AI Saved 11 Hours. Botsitting Took 6.4 Back

Glean’s survey, as written up by Insurance News on Sept. 22, asked 6,000 digital workers about AI at work. Eighty-seven percent use it. About three-quarters said it makes them more productive, and they put the savings at about 11 hours a week. The same writeup says they spend 6.4 hours a week botsitting: checking output, fixing errors, rerunning prompts, and stuffing in context the model did not have.

That is not a net figure the study published. Do not subtract 6.4 from 11 and call the remainder your raise. The two numbers measure different slices. One is a self-estimate of time saved. The other is time spent supervising the tool. Both can be true in the same week, and both can be wrong in the same way self-estimates usually are. People are bad at clocks. They are worse at clocks attached to a tool they want to justify.

If you already read the piece on how people waste the hours AI frees up, this is the other side of that ledger. The hours were never clean.

The 11 hours and the 6.4

The Insurance News account of the Glean report is specific about the split inside AI time, not just the weekly totals. Workers spend 37 percent of their AI-related time botsitting. They spend 36 percent of that same AI time actually producing work. The leftover, more than a quarter of AI-related time, goes to learning the tools and building agents.

Read that split slowly. Inside the hours people label as “using AI,” production and supervision are almost the same size. The writeup’s practical line is blunt: for every hour of usable work from the tool, roughly another hour goes to its shortcomings.

I have not seen Glean’s PDF. The figures above are what the Sept. 22 article attributes to the survey. If your company bought a dashboard that only shows the 11 hours, ask whoever owns it whether botsitting is even a field. A savings number with no supervision number is a brochure.

Self-estimates also stack. Someone who says “I saved 11 hours” may be counting the first draft, the meeting they skipped, and the email they did not rewrite by hand. Someone who says “I spent 6.4 hours checking it” may be counting the same afternoon twice, once as savings and once as review. The survey does not untangle that. Treat both as directional, then measure your own week with a timer if the decision is a headcount or a tool budget.

What botsitting actually looks like

The report’s definition, again via that writeup, is ordinary. You read the output. You correct a wrong name, a stale number, a confident paragraph that cites a file you never uploaded. You rerun the prompt because the first pass missed the constraint you thought was obvious. You paste the missing context and wait again.

None of that is exotic. It is the same loop as editing a junior colleague, except the colleague does not get tired and does not remember the correction next Tuesday unless you put it in a file the tool can see.

The time sink is the re-run, not the first read. A two-minute scan that turns into a 20-minute repair is the hour the 6.4 is made of. If your team has no shared place for “what the model got wrong this week,” every person pays the repair cost separately. That is not a training problem yet. It is a notes problem.

Building agents sits in a different bucket in the same survey: more than a quarter of AI-related time goes to learning and to constructing agents. That work can be useful. It is not the same as finishing the task you opened the chat to finish. If your calendar says “deep work” and the deep work was wiring a bot that still needs you in the loop, you did not get the 11 hours. You spent them.

The company number does not move

Here is the figure I keep coming back to. Just 13 percent of those workers said AI has improved their organization’s overall performance. Three-quarters feel personally faster. Thirteen percent see it in the org.

That gap is the whole argument, and it is easy to wave away. People always think they are the productive ones. Organizations always lag the tool. Fine. The survey still puts a number on the lag, and 13 percent is small enough that a rollout deck quoting only the 11 hours is leaving out the result the buyer actually paid for.

Insurance News floats the oversight load as one reason the org number stays flat. I think that is plausible and incomplete. Supervision eats hours. So does the fact that the saved hours are not automatically reassigned to anything the company measures. If the 11 hours dissolve into a longer lunch, a cleaner inbox, and a bot that still needs a human in the loop, the P&L does not move. Personal relief and organizational output are different products. Most AI rollouts sell the second and deliver a mix of the first.

If you manage people, the question to ask in the next staff meeting is not “are you using it?” Eighty-seven percent already are, in this sample. Ask what share of their AI time is production versus repair, and whether the repaired hours show up in a deliverable someone else can see. If they cannot answer, you do not have a productivity program. You have a chat window.

No clock time to get better

A separate study, covered by ZDNET on Sept. 23, comes at the same week from the training side. Workera surveyed 1,000 salaried professionals at US organizations with 5,000 or more employees. The fieldwork was July 2026, via Pollfish. ZDNET’s headline says nearly 70 percent of workers use AI regularly and many get no time to upskill. The body is more precise than the headline.

Fifty-six point four percent said no time is allocated during work hours for upskilling. Forty-two point five percent named a lack of relevant learning materials as a top obstacle. Eighty-four point three percent spend five hours or less a week on training. At the same time, 80 percent of companies in the study feel they are more likely to be on track for an AI-enabled future in 2026, up from 67 percent last year.

Put the Glean split next to that. People are spending more than a quarter of their AI time learning, and more than half of this large-employer sample has no work time set aside for it. The learning is happening in the cracks, or it is the botsitting itself, which is a miserable way to learn. You learn the failure modes of one prompt, not the job.

Company confidence going up while scheduled practice stays near zero is a familiar pattern. Leadership sees licenses. Staff see a tool they are supposed to already know. The 42.5 percent who cannot find relevant materials are not asking for another webinar about “the future of work.” They are asking for a worked example that matches the file types and the review standard on their actual desk.

Five hours or less of training a week, for 84.3 percent of the sample, also includes people who spend zero. “Or less” is doing a lot of work in that sentence. If your team’s number is zero, the Glean learning-time share is coming out of nights, or it is not happening, and the 6.4 hours of botsitting is the curriculum.

Home use is wider than work use

Workplace Insight on Sept. 24 cites the WIN World AI Index for a smaller, sharper gap. Twelve percent of people use AI every day for personal purposes. Ten percent use it every day at work. The daily-use gap is two points. The interesting part is what they do with it.

At work, the top cited uses are narrow: research and data analysis at 38 percent of users, workflow automation at 28 percent. Outside work, entertainment leads at 39 percent, schoolwork at 32 percent, content creation at 31 percent. People already use the tools to answer questions, study, and make things when nobody is watching the license. At the office the same people stay inside the approved lane.

I do not read that as “let people generate party invitations on the work laptop.” I read it as evidence that fluency is being built off the clock, then left at the door. If the only sanctioned work uses are research and automation, the botsitting load on those two tasks will stay high, because that is where the mistakes are expensive and where the practice is thinnest.

The two-point daily-use gap is easy to dismiss. Do not. A tool people will open every day at home and not every day at work is a tool the workplace has made slightly more annoying than the alternative. Sometimes that annoyance is a reasonable control. Sometimes it is a login wall, a banned file type, and a review process that takes longer than doing the task by hand. The WIN numbers do not say which. Your access logs might.

What to change on Monday

None of these studies hands you a schedule. This part is the practical reading, not a finding they published.

First, split the AI block on your calendar into two labeled chunks. One is production: the draft, the summary, the first pass at the spreadsheet. The other is review, and you timebox it. If review eats the production block, stop and write down the miss. A shared note of repeated misses is worth more than another prompt template. The Glean split says review is already almost half of AI time. Pretending it is free is how the 6.4 hours hide inside “I was using AI.”

Second, stop building agents in the same block as the task the agent is supposed to do. The survey already separates those hours. Your day should too. Agent construction is a project. It gets a ticket, a reviewer, and a kill date. If it still needs you to check every output after two weeks, it is not saving the 11 hours. It is a new coworker with no memory. Park it next to the meeting load problem: both steal the block you thought was focused work, and both survive because nobody names them on the calendar.

Third, if you are in the 56.4 percent with no work time for learning, ask for a recurring 45 minutes, not a course. One worked example from your own files, reviewed by someone who knows the standard, beats a library you will not open. ZDNET’s account of Workera says materials are a stated obstacle for 42.5 percent. Bring the material yourself: one redacted task, one bad model output, one correction. That is a training session. A link to a vendor academy is not.

Fourth, if you manage the rollout, stop reporting only hours saved. Pair it with hours spent checking, and with the 13 percent question: has anyone outside the user’s own estimate seen the org get faster? If the answer is still a feeling, you are not behind on adoption. You are behind on measurement. Adoption at 87 percent with an org-level yes at 13 percent is not a training gap you can close with a lunch-and-learn. It is a scoreboard that only counts the flattering half.

A timer for one week will teach you more than another survey. Mark production, review, and agent-building as three codes. If review matches production, you are living the Glean split, and the Monday change is a shorter review box plus a written miss log, not a new model. If agent-building is the biggest code, you are not behind on output. You are building a tool during the hours the tool was supposed to return.

The 11 hours are real to the people who reported them. So are the 6.4. Until someone on your team can point at a deliverable that exists because of the difference, the honest status is: we are busy with the tool, and the organization has not cashed the check.