AI-Generated "Workslop" Is Taxing Your Team's Productivity
Here's what's happening in your workplace right now: employees are using AI to create polished-looking documents that shift the cognitive burden to colleagues. This is called workslop—and it's the hidden productivity tax every leader needs to address.
Harvard's latest research found that 40 percent of workers received AI-generated "workslop". These aren't obvious copy-paste jobs. They're formatted reports and structured briefs that look professional but force recipients to spend nearly two hours per incident decoding, correcting, or redoing the work. That's not efficiency—that's transfer of labor.
Three Takeaways You Can Act On Today
Stop measuring usage—start calculating value.
I've seen companies brag about adoption rates while ignoring ROI. As I wrote in Building Your SME AI Literacy Program, most organizations receive little measurable return because employees use AI as a shortcut, rather than as a thinking partner. The question isn't "who's using AI?" but "is it driving business outcomes?" An AI readiness assessment for EU SMEs should focus on value creation, not tool proliferation.
Delegate writing, not thinking.
The leaders I work with who achieve results use AI to refine, not to replace, their thinking. Workslop happens when people offload cognitive effort to machines and then hand the burden back to others. Train your teams: AI enhances clarity, while humans own the analysis. Effective workflow automation design requires humans to maintain decision authority while AI handles execution.
Train pilots, not passengers.
Harvard's study shows "pilots" use AI 75 percent more effectively than "passengers." Pilots guide AI with clear intent. Passengers want the ride. Your training programs must build agency and accountability—not passive tool use. AI workshops for businesses should emphasize this distinction, turning team members into active participants rather than passive consumers.
From My Experience
In the past, a marketing team would produce slick campaign briefs that required three extra meetings to fix. The solution wasn't a new tool—it was teaching the team to use AI for research and drafts while humans made the judgment calls. The quality and speed improved overnight. This operational AI implementation approach—combining human judgment with AI capability—transformed their output quality and team morale.
Limits and Fixes
AI can't read context. If you don't set standards, you'll get garbage with a polished appearance. The fix: treat AI-assisted work with the same rigor as human-only output. Set clear guidelines for when to use AI, how to review, and what "finished" looks like. Business process optimization requires establishing these governance frameworks before scaling AI adoption across teams.
Advice: Audit your last five AI-generated docs. If they confuse more than they clarify, you're spreading workslop. Reset expectations now, because authenticity and clarity are your team's real productivity edge.
Let's do this—together.
Written by Dr. Hernani Costa and originally published at First AI Movers. Subscribe to the First AI Movers Newsletter for daily, no‑fluff AI business insights and practical automation playbooks for EU SME leaders. First AI Movers is part of Core Ventures.
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