TL;DRHBR says companies that went all-in on AI face “knowledge decay” as low-quality outputs pile up, erode trust, and cost $9M a year in rework.
Companies that pushed hardest to adopt generative AI are now contending with a problem the technology was supposed to prevent: their work is getting worse. Two articles published by Harvard Business Review this month describe a feedback loop in which AI-generated low-quality output degrades the information companies rely on to make decisions, a phenomenon the authors call “knowledge decay.”
The June 2026 HBR article, written by Oxford operations management professor Matthias Holweg and Babson College professor Thomas Davenport, argues that the damage goes beyond individual errors. When employees use AI to produce work that looks polished but contains mistakes or lacks substance, colleagues downstream waste time verifying, correcting, or redoing it. As those errors compound across teams and departments, the organisation’s collective knowledge base deteriorates.
The term for this low-quality AI output already has a name. BetterUp Labs and Stanford’s Social Media Lab coined “workslop” in a September 2025 HBR article to describe AI-generated content that masquerades as good work but lacks the substance to advance a task. Their survey of 1,150 US full-time workers found that 41 percent had received workslop in the preceding month, with each incident requiring an average of one hour and 56 minutes to sort out.
