Companies that went all-in on using generative AI tools are developing a major “workslop” problem.

In their pursuit to boost productivity, become less reliant on human labor, and reassure investors that they’re riding the cutting edge of tech, some nagging issues are cropping up. As the Harvard Business Review points out, over-relying on AI can prove disastrous for organizational knowledge, the critical business insights companies need to make strategic decisions.

The phenomenon, dubbed “knowledge decay,” describes the deterioration of information over time, marked by workers forgetting skills and organizations relying on outdated processes. In the context of AI, it can be a dangerous downward spiral that starts with workers using AI to produce low-quality work, which wastes colleagues’ time, erodes trust, and gradually sloppifies organizational knowledge into worthless soup.

Multiply that by entire departments, and a business’s outputs start to crumble as well, as the Harvard Business Review explains.

It’s an already familiar trend. Even in the early days of the AI boom, experts warned that employees may be spending more time hunting down the many errors being made by unreliable and hallucinating AI tools than if they weren’t using the tech at all. Some companies even resorted to hiring workers specifically to fix AI errors.

“Errors compound and pile up,” the Harvard Business Review writes. “Trust in information erodes. People spend more time verifying facts or risk costly and dangerous mistakes. Eventually, people start to lose trust in the processes that they rely on to do their jobs.”

None of this should be particularly surprising to workers, who have been forced to use AI against their will at work as they watch the labor market deteriorate in real time. Their bosses’ insistence on AI has triggered a mass rebellion, with disillusioned workers suffering from low morale and even sabotaging the tech at work.

Hiring new workers has also turned into a quagmire for companies in large part thanks to AI, making the recruiting of qualified candidates extremely difficult.

“The overall impact of AI ‘augmenting’ each step is that it has sunk trust in the process to all-time lows for both job seekers and recruiters,” the Harvard Business Review explains.

To stop the “slopification” of work and knowledge decay, business leaders are now forced to ensure that information is being meticulously verified and cleared of AI hallucinations or errors, a labor-intensive process that eats up the time of actual human employees.

Going forward, a shift is clearly needed: employers need to figure out how to ensure that AI is only being used when it actually makes sense and adds value.

“For many tasks, using public LLMs often adds little to no real value,” the Harvard Business Review wrote. “It creates generic prose that often contains mistakes. But the use of proprietary models and/or leveraging proprietary data may well add value.”

In short, now that much of the early hype about AI leading to a productivity revolution and making human labor a thing of the past has been stripped away, and companies that embraced AI are forced to pick up the pieces or risk deteriorating into irrelevancy — an AI hangover that could haunt them for years to come.

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