‘Botsitting’: The AI time-savings killer only governance can stop

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 survey from the Work AI Institute, digital workers save an average of 11 hours a week through AI, but the net time savings is much less, because they spend 6.4 hours a week “botsitting.”

Botsitting involves activities such as feeding AI tools missing context, checking AI outputs, debugging AI mistakes, rerunning prompts, and cleaning up the confident-but-wrong answers they leave behind, as defined by the Work AI Institute, a research group founded by AI copilot and search provider Glean.

The botsitting problem is real, several IT leaders agree, and it has serious implications for IT organizations. In many cases, organizations aren’t training their employees to effectively use AI, says Tal Carmi, CIO at digital adoption platform provider WalkMe.

WalkMe’s 2026 State of Digital Adoption report found similar results, with employees losing nearly eight hours a week to botsitting, Carmi notes. At the same time, most employees use AI for shallow tasks like writing emails because they don’t trust it for more complex activities, WalkMe found.

As a result, enterprises aren’t getting the full ROI of their AI purchases, Carmi says, a significant issue for CIOs and organizations in general.

Hours wasted

Going into the survey, researchers at the Work AI Institute suspected botsitting was a problem for many organizations, but the results were eye-opening, according to Rebecca Hinds, 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 AI tool sprawl, they cognitively offload 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 Frank Meltke, CEO of digital transformation consulting firm contraco.

“Workers are spending nearly a full day verifying AI output because nobody at deployment defined what verification was required, who owned it, or what good output looks like before it moves downstream,” he says. “That is a governance gap, not a tool problem.”

Meltke also doubts there’s a net time-savings gain of four-plus hours per employee each week when their fellow workers sometimes must redo their AI-assisted outputs.

More than two-thirds of digital workers surveyed admit to shipping AI-assisted outputs they have not verified, he notes. “That output lands on someone else downstream, usually without context to fix it,” he says. “The 4.6-hour net gain at the individual level gets absorbed invisibly at the team level as rework nobody budgeted for.”

This phenomenon explains why time savings observed by individual employes does not show up in organizational performance, he adds. “The productivity gain was never real savings,” Meltke says. “It was a transfer of labor from the person who generated the output to the person who inherited it.”

Not all botsitting is a bad thing, however, says Adam Wachtel, 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.”