a means to headcount reduction but about flipping the narrative to how AI will work alongside employees, not replace them. Gilbert adds that humans should always be in the loop to fine-tune models and improve the accuracy of AI’s outputs over time.

Finding the right balance is key, given the gap that still exists between leader and employee sentiment around AI. Executives are 15% more likely to say AI has had a significant positive impact on their companies than their employees are, according to a survey commissioned by Google Workspace.

Stricklin also advises CIOs to have a focused strategy for how they adopt AI instead of trying to boil the ocean and immediately implement AI organization-wide. So they should pick two to three priority areas to use AI over the next six months, and get employees involved with the best course of action. “Trying to address everything simultaneously will cause more harm than wins,” Stricklin says, adding that equally important is selecting areas in which an organization won’t pursue AI.

Gilbert agrees that not every facet of a business is enhanced by gen AI. CIOs should be mindful of that and not be afraid to push back against CEOs or boards if they suspect an AI deployment is unnecessary. “Sometimes AI isn’t the answer,” Gilbert says.