Here is a thing that happens to a lot of small business owners right now.
They spend a few months getting their AI setup dialed in. They subscribe to the right tools. They learn the prompts. They build the workflows. They tell themselves - and mean it - that this is finally going to give them their time back.
Then six months pass, and something feels off. They're busier than before. Not in the good way. The kind of busy where you end every day feeling like you ran hard but didn't move far. The AI is working. The automations are running. The calendar is full. And somehow, there is never enough time.
For a long time, that feeling didn't have a name. Now it does.
Botsitting
Researchers from Glean's Work AI Institute, working alongside academics from Emory University, Stanford, UC Berkeley, and several other institutions, surveyed 6,000 full-time digital workers across the United States, the UK, and Australia for their Work AI Index 2026. The findings started circulating this week.
The headline number is one of those statistics that sounds like good news until you read the next sentence.
AI is saving workers an average of roughly 11 hours a week. That's the promise delivered - real, meaningful time savings from real tool usage.
The next sentence: those same workers are losing more than six of those hours back to something the researchers call "botsitting."
Botsitting is the accumulated overhead of keeping AI useful. It's the time you spend re-feeding context to a tool that doesn't remember your preferences. Supervising outputs you can't fully trust yet. Debugging an automation that quietly broke last Tuesday. Re-explaining your brand voice to a content tool because something got reset. Manually copying data between three apps that were supposed to connect but don't quite.
Every one of those tasks is real work. None of it shows up in the productivity math.
The net number, after you run the subtraction, is roughly four to five hours of genuine time savings per week - assuming you're doing everything right. For many people, the actual number is lower.
Why Small Business Owners Feel This More Than Anyone
The Work AI Index data comes primarily from corporate employees at larger organizations. Which means the real picture for small business owners may be worse.
When a 200-person company implements AI tools and workers start losing hours to botsitting, there's an IT team that can troubleshoot integrations. There's an operations manager who can rebuild broken workflows. There's an L&D budget to train people on the new stack. The overhead gets absorbed across teams and time.
When you're running a business with five people - or by yourself - that absorption layer doesn't exist. The botsitting hours come directly out of your time. That's the same time you use to talk to clients, build the product, close deals, and occasionally sleep.
The r/AiForSmallBusiness community - before it went offline last week - had a thread on exactly this that picked up real traction. The premise: at what point does an AI marketing stack become a liability instead of an asset? The answers were illuminating. One founder described their setup as "context switching hell" - moving between five tools that each require different syntax, different prompt approaches, and different manual exports. Another called it "managing a team of five interns who don't talk to each other and all have different personalities."
That's not a complaint about AI. That's a diagnosis of tool sprawl.
There's a Second Problem: Nobody's Measuring Any of This
Intuit runs an annual survey of small businesses that use their QuickBooks platform. For their 2026 AI Impact Report, they surveyed more than 34,000 small to mid-size businesses. The findings: 77% of US SMBs now regularly use AI. 74% report improved productivity. 41% say their revenue has increased.
Those are genuinely encouraging numbers. Here's the problem buried a little further in: more than half of those businesses are measuring their AI improvement by a general feeling. Not actual before-and-after tracking. Not a time audit. A feeling.
That matters because feelings are bad at accounting for botsitting. You might feel more productive because your output per hour has increased - while simultaneously spending more total hours working, because the overhead has grown alongside the output. Both things can be true. The feeling tends to capture the first part and miss the second.
If you've never actually calculated how many hours per week you spend feeding AI context, verifying AI outputs, and switching between disconnected tools - you don't know your real number. Most business owners who run this audit for the first time are surprised.
The Botsitting Audit
Here's a simple way to get your actual number. For one week, track every time you do any of the following:
- Re-enter context or instructions into an AI tool that should have remembered them
- Read through and correct AI-generated output before sending or publishing
- Manually copy data between AI tools (or from AI output into another system)
- Troubleshoot a broken automation or workflow
- Switch from one AI app to another to complete a single task
Log the time. At the end of the week, add it up.
Most people who do this for the first time land somewhere between two and eight hours. If you're at the high end, you're not getting a time-savings benefit from AI - you're breaking even at best, and working more at worst.
The Counterintuitive Fix: Fewer Tools
The businesses that report the lowest botsitting overhead have something in common. They're not using fewer AI tools because they're behind on adoption. They're using fewer AI tools deliberately.
The pattern that shows up consistently in small business communities right now: owners who picked one or two tools and built real fluency with them over 90 days, rather than rotating through every promising new release, end up with significantly less context-switching overhead. The tools remember them better (because they're used daily, consistently). The outputs are cleaner (because the prompts have been refined over dozens of iterations). The integrations are stable (because there are only two of them instead of seven).
The promise of AI is time. But that promise requires maintenance to keep. And maintenance, like everything else, costs time.
The owners getting the best return right now are the ones who've drawn a line somewhere and held it.
The Vocabulary Helps
There's something useful about having a word for this. "Botsitting" is going to land differently than "AI overhead" or "tool sprawl" or "I feel like I'm working more than I used to." Those descriptions are fuzzy. Botsitting is specific.
If you're six hours deep into your week and most of those hours went to managing AI rather than doing actual work, you can name that now. You can put it in the category it belongs in - not failure, not falling behind, not bad AI adoption. Just overhead that got away from you. Overhead that can be reduced with the same deliberate approach you'd take to any other cost in the business.
The tools are not the problem. The accumulation is.
Sam Torres covers productivity and AI adoption for small businesses. Sources: Glean Work AI Institute Work AI Index 2026 (6,000 workers, US/UK/Australia); Intuit QuickBooks AI Impact Report 2026 (34,000 SMBs); r/AiForSmallBusiness community thread on AI marketing stack overhead.