Workslop, a term coined by the researchers and based on AI-generated “slop” you can find clogging your social media feeds, is defined as “AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.”
What the new research does show is just how pervasive and costly workslop has become within organizations.
Out of 1,150 full-time U.S. employees surveyed, 40% said they’ve encountered workslop in the past month. Just under half of this low-quality work is exchanged between colleagues at the same level. A further 18% of respondents said they received it from direct reports, while 16% said it came from managers or people higher up the corporate ladder.
Far from speeding up workflows, this AI-generated slop created more work, employees said. According to the research, employees spent just shy of two hours dealing with each piece of AI-generated work. Based on time spent and self-reported salaries, the researchers calculated that workslop could cost single employees $186 per month. For an organization of 10,000 workers, this could mean over $9 million a year in lost productivity.
The incidents have morale costs as well, with employees reporting being annoyed, confused, and offended when they receive the low-quality work. According to the research, half of the people surveyed viewed colleagues who produced workslop as less creative, capable, and reliable. They were also seen as less trustworthy and less intelligent.
Overall, employees receiving low-quality work were less inclined to collaborate with their colleagues.
Some level of AI-slop is a natural byproduct of current AI models. LLMs are designed to generate content quickly by predicting the most likely next word or pattern, not to guarantee originality or meaningful insight. Models also hallucinate, which can impact the accuracy of AI-generated work.
But the new research does point to a lack of employee understanding—or care—when it comes to using AI tools. Top-down AI mandates from leadership often emphasize experimentation without providing clear guidance. And while experimentation is part of adopting new tech, encouraging AI usage without direction can pressure employees to produce output even when it’s inappropriate.
So how do companies stem the tide of workslop? The researchers’ suggestions include more guidelines for when and how AI should be used, encouraging purposeful, rather than shortcut-focused, use of the tech, and fostering collaboration and greater transparency between employees on AI use. Without these measures, companies rushing to adopt AI risk creating more friction than efficiency.
With that, here’s more AI news.
Beatrice Nolan
