AI saves knowledge workers roughly 11 hours a week. That is the number that gets quoted in every boardroom slide and vendor pitch. What almost no one mentions is that 6.4 of those hours get consumed by something the Work AI Index 2026 calls “botsitting”, the invisible human labour of feeding AI context, correcting its mistakes, and cleaning up its output. Glean’s Work AI Institute surveyed 6,000 digital workers across the US, UK, and Australia, and the result is the first large-scale empirical accounting of what AI actually costs in human terms. The report introduces two paired concepts, botsitting and botshitting, that together describe a pipeline from hidden labour to abandoned oversight. This article walks through the numbers that define the broader botsitter economy.
What is botsitting, and why has the term suddenly entered the workplace lexicon?
Botsitting is the human labour required to make AI usable at work. It breaks into three activities: context-feeding (telling AI which documents are authoritative, what internal jargon means, which workarounds exist), error correction (identifying and fixing AI mistakes), and output cleanup (rewriting, reformatting, and verifying AI-generated work before it is usable). The Work AI Index 2026 coined the term, and it caught on because it names what millions of workers were already experiencing: they expected AI to reduce their workload and instead found themselves managing an unreliable digital colleague.
Rebecca Hinds, Head of the Work AI Institute at Glean and lead researcher on the report, put it plainly: “It’s exhausting for workers to not only do this, but to have the work be unrecognized, often unrewarded and unacknowledged within the organization.” The report draws on researchers from Stanford, Emory, UC Berkeley, UC Santa Barbara, UNC Charlotte, University College London, and the University of Notre Dame.
Not all botsitting is wasted effort, a nuance the next section unpacks in detail. But for most workers, it is tedious, unrewarded labour that sits outside any performance metric. The question that follows naturally is: how much of it is actually happening?
How many hours are workers actually losing to botsitting each week?
According to the Work AI Index 2026, workers spend an average of 6.4 hours per week on botsitting. That is 37% of total AI time, slightly more than the 36% of time they spend actually using AI to complete work. For every hour a worker gets productive output from AI, they spend roughly another hour making it usable.
The burden is not evenly distributed. High AI achievers spend less time on unproductive botsitting because they are selective about what they delegate, invest in context-rich tooling, and use AI as a teacher to build judgment. They practise the productive form of botsitting introduced in Section 1, using the oversight process to build expertise rather than just firefighting errors. Low achievers prompt, accept, and move on, which produces more errors to clean up later. The gap compounds over time.
Then there is the exhaustion multiplier. Botsitting is disproportionately draining. Debugging probabilistic outputs from a black-box system is cognitively harder than equivalent hours of productive work because the worker must work backward from an incorrect output without knowing which assumption or context gap caused the failure. It is investigative thinking rather than execution, and it burns through focus faster. Hinds identifies debugging as the biggest driver: “because of the nature of LLMs, you’re not quite sure why it’s broken.”
Context-poor workers, whose AI tools lack access to organisational knowledge, feel this most: 50% felt worn out by AI, compared with 18% among context-rich workers. Hours matter, but the real story is the failure rate that makes those hours unavoidable in the first place.
Just how often do AI agent sessions fail in enterprise settings?
The Work AI Index 2026 finds that 36% of AI agent sessions fail outright. That means the task was not completed, the output was unusable, or the agent stalled and required human intervention. At that rate, human oversight is mechanically required: with more than one in three sessions failing, abandoning verification means shipping broken work.
Deloitte’s 2026 State of AI in the Enterprise report, surveying 3,235 leaders across 24 countries, corroborates the picture. It found only one in five companies has a mature governance model for autonomous AI agents, and workforce readiness remains the top barrier to scaling AI. Agents are moving into production faster than the frameworks designed to govern them, and faster than most workforces are prepared to manage them.
Compounding the problem, 54% of workers have not read their organisation’s AI policy. The distance between documented governance and actual worker behaviour is wide. A 36% failure rate in an environment where half the workforce is unguided by any policy constitutes a structural risk, not a transient adjustment period.
The failure rate directly drives the botsitting hours. Every session needs a human checking the result, and those checks add up to 6.4 hours a week. The question the numbers push toward is: what happens when workers stop checking?
What is botshitting, and how does it differ from the labour of botsitting?
Botshitting is what happens when botsitting collapses. It is the practice of delivering AI-generated work that is unverified, poorly understood, or indefensible if questioned. Hinds defines it as “offloading your critical human thinking, judgment, and understanding.”
The distinction is straightforward. Botsitting is oversight labour: feeding context, correcting errors, cleaning up. Botshitting is oversight abandoned: shipping output as-is, hiding AI usage, blaming AI when things go wrong. Sixty-nine percent of AI users admit to some form of it, and 41% sometimes deliver work they could not explain if asked.
Botshitting is higher in context-poor environments and in organisations that have conducted layoffs, higher still when AI is named as the reason. The accountability dimension, which Section 5 explores in full, centres on a finding from Paul Leonardi, Duca Family professor of technology management at UC Santa Barbara and co-author of the report: when AI-generated work fails, workers default to blaming the tool, and the pattern intensifies with heavier AI use. Researchers call this moral disengagement, the gradual process by which people stop holding themselves accountable. The stat that makes the consequences concrete is the one the next section unpacks.
How many workers are shipping AI-generated work they cannot defend or explain?
The 69% botshitting rate, combined with a blame-deflection pattern Leonardi has documented, describes an accountability vacuum. When AI-generated work fails, 40% of workers blame AI while only 29% admit personal fault. Heavy AI users are 3.4 times more likely than light users to blame the tool. Nearly seven in ten workers are shipping work they cannot stand behind, and the default posture when it fails is to point at the machine.
The practical consequences are broad. In engineering teams, code quality degrades when AI-generated code ships without review. Legal exposure accumulates when AI-generated contracts or compliance documents go unverified. The Moffatt v. Air Canada case has already established that organisations are liable for what their autonomous agents produce, even when those actions contradict internal policy. Decision quality erodes when executives act on AI-generated analysis they cannot interrogate. Reputational risk grows when customer-facing AI output cannot be defended.
The measurement environment matters because it sets the incentives. When organisations track both quality and productivity, rather than productivity alone, botshitting drops from 74% to 64%. Tracking output volume without tracking output quality effectively tells workers that shipping unverified AI work carries no consequence. The numbers shift when the incentives do. All of which brings us back to the question the article opened with: what is actually left after all of this?
After you subtract the botsitting tax, how much time does AI actually save?
The arithmetic is tight. Eleven hours saved minus 6.4 hours of botsitting overhead leaves roughly 4.6 hours of net gain per week. That reframes AI as a marginal productivity improvement rather than the transformative leap the 11-hour headline implies. When you set that 4.6 hours against the cost of AI tool licences, the budget burn-through problem emerges: what are you paying, and what are you actually getting?
The average conceals wide dispersion. High AI achievers, context-rich workers, and employees in organisations that the report calls “transformative” capture a larger share of the gross 11 hours because they spend less time on unproductive botsitting. For others, the botsitting tax may consume nearly all the gross savings. The 4.6-hour figure also excludes the exhaustion multiplier: the real cost in worker wellbeing is higher than the hours arithmetic suggests. Workers who spend disproportionate time supervising AI are 73% more likely to be looking for a new job.
The gap between individual and organisational outcomes tells its own story. Seventy-five percent of individuals report a productivity boost from AI, yet only 13% say their organisation has seen significant gains. That gap is where the botsitting tax does its work.
The Work AI Index 2026 has given the hidden costs of enterprise AI a name and a number. Botsitting and botshitting are not separate problems; they are a single pipeline. One is the cost of making AI work. The other is the cost of giving up on making it work. The 6.4 hours of oversight, the 36% failure rate that makes it necessary, the 69% botshitting rate when it collapses, and the 4.6 hours of net gain at the end together describe the real empirical picture of enterprise AI in 2026. The data shows a marginal improvement carrying a structural cost most organisations have not priced in, a long way from the productivity revolution the headline numbers suggest. Whether organisations treat botsitting as a cost of doing business or as a signal that their AI deployment needs restructuring is the question the numbers put on the table — and the central tension of the emerging botsitter economy.
Frequently Asked Questions
Is botshitting the same as using AI to draft work that I verify afterwards?
No. Using AI to draft and then verifying, editing, and understanding the output before you ship it is productive AI use. Botshitting is the opposite: skipping verification entirely and delivering work you could not explain or defend if someone asked. The distinction is not whether AI was used, but whether a human took ownership of the result. The Work AI Index 2026 draws this line sharply. Verification is the boundary between competent AI use and botshitting.
Will AI eventually get reliable enough that botsitting disappears?
Not in the near term. The Work AI Index 2026 records a 36 percent enterprise AI session failure rate, which means oversight remains mechanically necessary. Even as models improve, the organisational context problem persists: AI will keep needing to be fed which documents are authoritative, what internal jargon means, and which workarounds apply. Reliability gains may reduce error correction, but context-feeding and output verification are structural requirements, not temporary growing pains.
How do I know if I am a high AI achiever or a low AI achiever?
The Work AI Index 2026 distinguishes high AI achievers by three behaviours: they are selective about what they delegate to AI rather than automating everything, they invest in giving AI rich organisational context before asking it to produce output, and they use AI as a learning tool to build their own judgment over time rather than as a black-box answer machine. If you mostly prompt, accept, and move on without verification or learning, you are likely in the low-achiever category.
What should organisations actually measure to catch botshitting?
Organisations that track both quality and productivity, rather than productivity alone, see botshitting rates drop from 74 percent to 64 percent. The Work AI Index 2026 suggests three practical measures: output explainability audits, where workers are periodically asked to explain the reasoning behind AI-assisted work; quality sampling of AI-generated deliverables against human benchmarks; and tracking whether AI usage is disclosed in review workflows. Measurement that ignores quality actively incentivises botshitting.
Is this just a large enterprise problem, or does it affect smaller teams too?
Botsitting and botshitting scale with AI adoption, not company size. Smaller teams may actually face higher per-person botsitting burdens because they lack the dedicated governance roles and context-rich tooling that larger organisations can fund. The Work AI Index 2026 draws from 6,000 digital workers across varied workplace sizes, and the patterns hold broadly. The difference is that in a small team, one person’s botshitting carries proportionally larger consequences for the entire organisation.
Are there legal or compliance consequences for botshitting?
Yes, and they are accumulating. When AI-generated contracts, compliance documents, or customer communications are shipped without verification, the organisation carries legal exposure for content it cannot defend in a dispute or audit. The Work AI Index 2026 highlights that 40 percent of workers blame AI for failures, but regulators and courts assign accountability to organisations, not to the language model. In regulated sectors like finance and healthcare, unverified AI output can trigger specific compliance violations beyond general negligence risk.
Why do not workers just admit when AI makes mistakes?
The Work AI Index 2026 finds a revealing asymmetry: 40 percent of workers blame AI for failures while only 29 percent admit personal fault. Paul Leonardi’s research on digital exhaustion suggests that the cognitive drain of constant AI oversight erodes the psychological resources workers need to take ownership. When you have spent hours debugging a black-box system that still produces errors, the impulse to deflect blame rather than absorb another failure is powerful. The accountability vacuum is as much about exhaustion as it is about ethics.
What is shadow AI, and how is it different from botshitting?
Shadow AI is the use of AI tools outside an organisation’s approved channels: workers using personal ChatGPT accounts, unauthorised browser extensions, or free-tier tools that IT has not vetted. Botshitting is the quality failure that can result from shadow AI use, but it also occurs inside approved tools. The Work AI Index 2026 treats them as related but distinct risks. Shadow AI creates the context-poor conditions that make botshitting more likely, because unauthorised tools typically lack access to organisational knowledge and governance guardrails.
Does this mean AI is not worth the investment for most workplaces?
No, but it means the investment case is narrower than the hype suggests. After botsitting overhead, the Work AI Index 2026 shows a net gain of roughly 4.6 hours per week: a real but marginal improvement. The question is whether the cost of AI tool licences is justified by that net gain, and the answer depends heavily on whether an organisation invests in context-rich tooling and quality measurement. AI produces returns, but they are earned through deliberate investment in oversight infrastructure, not delivered automatically.
Are some industries more vulnerable to botshitting than others?
Yes. The Work AI Index 2026 identifies that context-poor environments, where AI tools lack access to organisational knowledge, produce higher botshitting rates regardless of industry. But the risk is especially acute in sectors where output defensibility matters most: law, finance, healthcare, engineering, and any field where AI-generated work carries compliance, safety, or fiduciary consequences. Industries where quality is hard to measure at speed, such as consulting and marketing, also face elevated risk because botshitting is harder to detect before downstream damage occurs.
