A new Glean report says white-collar workers spend an average of 6.4 hours a week supervising AI—feeding it context, checking outputs, debugging mistakes, and cleaning up errors—work they say is often tedious and exhausting. The report links the burden to job-
By midweek, many office workers are already spending more time than they expected on a task that wasn’t supposed to exist at all: supervising AI.
A new Glean report found that white-collar employees spend an average of 6.4 hours a week “botsitting” AI—feeding it context, checking outputs, debugging mistakes, and cleaning up errors. The work, the report says, amounts to most of a full working day each week.
“Workers now burn an average of 6.4 hours a week botsitting — most of a full working day, every week,” the report said.
The report’s numbers come from a survey of 6,000 full-time workers in the US, UK, and Australia who primarily work on computers or digital tools, conducted between December 2025 and January 2026.
That term—“botsitting”—was coined by the report’s authors to describe the often-overlooked labor required to make AI actually useful. Even as AI is marketed as a time-saver, the findings point to an opposite reality for many employees: hours spent correcting tools instead of using them.
More AI at work, but less companywide improvement
The tension runs through the results. In Glean’s survey, 87% of workers said they use AI at work, and 75% said it makes them more productive. But only 13% said their organization was performing significantly better because of it.
For Rebecca Hinds, head of the Work AI Institute at Glean and one of the report’s authors, the gap shows up clearly in what employees are being asked to do.
On Glean’s “Cognitive Revolution” podcast on Wednesday, Hinds described botsitting as “often tedious,” “exhausting” work that is “not rewarded and it’s not appreciated or tracked or measured and certainly not incentivized within the organization.”
In her view, much of the missing productivity comes from work employees never expected to take on: the time spent absorbing AI’s failures and compensating for them.
As tasks pile up, workers look to leave
The report also traces how the burden affects morale. Workers who spend an unusually large share of their AI time botsitting are 73% more likely to be actively looking for another job.
“Workers who absorb it without recognition or reward grow exhausted. Then they grow resentful. Then they start polishing their résumés,” the report said.
The frustration isn’t just about time. The report describes how employees often become the bridge between systems that don’t communicate smoothly—moving information between disconnected AI tools. fixing mistakes. and supplying context that the tools should already have. In effect, employees are pulled into the middle of technology that breaks down in real workflows.
In some cases, the pressure extends to the most enjoyable parts of work. Hinds said on the podcast that employees are sometimes asked to automate tasks they actually like doing.
Pointing to customer-service employees who enjoy building relationships, Hinds said they are increasingly expected to supervise AI agents instead.
“That’s what gives you joy and meaning at work,” she said. “That is very dangerous.”
What separates the companies that improve from those that stall
The report argues that the fix isn’t simply deploying more AI.
Organizations seeing the biggest gains are often doing more work around AI—helping employees access the right context, teaching them how to use the technology effectively, and establishing clearer standards for what good AI-assisted work looks like.
“The companies pulling ahead are doing something different,” the report said. “They aren’t spending a greater share of their AI time using AI. They’re spending a greater share on the work around it: setting context. defining what ‘good’ looks like. building judgment. and deciding what should never have been handed to a model in the first place.”.
For companies that don’t make those adjustments, the report warns that employees will keep paying the botsitting price. It also warns of a more permanent cost: “in the steady departure of the people who got tired of cleaning up after the bots.”
An editorial rhythm is emerging from the facts themselves: AI use is rising, self-reported productivity is high, and yet company performance gains lag—while the labor of supervision keeps expanding into a weekly burden that workers say drains them and pushes them to search for a way out.
Glean Work AI Institute botsitting AI productivity paradox workplace automation job satisfaction employee morale AI supervision workplace AI