HBR IdeaCast: The Hidden Causes of AI Workslop—and How to Fix Them

Podcast: HBR IdeaCast
Date: March 10, 2026
Host: Alison Beard & Adi Ignatius
Guests: Jeff Hancock (Professor of Communication, Stanford), Kate Niederhofer (Chief Scientist, BetterUp)

Episode Overview

This episode examines the phenomenon of "AI Workslop": low-effort, AI-generated work that appears to fulfill workplace tasks but lacks substance, often creating more problems than it solves. Alison Beard and Adi Ignatius speak with Jeff Hancock and Kate Niederhofer, who have published influential articles on AI Workslop in HBR. Together, they explore causes, organizational impacts, and practical solutions for leveraging AI productively—without harming trust, collaboration, or productivity.

Key Discussion Points & Insights

1. Defining "Workslop"

• Workslop is work that looks like it meets the requirements but doesn't actually advance a task.
  • “It looks like it does the work but actually doesn't advance the task… masquerade captures that, looks good but actually isn't what it says it is.” – Jeff Hancock [03:02]
• The defining feature is its interpersonal impact—it shifts the burden to the receiver.
  • “The most important part is that it is interpersonal and shifts the burden of the work onto the receiver.” – Kate Niederhofer [03:20]

2. Why Has AI Made Workslop More Prevalent?

• AI decouples effort from quality—facilitating greater volume of low-quality work that passes superficial checks.
  • “With AI, it has this special way of decoupling effort and quality… the signals are almost deceptive.” – Kate Niederhofer [04:06]
• Pervasiveness: Surveys show 53% have admitted to sending “workslop”—likely an undercount. [04:33]
• Not simply a “laziness issue”—underlying structural and organizational pressures drive workslop.

3. Structural Causes: Mandates & Overload

• AI Mandates: General, poorly defined mandates (“use AI, we spent money on this”) pressure workers to adopt tools uncritically.
• Expectations: Organizations expect higher productivity due to AI, increasing workload and stress.
  • “If you overburden people and you tell them they have to use AI, the likelihood that they produce this workslop goes way up.” – Jeff Hancock [06:37]
• Organizational Symptom: Workslop signals organizational issues, not individual failings.

4. Costs of AI Workslop

Cognitive, Emotional, & Interpersonal

• Extra time spent deciphering, correcting, or redoing poor AI output.
• Emotional toll: Recipients feel annoyed, frustrated, angry, and judge workslop senders as less competent and trustworthy.
  • “It’s like, look, I’m just trying to get my work done here and this is costing me more time and it’s clearly not authored by you…” – Kate Niederhofer [07:35]
• Trust, collaboration, engagement, and well-being suffer.
• Quantitative cost: Estimated $9 million per year for a company with 10,000 employees addressing workslop. [09:24]
• Managers spend even more time mitigating these problems.

5. Leadership Challenge: Moving Beyond Individual Responsibility

• The solution isn’t about AI literacy alone but fostering an agentic (“pilot”) mindset: responsibility, ownership, editing, and discernment.
• A shift is needed from “tool talk” to organizational and cultural change:
  • Redesigning teams' work in the context of AI.
  • Communicating a future-focused, positive vision to counteract employee fears about automation and layoffs.

6. Diagnosing The Problem

• Diagnostic step: Determine if AI use is being mandated—this strongly predicts workslop prevalence.
  • Measure: Employee engagement, optimism, agency, and AI mindset to assess organizational health. [12:10]

7. Solutions & Culture Change

Organizational Level

• Move away from general mandates—develop team-level AI strategies.
  • “How did Kate and I and our team rethink how we do research now that we have this tool?” – Jeff Hancock [13:23]
• Trust & Agency: Leaders must foster trust, provide psychological safety, and involve teams in workflow redesign.
• Transparency: Address concerns about automation and layoffs directly; hiding individual use of AI stifles innovation.
  • “If leaders are talking about automation all the time... employees are detecting the signal and will start looking for exits.” – Jeff Hancock [14:53]
• Consider creating roles like “AI collaboration architect” to bridge tech and human needs. [16:00]

Team Level

• Compassion & Support: Instead of judgment, managers should approach team members with understanding and help lighten overload.
  • “Catch yourself rolling your eyes and instead offer some compassion and try to understand… what can I help take off your plate?” – Kate Niederhofer [24:55]
• Psychological Safety: Encourage constructive feedback and mutual critique.
  • “Teams that are willing to critique each other in a really positive and critical way… just didn’t produce as much workslop.” – Jeff Hancock [25:35]
• Feedback & Coaching: Teach employees to evaluate and improve AI outputs, not just accept them at face value.
• Recognize AI’s limits: AI is an excellent aggregator but only human brains generate new ideas and context.
  • “AI is a fabulous averaging machine… But your brain and your input really matters.” – Alison Beard [27:56]

Noteworthy Example

• LEGO: Highlighted as a company investing in both hiring people and providing AI tools, giving teams space for experimentation and innovation. [21:46]
• Organizations excelling at integrating AI avoid blanket productivity metrics and instead choose focused workflow challenges with targeted measurement. [23:55]

Notable Quotes & Memorable Moments

• "It looks like it does the work but actually doesn’t advance the task… masquerade captures that, looks good but actually isn’t what it says it is." – Jeff Hancock [03:02]
• “The most important part about the definition is that it is interpersonal and it shifts the burden of the work onto the receiver.” – Kate Niederhofer [03:20]
• "With AI, it has this special way of decoupling effort and quality... the signals are almost deceptive." – Kate Niederhofer [04:06]
• “Workslop is more a symptom that there’s a problem in the organization. And so if that’s true, then it’s a leadership problem.” – Jeff Hancock [06:37]
• “All of that is a cost... the more toxic cost is really one that's emotional and interpersonal.” – Kate Niederhofer [09:16]
• “On average, people said it took them about two hours to deal with instance of workslop...for a company of 10,000 employees, that's $9 million a year.” – Jeff Hancock [09:24]
• “Move away from general AI mandates and instead think about how AI can function within their firm… It keeps my agency involved.” – Jeff Hancock [13:23]
• “We need to move away from a tool-focused… conversation and into what type of organizational changes do we need to make.” – Kate Niederhofer [10:53]
• “AI is a fabulous averaging machine… But your brain and your input really matters.” – Alison Beard [27:56]

Timestamps for Important Segments

• Definition of Workslop: [03:02]–[03:55]
• Why AI amplifies the issue: [04:06]–[05:39]
• Root causes and organizational pressures: [06:37]–[07:21]
• Workslop’s business costs and impacts: [07:35]–[10:23]
• Team dynamics, trust, and agency: [13:23]–[17:27]
• Real-world examples (LEGO): [21:46]–[22:30]
• Advice for managers and feedback culture: [24:44]–[27:56]
• Final thoughts and wrap-up: [28:33]–[29:02]

Key Takeaways

• AI workslop is a widespread, structural challenge stemming from vague mandates and added pressures to “do more with AI.”
• The emotional and interpersonal costs are significant: trust, collaboration, and engagement suffer—undermining the very productivity AI is meant to boost.
• Solutions require organizational, not just individual, change: clear purpose, psychological safety, team-wide workflow redesign, and new roles that bridge tech and people.
• Line managers should coach and show compassion, focusing on team trust and open discussion of AI use.
• Human ingenuity, discernment, and collaboration remain critical even as AI becomes more capable.

Further Reading

Find Jeff Hancock and Kate Niederhofer’s articles on AI Workslop at HBR.org.