As generative AI tools spread across offices, many companies report little clear return on investment, and a growing share of staff say the tools are creating more work, not less. New findings from BetterUp Labs and Stanford point to a root cause: “workslop.” The research warns that low-quality AI output is slipping into workflows and shifting the hard thinking to coworkers, with real costs for productivity and trust.
How “Workslop” Drains Time and Trust
Researchers describe workslop as content that looks polished but is thin on substance. It lands in inboxes, decks, and documents and then must be fixed by someone else.
“Content that appears polished but lacks real substance, offloading cognitive labor onto coworkers.”
According to the study, 41% of workers have run into AI-generated workslop. Each instance takes nearly two hours of rework to repair. That time loss is only part of the impact. The report links workslop to lower trust and weaker collaboration, as teams spend more time correcting than creating.
“Creating downstream productivity, trust, and collaboration issues.”
The findings land at a moment when boards are asking for fast AI adoption. Many teams rushed to plug in tools without clear rules for use. The research suggests that speed, without standards, is feeding the problem.
Mandates Without Guardrails
The study points to leadership behavior as a key factor. Broad mandates to “use AI” can push employees to rely on the tools for tasks that still need human judgment.
“Leaders need to consider how they may be encouraging indiscriminate organizational mandates and offering too little guidance on quality standards.”
Experts note that AI drafts are helpful for structure and brainstorming, but they require careful review. When teams skip that step, work moves faster through the system but improves little. Over time, coworkers learn to distrust AI-tagged work, which slows adoption and sours teamwork.
What Good Looks Like
The research outlines practical steps to raise quality and restore confidence. The emphasis is on purpose, not blanket usage.
- Model purposeful AI use: Leaders should show where AI adds value and where it does not.
- Set clear norms: Define quality checks, disclosure, and review steps for AI-assisted work.
- Adopt a “pilot mindset”: Pair high agency with optimism and learn from small tests.
“Promoting AI as a collaborative tool, not a shortcut.”
Clear norms could include citing when AI is used, requiring human edits on key tasks, and setting standards for evidence and tone. Pilot teams can track outcomes and share lessons, helping others avoid repeat mistakes.
Voices From the Front Line
Many employees welcome AI for drafting emails, summarizing notes, or building outlines. They say it saves time on low-value work. But peers report friction when drafts look finished yet lack insight.
“Workslop” spreads when output looks done, but thinking has not been done.
Managers face a tough balance. They must meet productivity goals and protect quality. The study suggests that quality signals—citations, clarity, and relevance—should weigh more than speed. Teams that reward thoughtful use over volume are less likely to ship fixes downstream.
Measuring the Real Return
The next phase is measurement. Time saved is not enough if the organization spends those minutes on rework. Leaders can track:
- Rework hours tied to AI-assisted tasks
- Quality scores from peer review
- Trust and collaboration metrics on teams using AI
If these indicators improve, ROI will follow. If they do not, mandates may need to pause until standards catch up.
The headline from the research is clear: AI can help, but careless use hurts. Companies that model purposeful practices, set clear norms, and run disciplined pilots can avoid workslop and rebuild trust. The coming months will test which firms treat AI as a teammate—and which keep passing the work along.
