One of AI’s biggest selling points is all the high-value tasks employees will be free to accomplish with the time saved using AI. Reality, however, remains far from that.
While IT workers and other employees do save several hours each week thanks to AI, more than half of that time is burned up babysitting the technology, a new study reveals.
According to a , rerunning prompts, and , CIO at digital adoption platform provider WalkMe.
WalkMe’s , founder of the organization.
“The surprise was how prevalent it is,” she says. “The fact is that workers are spending roughly the same share of their AI time botsitting as they are using the technology to move work forward.”
Moreover, while 87% of digital workers, and 97% of IT workers, said they use AI at their jobs, only 13% believe their use of the tools has led to significantly improved performance or outcomes.
Part of the problem is a phenomenon Hinds calls “coordination neglect.” Employees often focus on their own productivity without considering the broader benefits to the organization, she says. As a result, their AI-assisted work sometimes conflicts with another employee’s work.
“I can use the technology to, say, convert a single bullet point into a five-page report,” she says. “I can then ship that five-page report to a colleague, but the colleague sees that it’s so much content. They can use the same AI tool to then convert the five-page report back into a series of bullet points.”
In some cases, employees do divert AI time savings to personal activities, but the most common use of the time saved, according to survey respondents, is to improve the quality of their work, Hinds says. Overall, however, organizations aren’t seeing a major quality improvement, she adds.
Shipping AI-generated work that workers haven’t verified, don’t fully understand, or can’t confidently stand behind is a significant issue, according to the AI Work Institute report.
And then there’s the “AI toggle tax” — when employees switch between multiple AI tools to do their jobs, which leads to additional unverified work. Moreover, as employees become overwhelmed with their work to AI.
“They hand more of their thinking and judgment over to the machine,” the report says. “They start to cut corners. They stop checking outputs, verifying sources, and asking whether the AI’s recommendations make any sense.”
Governance problems at the core
Botsitting, and giving in to AI slop, are real but also symptoms of a larger governance problem, says , CTO at HR platform Click Boarding. Verifying outputs, iterating on prompts, and adding domain context for the AI tool to use are good engineering practices, when done right, he notes.
“The issue is that organizations aren’t distinguishing between what’s worth doing versus a symptom of a poorly deployed tool,” he says.
A big problem is a lack of context for AI tools, he suggests. “When AI tools don’t have access to accurate data and aren’t built in the right way to make their output usable, employees become the integration layer that re-explains a project to every tool and fixes what breaks,” Wachtel says.
Meanwhile, the 6.4 hours spent botsitting aren’t evenly distributed and instead fall on employees already engaged in detailed work, such as senior engineers, he says.
“You have others skipping that verification, thinking they’re saving 11 hours, and then may not be responsible for the mess that comes of it — often downstream when code breaks or a process stops working,” he adds.
Individual productivity gains don’t automatically add up to organizational ones, Wachtel adds. For example, if an engineer builds code faster, someone else may have to verify it. One employee’s time savings creates work for someone else.
Many organizations also struggle to measure quality of AI outputs, he adds. IT leaders should educate the full C-suite on the metrics that matter the most, rather than how many times an AI tool was used, he recommends.
“Organizations are touting efficiency gains, but I don’t see a lot of chatter around agents’ accuracy, continuous improvement, or cost takeout that are more impactful to align to,” he says. “A lot of AI was developed and launched for speed rather than for impact, and so the right people weren’t involved or trained.”
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