Imagine it’s Tuesday afternoon and a colleague sends you a report. It looks good at first: clear headings, tidy bullet points, a confident summary. Then you start reading. Several paragraphs say very little, one of the numbers doesn’t seem right, and the final recommendation barely relates to the project.
You spend the next two hours checking facts, fixing the argument and rewriting sections. Meanwhile, your colleague is pleased with how much time AI saved them.
In September 2025, researchers from BetterUp Labs and Stanford’s Social Media Lab described this problem in Harvard Business Review. They called it “workslop”: AI-generated material that looks professional but doesn’t contain enough substance to move the work forward. It might be a report like the one above, a memo full of impressive language and little information, or a presentation that covers every angle without reaching a conclusion.
How common is workslop?
The researchers surveyed 1,150 full-time desk workers in the US. Around 40% said they had received workslop in the previous month, and those who had encountered it estimated that roughly 15% of the work they received fell into this category. Managers appeared to deal with it more often: 54% reported receiving workslop, compared with about 38% of individual contributors.
Respondents also recognized the problem in their own work. More than half acknowledged that at least some of what they send to others could probably be described as workslop.
Someone still has to do the work
On average, respondents said they spent almost two hours on each instance of workslop. Sometimes that meant correcting errors or rewriting the document; other times it meant going back to the sender to work out what they were trying to say.
Based on the salaries participants reported, the researchers estimated that this effort costs around $186 per employee each month, which could add up to more than $9 million a year for an organization with 10,000 employees. These figures rest on self-reported time and salaries rather than measured costs, but they give a sense of the scale.
The cost is easy to overlook because of where it lands. One person uses AI to finish something quickly, and another spends hours making it usable, so the time saved has been passed along rather than eliminated. If an organization measures productivity by how many documents, reports or presentations people produce, that transfer stays hidden. The sender appears more productive, while the colleague who spends the afternoon fixing their work has less time for their own responsibilities.
It changes how colleagues see each other
The researchers found that workslop also affects working relationships. More than half of recipients said they felt annoyed after receiving it, and around one in five felt offended. Roughly half viewed the sender as less creative, capable or reliable afterward, and 42% considered them less trustworthy.
Those reactions compound when the same colleague keeps sending material that needs substantial corrections. Eventually you stop taking their work at face value: you check their numbers more carefully, question their conclusions and avoid relying on them for important tasks. The sender may have saved twenty minutes, and rebuilding their colleagues’ confidence can take far longer.
Why do capable people send workslop?
Laziness is the obvious explanation, but we don’t think it accounts for most cases. The researchers point to workplaces where employees are encouraged, or even required, to use AI as much as possible without much guidance on what good use looks like. Add pressure to produce more in less time, and the problem follows.
AI makes a polished document easy to produce. The headings are there, the language sounds professional and the argument appears to flow, so under time pressure the result can feel finished. Whether a recommendation fits the situation, or whether a well-organized report contains the information the reader needs, is something only subject knowledge and judgment can tell you.
We’ve discussed a related concern before: if people rely on AI to do too much of the thinking, they may gradually lose practice in the skills they need to evaluate its output. That makes workslop harder to catch at both ends. The person generating the material may not notice what’s missing, and the person receiving it may assume someone else has already checked.
The heaviest AI users send less of it
Earlier research by BetterUp and Stanford sorts workers into two groups. “Pilots” combine a strong sense of agency with optimism about AI, while “passengers” are low on both. Pilots use generative AI 75% more often at work than passengers and are more likely to use it to extend their own creativity. Passengers are more likely to use it to avoid doing work. According to BetterUp, pilots are also half as likely to send low-quality AI work.
In other words, the people who use AI most produce less workslop. What separates the two groups is what happens after the AI produces a draft. A pilot might use AI for a first version of the report, then check the numbers, cut the filler and rework the recommendation until it fits the project. A passenger forwards whatever came out because it looks complete. The tool is the same in both cases, and the difference in quality comes from the effort put in afterward.
Shortcuts paid for with someone else’s time
That makes workslop a matter of individual responsibility as much as company policy. Pressure to use AI and produce more can explain why someone sends a half-finished document, but it doesn’t excuse it. The sender usually knows, or could find out in a few minutes, that the work isn’t ready.
For the sender, workslop can feel like beating the system: a task gone from the to-do list in twenty minutes, with nobody apparently the wiser. Someone does notice, though. The colleague who spends two hours fixing the document is paying for the shortcut, and as the survey shows, they’ll remember who sent it.
People who get good results from AI treat its output the way careful professionals have always treated a first draft from any source, whether a junior colleague, a template or a search result: as material to check and improve. Workslop starts when someone treats that first draft as finished work.
When workslop becomes company knowledge
The cost of a shortcut doesn’t always end with the colleague who fixes it. A weak email gets deleted and forgotten, but a weak report can stay around for years. It gets saved in a shared folder, quoted in another presentation or added to an internal knowledge base, and months later someone uses it as a reference without knowing how it was produced. Increasingly, that someone may be another AI system.
In a June 2026 Harvard Business Review article, Matthias Holweg of Oxford and Thomas Davenport of Babson College called this organizational risk “knowledge decay.” They argue that when low-quality AI-generated material enters an organization’s knowledge systems, it can gradually undermine the reliability of the information people depend on. Their framework hasn’t yet been tested in controlled studies, so it’s best treated as a plausible risk rather than a proven outcome.
Habits that reduce workslop
The researchers’ concern is AI-generated content passed along without enough human attention, which points to a simple principle: if you send a piece of work, you’re responsible for its quality, however you produced it. A few habits help:
Read what you’re sending. Check whether the information is accurate, whether the conclusions make sense and whether the document answers the question it was meant to address.
Make sure you understand it. If a colleague asked about the reasoning behind any recommendation, could you explain it? A paragraph that sounds impressive but that you can’t explain needs more work.
Say what you’ve checked. There’s nothing wrong with mentioning that AI helped draft a report. It’s more useful to tell colleagues which facts you’ve verified, which assumptions remain and where you still need input.
If you manage a team, ask about quality as well as speed. Asking how someone reached a conclusion or verified a result sends a different message from praising them for producing more material.
When you receive workslop, say something. Constructive feedback helps more than quietly fixing the document each time, since the sender may believe they’ve done a good job and have no idea how much extra work they’re creating.
A document that takes ten minutes to produce and two hours to repair costs the team time overall, even if the sender’s numbers look better.
Have you received workslop from a colleague? And have you ever caught yourself sending it?
What they are calling "workslop" is just anti-information at the corporate level.
The AI isn't doing the work; it is generating the appearance of work. It gives you a perfectly formatted document—the clean headings, the bullet points, the confident summary—to stop you from looking for the substance. The document is real, but the truth is in the gap.
This is the exact same architecture we see in politics and media. It manufactures the legibility (the surface) while creating a massive void in the depth. The colleague gets the credit for the "speed," while the reviewer absorbs the friction of actually fixing it.
It is the "Office Supplies" receipt. The paperwork looks perfect, but the money was stolen. The machine doesn't want to do the work; it just wants to control the rules of legibility and shift the labor to the person who actually has to read it.