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Understanding 'Slop' and The Rise of Low-Quality AI Content
Jan 28, 2026 8:00:00 AM
Merriam-Webster marked its annual tradition last month and announced that “slop” had earned the title of word of the year for 2025. They defined slop as: “Digital content of low quality that is produced usually in quantity by means of artificial intelligence.”
In other words, "you know, absurd videos, weird advertising images, cheesy propaganda, fake news that looks real, junky AI-written digital books.”
As we kick off 2026, let’s take a look at what slop is, the problems it can cause, and some steps you can take.
What Work Slop Looks Like
We all know that AI use is increasing in the workplace. Most likely, you have seen an increase in:
- Generic emails that all start to sound similar
- Business content that’s repetitive and lacking substance
- Resumes that almost exactly match your job postings
Why Employers Should Be Concerned
While AI slop can be annoying, it can also pose business risks. It can erode your brand as customers and employees see that you don’t care enough to create quality work. It can erode productivity because even though there may be quick output with AI, there are later revisions and clean-up. If employees are encouraged to heavily use AI and outsource their thinking, you move away from creativity. And of course, legal exposure with AI-generated documents and communications that are not legally correct or that conflict with existing policies and practices.
What Employers Can Do
You can take a few steps to help preserve human judgment and the quality of work.
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Initial AI policies from the early days of ChatGPT may already be outdated. It is not enough to say what AI is allowed to be used for; you should also spell out what needs to be avoided, and insist that AI output must be reviewed for accuracy and consistency with company standards.
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Managers need to recognize overly generic language, perfect paragraphs that lack a human voice, repetition, excessive use of dashes, and buzzwords.
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For items relating to compliance, make sure you require human review.
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Prioritize data quality, not just quick output.
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Invest in proper training and oversight.
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Evaluate AI solutions on a regular basis.
