Workslop and the Erosion of Critical Thinking: Why ECI is the Executive Antidote
by Carsten Krause, CDO TIMES, October 1st, 2025
It has probably happened to you. You received a confusing meeting summary with your name attached to an action item from a meeting you participated in.
The only problem is – this action item was never discussed and you were not aware of it being assigned to you.
How could this happen? You received the invite from a respected colleague you usually trust.
Clearly he got this wrong.
So what happened?
Your colleague used an AI tool to transcribe the meeting and to get an automated meeting summary. Unbeknownst to your colleague the AI hallucinated to the next best option for something it did not understand correctly and wrongly assigned a made up action item to your name. The sender of the summary did not check the accuracy of the document since he trusts the AI tool “blindly” without checking its output.
this is happening all over the place right now as pointed out in a recent article of Harvard Business school describing the AI generated “workslop” problem.
In an ongoing survey in conjunction with Standford University 40% of the 1150 US based employees report of having received bad AI generated information they termed “workslop”.
So what is workslop?
Workslop describes the phenomenon of relying too heavily on AI generated content that sounds accurate, but has been modified or plainly made up by generative AI based solutions that try to best guess the next best answer or try to please the request or with an answer even if the source document does not support it.
Honestly, I have been a victim of workslop and also got fooled by the output of generative AI based tools since it sounds quite factual and well researched. When you review the details often facts get misconstrued and in some cases replaced by non-factual information that sounds very plausible.
However, I noticed and heard from peers at executive leadership conferences that generated content we receive even from trusted solutions like Microsoft Teams, Zoom, summarizing singular documents are all still in danger of including inaccurate and sometimes fabricated information. This study further confirms that.
According to the Harvard research this workslop or “AI Slop” most often gets passed on from peer to peer (40%), less often from employees to mangers (18%) and least often from managers to employees (16%).
The problem with “AI Slop” and “AI Sloppers” passing on that information unchecked (I should trademark that term) is manifold.
Rework: often receiving work slop creates stress with the impacted individuals and teams. This includes spending time deciphering what the author’s intention was, what the real driver and actions to take are. Also, there is the conflict of just working through this offline or reaching out to the sender to make him aware of the situation and asking for guidance.
Degradation of Trust: in the same Harvard study respondents highlighted emotions like frustration, anger and the loss of trust in the sender of AI slop. I see this phenomenon on LinkedIn of social influencers who are posting confrontational headlines and topics with clear factual mistakes in their posts. I know that these leaders are well qualified, but they lack checking their posts for accuracy.
Critical thinking: as a larger topic for educators, HR departments and learning managers the blind trust in AI output creates a problem for society. How do we use these tools correctly and leverage the massive data crunching power, but retain our critical thinking.
in the Harvard Article this is described as the pilot / passenger problem.
In my book I also discuss the related centaur and cyborg approaches.
what these approaches have in common is that we don’t want to be the “passengers” of an AI solution that does the thinking for us, but need to strive towards interacting and challenging each other. A pilot uses these tools as inspiration, input for research and does not copy and paste the output without further input and fact checking
So what can leaders do to address this dilemma?
On the one hand the advent of Artificial Intelligence has introduced various opportunities to improve efficiencies, upskill workers with an expert level AI assistant and deliver improvement in quality and speed of service across various industries. I have both experienced the upsides, lessons learned and downsides at Campbells, Wendy’s and current engagements.
This led to me developing the ECI framework to help companies and leaders understand the factors improve chances for an AI pilot actually making it to production and delivering value.
Let’s do an anlysis with the ECI framework I cover in my book the “AI Ready Leader – HI + AI = ECI” in greater detail if you want to read up on it further.
Where The Workslop Creeps In: An ECI Breakdown
Mapped against the formula, the risks become visible:
| ECI Lever | Lesson from P&G + Harvard Studies | How Workslop Emerges |
|---|---|---|
| HI | Humans still provided domain-specific judgment. | When employees defer willingly, accidently or blindly to AI, errors go unchallenged. |
| AI | AI accelerated idea generation and broadened solution sets. | When AI outputs lack citations or quality control, slop spreads unchecked. |
| T (Trust/Validation) | Embedded real-world oversight, checkpoints, and leadership support. | Without review steps, flawed summaries circulate widely before correction. |
| R (Resistance) | AI improved morale, reducing “fear of trying.” | If resistance to challenge is too low (overtrust) or too high (fear of blame), errors slip through. |
Workslop isn’t just sloppy work—it’s the signal of an ECI imbalance.
Leading Indicators: Spotting Workslop Early
Executives can’t wait until the rework bill lands. They need to optimize leading indicators that flag when ECI levers are drifting off balance.
Human Intelligence (HI)
- Declining number of questions raised in meetings.
- Drop in depth of peer-review comments.
- Rising reliance on unedited AI summaries.
Artificial Intelligence (AI)
- Declining citation density in reports.
- Rising hallucination/error rates in random spot-checks.
- Fewer prompt iterations (overtrust in the first output).
Trust/Technology (T)
- Shortened lag between AI generation and distribution (no review).
- Increase in correction requests from downstream teams.
- Absence of shared “source of truth” data platforms.
Resistance (R)
- Low number of flagged errors or challenges.
- Passenger vs pilot syndrome of passing on generated content unfiltered
- High gap between “confidence in outputs” vs. “actual correctness.”
- Lack of AI explainability to trace back what data has been used for the AI based output and recommendations – very important for executive decision making
- Employee trust erosion in AI and AI sloppers
When these leading indicators drift, workslop is scaling silently.
Executive Action Plan
- Train for Critique, Not Just Prompts:
Teach staff how to spot faulty logic and hallucinations. It is ok to use AI for research and inspiration, but be aware of potential bias and hallucinations that even currently occur with top models and trusted integrated AI in office collaboration tools. - Enforce Provenance:
Require every AI summary to include citations or source provenance. This does not only mean to list the sources, but reading the sources and verifying the data. Combine it with old fashioned research by reading articles, books, library databases and search engines - Red-Team Rituals:
Assign a rotating role challenge AI-driven claims. Evaluate tools that identify internally generated AI content. And of course limit and monitor AI tools data exfiltration limiting risk exposure of internal sensitive and confidential data leaving your organization. - Culture of Safe Skepticism:
Reward those who flag errors, not just those who produce volume. This could be monitored with ECI slop reduction scores. - ECI Dashboards:
Monitor HI, AI, T, and R leading indicators actively over time and measure its effectiveness and ECI outcomes.
The CDO TIMES Bottom Line
Harvard’s warning on workslop is not about tools—it’s about thinking. When teams forward AI outputs unchallenged, they trade critical thought for cosmetic polish. That is the real productivity drain.
The ECI formula—(HI + AI) × T – R—is the executive compass. Use it to measure whether humans are thinking, whether AI is transparent, whether trust scaffolding exists, and whether resistance is healthy.
In “The AI Ready Leader” I discuss the Procter & Gamble’s field experiments for the cybernetic teammate and AI factories to prove the point: AI can elevate human performance, but only when paired with disciplined oversight and a culture of critique. Without that, workslop is inevitable.
Executives who monitor the leading indicators of ECI will catch workslop before it becomes systematic and harder to control. Those who don’t will be left with pretty memos, rework spirals, and eroded trust.
The challenge of the decade isn’t adopting AI—it’s keeping your organization thinking while you do it.
Sources:
- Harvard Business School (2025) AI Generated Workslop is Destroying Productivity. https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity
- HBR (2025).
The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise.
https://www.nber.org/papers/w33641 - BetterUp and Standford Social Media Lab (2025). Workslop is the new busywork. And it’s costing millions. https://www.betterup.com/workslop
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