# AI Work Slop: Strategic Costs and Leadership Fixes

**Podcast:** HBR IdeaCast
**Published:** 2026-03-10

## Transcript

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I'm Alison Beard.
And I'm Audie Ignatius, and this is the HBR Idea Cast.
Artificial intelligence promised to make us faster, smarter, and more productive at work.
So why does it sometimes feel like it's doing the opposite?
If you're trying to figure out how to use AI without undermining your culture, collaboration, or credibility, this episode is for you.
Today we'll explore why AI stop, stop, stop, stop.
Allison, did you even write this?
Good catch, Audi.
No, our producer Mary and I decided to give it to Chat GPT.
And that's not because we were busy with other stuff that we were, but it was to try to prove the point of today's episode.
That the rise of AI has also meant the rise of what our guests call AI work slop.
And that is bad product that kind of passes, but actually creates a lot more problems than it solves.
So I know who you're talking about and your guests have written a couple of articles for us on AI Work Slop.
They've been among the most popular things we've published in the past year.
Work Slop is a problem.
AI is achieving a lot for us, but it is also creating content that is problematic.
Exactly.
And this issue is resonating.
The coinage of the term work slop has really taken off.
But one of the key points that we'll dive into is the idea that this isn't just laziness.
There are structural pressures causing workers to use AI to create junk.
Two co-authors of those articles you mentioned are Jeff Hancock, a professor of communication at Stanford, and Kate Niederhofer, chief scientist at BetterUp.
And they're going to explain how this trend hurts teams and organizations and outline the changes that leaders have to make to ensure our AI use is working for us, not against us.
Here's our conversation.
That was really you, right?
Yes, Audie.
So let's start with the basics.
How do you two define work slop?
I really like the definition we started with, which is it looks like it does the work, but actually doesn't advance the task.
And actually, even more, I like the word masquerade that we use to define it.
So it's sort of like masquerade captures that looks good, but actually isn't what it says it is.
I think the most important part about the definition is that it is interpersonal and it shifts the burden of the work onto the receiver.
But it's really important to know that we did not define it like that in talking about it to others to measure the prevalence, for example.
So the classic definition of it is low effort, low quality, AI generated work that appears to fulfill a workplace task, but doesn't really have the substance necessary to do that.
But when we talk about work slop, it's really important to think about that interpersonal shift of burden.
You know, obviously there have always been people who phoned in their work, but how has the rise of Gen AI made that problem worse or more prevalent?
I think people ask us this question all the time and are failing to understand that with low quality work prior to AI, there was really no question about it.
With AI, it has this special way of decoupling effort and quality.
And so the signals are almost deceptive now when you receive it.
It's low effort, low quality, and it's trying to pass for something other than what it is.
The other thing that AI allows you to do is just do more of it.
I think one of the things that we were shocked by is how pervasive it is.
We asked people not only like how often have you received it, a fair number of people said that about 40%, but we also asked if they had sent any work slop.
And this is a pretty negative question.
And so social psychology tells us social desirability bias should really like lower that.
53% of our participants said that they had sent some work slop or that some of the AI work they did was sloppy.
So if anything, that's an undercount.
And it really gives you a sense of like how pervasive people are worried that even they are doing it themselves.
Yeah.
So people are admitting to the bad behavior.
Right.
I think the premise, certainly, that AI companies are putting forth is that, you know, you can use these tools to effectively automate a lot of your routine work.
That's the promise.
And they can free you up for more complex tasks or deeper thinking.
So why isn't that happening now?
Is it, you know, because people are lazy or they don't know how to use the tools properly?
I think we tend to think it's not that they don't know how to use them.
The the responsibility is not so much on the individuals, but they're put in situations where they're already feeling tired or disengaged or don't really have the fuel to operate at work in the most powerful way right now.
And they have these mandates to use these powerful new tools on top of everything that's on their plate, or in order to do everything on their plate.
So it's not so much that they haven't had the time to learn the training, but it's a new class of tools that requires like an agentic mindset.
We would call it a pilot mindset to really figure out how to have ownership in the work that you create, how to edit it, to discern what voice you want it to have, and really work with the tools to make sure that the work is meaningfully advancing the task at hand.
One of the things that became clear is that it's easy to blame the person.
Say, oh, look at this is just a person being lazy.
And that's not really what this is.
It's, I think work slop, and we we argue this in our paper, is more of a symptom that there's a problem in the organization.
And so if that's true, then it's a leadership problem.
When we look at all the factors that lead to work slop, it's really a recipe with two main ingredients.
One is uh AI mandates that are really general.
Hey, you need to use AI.
We've just spent all this money, you better use it.
And the second is because we have given you all this AI, you should be able to do more work.
So if you overburden people and you tell them they have to use AI, the likelihood that they produce this work slop goes way up.
What have you learned about the costs of this prevalence of AI-generated work slop?
I think first, let's talk about what hits are you seeing to productivity, decision making, performance.
The costs are multiple.
So I think the first cost is just that cognitive effort that's required to understand what's going on.
And the time that people are spending is not just figuring out what's going on here, if it's missing context or what it does contain that feels a little inaccurate or unlike the sender who produced the content, but also what to do about it, whether it's egregious enough to say something to the person who produced it, or even to initiate some sort of gossiping behavior, you know, to talk about how absurd it is that somebody is missing the important context that you know exists and needs to be in a document, or that they would use, you know, a style that's so unlike them.
So the first is just that cognitive effort.
But what we found in the research, and what really was remarkable to us was how emotional it is, how annoyed, frustrated, even angry people are when they receive it.
So there's sort of that first wave of emotional experience that you have that'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, and it's not advancing this task.
And then it creates this interpersonal phenomenon where it actually makes you judge the producer of the content to be less competent, less trustworthy, like you don't want to work with them anymore.
So all of that is a cost.
I think, as Jeff mentioned before, people really focus on the productivity, you know, the time that it's spending and draining from the workforce.
But I think the more toxic cost is really one that's emotional and interpersonal.
Yeah.
So that knock-on effect hurts collaboration, trust, engagement, and well-being.
Totally.
Like we start seeing that people are like if Kate had sent me something, I literally judge her as less creative, less capable, and less trustworthy.
So it just kind of gets right at the like core, the foundation of teamwork.
It has these huge interpersonal costs.
I think Kate's right that they're probably the biggest cost.
They're a little bit invisible for the more visible costs.
On average, people said it took them about two hours to like deal with an instance of work slop.
So you kind of got to detect it and then you gotta be like, oh crap, what am I going to do?
Do I ask Kate to redo this?
Do I just do it myself?
And so it's two hours of work.
We did some back of the envelope calculations where we asked people what their salary was and how much time they estimated they had to deal with it.
And for a a company that's about 10,000 employees, that's nine million dollars a year.
So it's non-trivial even the sort of hard productivity numbers.
Which is ironic because companies are paying for these tools in order to save money.
That's right.
One thing that I just wanted to add to that too is we found that managers are actually reporting spending more time and effort dealing with it.
So there's something about this being a more effortful process for more senior resources, which I think is ironic.
I guess the question is you're asking people to find the right balance between using AI in the evolution of our research is that this is a leadershiput and their own critical thinking.
And it's hard to figure out right now where that sweet spot is not so much on us exclusively as individuals, but it's a leadership challenge to set up the talent infrastructure to ensure that we have the right conditions in talking about how to introduce these powerful tools and how we want to achieve any sort of productivity or innovation gains.
We do need to start figuring out the balance between, you know, our voice and the expertise of these tools, but I think we have to move away from a tool focused or even tech focused conversation and into what type of organizational changes do we need to make everything from the communication of this opportunity and privilege to introduce these new tools to creating the culture that is really connected and trusting and ripe for people to lean in and engage in their work.
Okay, I want to dig into all of that because solutions are why we're here.
I guess the first question is how do you diagnose how big a problem this might be in your organization before you even start trying to tackle it?
Well, there's one really diagnostic thing, and that's just to understand is AI mandated in the organization.
That's like the single biggest predictor of work slop.
And so it's pretty telling if people feel as though their organizational strategy is one of mandates.
And then from there, you know, you can go on to measure the prevalence.
We've had quite a good success rate in picking up on a reliable prevalence rate across different organizations, situations, geographies, even.
So I think that's the first thing is like visibility into the problem and thinking about the root cause.
I would also encourage leaders to think about their culture and to measure things like engagement, or Jeff and I have done a lot of research on optimism and agency.
And that's something that you can also measure as well.
You can measure simply how agentic and optimistic people are about their work, but you can also measure their mindset toward AI.
And so each of those are important things to think about as diagnostics today.
So what are some steps that leaders might take to creating a more positive productive culture around AI use?
I'll talk about two, I think that leaders could adopt right away.
Number one, as Kate said, move away from general AI mandates and instead think about how AI can function within their firm.
And I think the team level is a really important way to think about it.
So one thing I think that I'm seeing, and I saw this as a theme at the World Economic Forum in Davos this year, was how to get teams to rethink and redesign their work together in the context of AI.
So instead of like, okay, I'm gonna use AI differently or learn and become more literate, it would be well, how did Kate and I and our team rethink how we do research now that we have this tool?
And that would change the way the team works.
It also surfaces how AI can play a role, but it keeps my agency involved.
I'm the one that's working on redesigning our teamwork together.
And so I think that's a really powerful approach.
The other one is trust.
I think there's a lot of people who aren't dumb and they're thinking, wow, everyone in this company signal AI, that's great.
Am I going to get laid off because of this?
I think that the way that leaders talk about AI and the vision that they have inside their organization is heard loud and clear by employees.
Everybody knows AI is this big powerful thing.
It's really ambiguous.
Nobody really knows what's coming.
And so if leaders are talking about automation all the time, uh, if they're using these general mandates, I think employees are detecting the signal and will start looking for exits.
And maybe that's part of why they start to generate work slap because they feel like they're gonna be outsourced soon anyway.
Right.
Exactly right.
We've recently been researching that exact thing.
And we find that employees are are really perceptive.
They're really picking up on subtle cues and making powerful judgments about organizational AI strategy and its implications for the extent to which an organization is resilient and can modernize and can really approach the future in the right way.
I just wanted to add like a couple things to what Jeff was saying.
Cause I think we did really hear a different conversation at Davos this year, which was about reimagining the future of work.
And you can see, like in our second article, we have some suggestions about like ways that people can reimagine and think about a new model for leadership as well as management.
There's a really interesting distinction that I actually heard on your podcast long ago about the difference.
About the difference between leadership and management.
And that, you know, in times of stability, it's really about management and what type of systems are going to help scale.
But in times of volatility and uncertainty, it's really a leadership challenge.
And so I think that's what we're seeing now is there is leadership challenge as we've discussed, and it's an opportunity to really reimagine what leadership looks like in the organization.
And that includes the design of the org as well as new roles.
So Jeff and I had suggested, you know, a new role as something like an AI collaboration architect.
And this is somebody who's fluent in what we'll lightly refer to as the human and the tech, right?
Who understands like what are some of our collaboration issues here?
What are some of the challenges that we're really struggling with that need to be solved, whether with AI or other tools?
And then how can I really think about embedding AI into this workflow to solve the problem?
Instead of just like throwing these tools in, it's thinking like an architect and how you can embed this very powerful technology to solve some of the real challenges that you have.
And that's when you'll see the real productivity and innovation gains.
And it sounds like, Jeff, you were saying that it's really important for C-suite leaders for that new AI collaboration executive to listen to the teams on the ground to figure out the AI processes that will work best for them.
Yeah, that's right.
I mean, if you think about, you know, how Toyota redesigned the manufacturing of cars, they did it by having the employees engaged in the redesign of the way the factory would work, and led to massive success because the employees were engaged and they had agency.
And I think the same thing happens here.
Trust in your teams if you think that they can rethink the way they work and make themselves more efficient or even better, create new capabilities, that would be amazing.
But there's a risk for leaders with that.
Our economics colleagues have talked about the J curve of these kinds of new technologies, where initially there's um a decline in productivity.
I think we're in that sort of dip of the J curve.
We have to invest in our people and give them time and space to rethink how they're going to be able to work with these tools.
And that's an investment.
And it won't be until those teams are able to rethink things and redesign that you'll see those massive payoffs.
And I think the massive payoffs come from augmenting teams, allowing them to do new things, which is risky.
We don't know what those new things are, versus automating everything that they did.
It strikes me that right now we're in this phase where everyone is experimenting on their own with AI tools provided by the company or not, with vastly varying degrees of success.
And so it really does need to be this information coming up from the ground, but then a top-down approach to figuring out how we're going to make humans and the technology work best together.
That's right.
It's the weirdest thing.
And we see this like actual hiding of individual use.
And that just crushes innovation.
And part of it is the reaction to our work slot article.
Like people are like, oh, look, see, when people use AI, it's bad.
A lot of firms' initial reaction to AI was a risk approach, which is totally fair, but led everybody to be worried about using AI.
And then look, if you're a young person in your 20s, you've already seen that your job outcomes and employment are impacted.
They're the first generation to see a decline in employability.
So we have all these reasons for why people are staying at the individual level, using it privately.
And the firms that figure out how to surface it and make it part of like a team's work, those are the ones that are going to thrive.
One thing that we thought was really interesting when we were modeling predictors of work slop is that the self-reported reason for creating work slop is because everything feels urgent and important.
People feel overburdened.
I don't think people are consciously thinking about it as you know, I am doing this in a conscious way to have a nefarious impact on others.
Instead, it's more like I have no time and I have a million people to manage and a million different tasks to complete.
Everything feels urgent and important.
And here's this like really frictionless way for me to just get by.
So I think there is like something to acknowledge at the individual level that has to do with like how easy this shortcut behavior is right now and how blameless the individuals are at the same time.
So what organizations are figuring out a way to do this well and prevent work slop?
So we know there's a couple from colleagues that are investing in people and in AI and seem to be getting their employees really excited about the possibility.
One is is Lego.
You wouldn't think of that necessarily, like doesn't seem like a super high tech, and that's probably where you're gonna see a lot of advances, is not in the super high tech who are already kind of adopting at high rates.
But Lego is investing in hiring people and in providing them with AI tools and giving them space to create and innovate.
That comes at a with a cost, right?
So this is a privately owned firm that has a fair amount of uh resources.
So we're seeing those kinds of companies are able to do that kind of investment required for the J curve.
Yeah, I mean, I'm not at liberty to speak about any of our customers.
I think that um there are companies that are doing incredible things, and I'll tell you sort of the themes that I see horizontally across them.
And one is that they're building the right mindsets.
So initially there were a lot of training tools available to help people develop AI literacy.
And I think what makes somebody stand out is providing the training tools bundled with mindset training.
So we have done a bunch of work on this idea of the pilot mindset.
It's people who have high agency and high optimism about AI.
It's their mindset toward using the tools.
And so we have customers who are providing that type of training so that people can approach these tools with the right mindset to be curious and experimental and confident in their usage as opposed to just literate per se.
So that seems to be making a really big difference.
The other is we have a lot of organizations that are investing in the talent infrastructure of the organization by providing, for example, coaching to address things like low agency, low optimism, low motivation, a low sense of mattering and providing opportunities to get the workforce to feel like they can not only be engaged in their work, but they can compensate in some ways for the opportunity cost, which is collaborating with others, right?
Like using tools instead of interacting with other people or aligning and managing others.
So we see big investments in the people within the organization.
And then I think the last is like, I see some organizations identifying less than 10, for example, seven priority areas where they can really embed AI into the workflows to solve particular challenges.
And then they measure those particular outcomes that they're trying to solve for.
And they're not just looking at blanket productivity, but instead thinking about is this technology solving the problem that we have and leading to productivity in this specific place?
So it's sort of like precision development and then also targeted measurement.
So that's the very high level.
Let's bring it down one level.
If you're a manager on a team that is struggling with some sharing of work slap, what advice do you have for that person?
Compassion.
I think that's what we've learned first is to catch yourself rolling your eyes and instead offer some compassion and try to understand like why is it that you're feeling like everything is urgent and important right now and you need a shortcut?
You know, what can I help take off your plate?
And how can I see you and invest in you as a person, right?
So thinking back to classic management, like what is my job?
It is to see and hear and grow and develop my team.
That's the first thing to think about is like, how can I approach this in the most human way possible?
Yeah.
Well, I think it's related to that, which is what Amy Edmondson calls psych safety.
We call trust.
So teams that are willing to critique each other and to do that in a really positive and and critical way that's constructive.
I mean, it's like anytime you've been on a team that's really great, it's not all, hey, let's just be nice and fluffy with each other.
You know, I respect you enough that I will critique your work and I'll pay attention to it and help us do something great.
And so psych safety or or team trust was one of the biggest predictors of reducing work slop.
So if somebody was on a team where they felt like really good that they were going to be critiqued in constructive and positive ways, they just didn't produce as much work slop.
One of the issues that we're struggling with right now, and the reason why we have such cognitive effort expenditure when we receive work slop is we are not in practice of providing constructive feedback to our colleagues.
So that's something that we need to do regardless of whether they're creating work slop.
It's something that just it leads to growth and development within the workplace.
Yeah, it seems like an important part of the feedback would be coaching employees and colleagues on how to better evaluate what they're producing with AI and maybe giving some tips or sharing knowledge on how to get better results from the Gen AI tools that you have at your disposal.
We often think of generative AI.
But we're moving, you know, away from where it's just generating content like work slop.
Work slop is essentially a generative thing to where AI is being used much more for analysis, for decision making, where the output isn't just a bunch of text in an email or a slide deck.
And so I think the only way to make sure that we're going to be using these tools in these new ways in a high quality way is for teams that trust one another to use these tools and check one another, if that makes sense.
It's almost like it's going to become more embedded and less about like the thing I wrote and more about, well, here's this giant analysis that I did, and here's how I did it using these tools.
You know, will you check this for me or how does that sound to you?
And so I think that's going to be really important why teams and collaboration is going to matter more than ever.
Yeah.
And remembering that all of our brains are still a valuable input too.
You know, we're the ones who are coming up with new ideas, not just sort of gathering and regurgitating what's already existed.
So I think emphasizing to everyone that AI is a fabulous averaging machine.
You know, it's a fabulous aggregator of existing information, but your brain and your input really matters.
And so we need you to be applying your brain to everything that you're working on.
We will still need to use human judgment and discernment, and we will still need to figure out the best ways in which to collaborate with others, even when AI becomes masterful at context, for example, and is embedded in everything.
No matter what, we still have to be really good decision makers and problem solvers.
The tools are not going to do everything for us, no matter how powerful they become.
Well, I think that's a perfect way to wrap up.
Thank you so much for helping all of us figure out how to not generate work slop and do a better job making ourselves more productive and engaged with these new tools.
Thanks, Alison.
Thanks so much for having us.
That's Kate Niederhofer, Chief Scientist at BetterUp and Stanford Professor Jeff Hancock speaking about AI WorkSlop.
You can find their articles on the topic by heading to HBR.org.
Next week, Audi speaks with Yale Professor Jeffrey Sonnenfeld about the changing dynamic between government and business.
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Thanks to our team, senior producer Mary Duke, audio Product manager Ian Fox, and senior production specialist Rob Eckhart.
And thanks to you for listening to the HBR Idea Cast.
We'll be back with a new episode on Tuesday.
I'm Alison Beard.