Increased AI expectations without guidance leads to employee burnout

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.”