Harvard Business Review: AI ‘workslop’ is rotting companies from within

Companies that aggressively embraced generative AI are now confronting a paradox: instead of improving efficiency, their work quality is declining. Two recent articles in Harvard Business Review highlight a dangerous feedback loop where AI-generated low-quality output corrupts the information organizations depend on for decision-making, a condition the authors term “knowledge decay.”

In the June 2026 piece, Oxford professor Matthias Holweg and Babson College professor Thomas Davenport argue that the damage extends far beyond isolated mistakes. When employees use AI to produce work that appears polished but contains errors or lacks depth, colleagues downstream must spend time verifying, correcting, or redoing it. As these errors compound across teams and departments, the organization’s collective knowledge base erodes.

The term for this subpar AI output already exists. In a September 2025 HBR article, BetterUp Labs and Stanford’s Social Media Lab coined “workslop” to describe AI-generated content that looks legitimate but fails to advance a task. Their survey of 1,150 U. S. full-time workers found that 41 percent had received workslop in the previous month, with each incident requiring an average of nearly two hours to resolve.

The financial toll is substantial. Based on respondents’ self-reported salaries and time estimates, researchers calculated that workslop costs roughly $186 per worker per month. For a company with 10,000 employees, that translates to more than $9 million annually in lost productivity, a figure that doesn’t include downstream effects on morale and trust.

Those social costs may be even more damaging. In the same survey, 53 percent of workers who received workslop felt annoyed, 42 percent viewed the sender as less trustworthy, and about half considered the colleague less creative, capable, or reliable. A third said they were less likely to work with that person again.

The broader productivity picture is equally grim. A July 2025 MIT Media Lab report found that 95 percent of organizations saw no measurable return on their generative AI investments, despite billions spent. Goldman Sachs reached a similar conclusion in March 2026, finding no meaningful link between AI adoption and economy-wide productivity gains, even though 70 percent of S&P 500 management teams discussed AI on earnings calls.

Knowledge decay differs from the familiar issue of AI hallucinations. Hallucinations are factual errors in output. Knowledge decay describes what happens to an organization when those errors, combined with a broader pattern of low-effort AI-generated work, accumulate over months.

Workers begin to distrust internal documents. Processes built on unreliable information produce unreliable results. Institutional memory thins as employees rely on AI instead of developing their own expertise.

Holweg and Davenport warn that hiring has been especially damaged. AI-generated resumes flood recruiters, AI-generated job listings mislead candidates, and AI-powered screening tools filter out qualified applicants. The result, as HBR notes, is that trust in hiring has sunk to “all-time lows for both job seekers and recruiters.”

Worker backlash is already measurable. A 2026 survey of 2,400 workers across the U. S., UK, and Europe found that 29 percent admit to sabotaging their employer’s AI strategy by ignoring guidelines, refusing training, or deliberately skewing performance data. Among Gen Z workers, that figure rises to 44 percent, driven largely by fear of job displacement.

This resistance exists alongside a pattern of AI-justified layoffs that often lack evidence that AI systems actually replaced the eliminated roles. The tech sector recorded more than 95,000 job cuts across 247 events in 2026, with nearly half attributed to AI, even though analysts question whether many companies had mature AI implementations capable of absorbing the work.

The irony is that fixing the workslop problem requires exactly the kind of labor AI was supposed to reduce. Business leaders must now invest in verification processes, quality standards, and human oversight to ensure AI-generated content meets the bar, work that consumes employees’ time. HBR’s prescription amounts to building a new layer of human checking around AI output, which undermines the efficiency argument that justified adoption in the first place.

Both HBR articles distinguish between indiscriminate AI mandates and targeted use. The June article notes that proprietary models trained on company-specific data can add genuine value, while public LLMs applied to unsuitable tasks produce “generic prose that often contains mistakes.” Companies that froze hiring citing AI productivity gains are now discovering those gains may be illusory if work quality degrades faster than headcount shrinks.

The knowledge decay concept reframes the AI productivity debate. The question is no longer just whether AI makes individual tasks faster, but whether the cumulative effect of widespread AI use improves or worsens an organization’s decision-making. HBR’s answer, for companies that adopted AI without quality controls, is that it makes it worse.

Holweg and Davenport’s credentials lend the argument weight, but the knowledge decay framework has not yet been tested through controlled empirical studies. The concept synthesizes existing evidence rather than presenting new data, and the BetterUp-Stanford workslop survey relies on self-reported estimates of time lost. How accurately workers gauge time spent on rework remains an open question.

Still, the pattern is consistent across multiple sources. Goldman Sachs, MIT, BCG, and now two separate HBR articles from different research teams arrive at variations of the same conclusion: most companies are not getting what they expected from generative AI, and the ones that pushed hardest may be paying the highest hidden cost.