Discover the intriguing paradox of AI at work—it’s meant to lighten our workload but might just be doing the opposite. In this eye-opening discussion from Digital Labor Lab, hosts Jennifer and Brad Owens dive into how AI tools, designed to tackle simple tasks, are leaving human employees with only the most complex and stressful responsibilities. Across industries like healthcare, HR, and customer service, automation takes care of the “low-hanging fruit,” transforming traditional roles and creating new challenges for workers.
As AI and automation evolve, they offer the promise of unprecedented productivity. However, Jennifer and Brad reveal a hidden consequence: increased cognitive loads for employees handling complicated tasks. Explore how this shift impacts mental health and job satisfaction, leading employees to face burnout, increased stress, and decreased performance.
With a focus on practical application, the discussion examines how businesses can better balance AI’s capabilities with human needs. By strategically automating not just easy tasks but also reducing complexity in challenging ones, companies can provide employees with opportunities for rest and growth. Join the conversation and discover how reshaping digital labor strategies can enhance productivity and employee well-being.
Links & Resources:
Understanding the Role of Cognitive Load in Paramedical Contexts – https://www.tandfonline.com/doi/pdf/10.1080/10903127.2024.2370491?needAccess=true
Watch the full episode:
AI Generated Full Transcript:
Jennifer Owens (00:00)
So update on that AI tool that we were working on, by the way. I found that it responds to like 85 % of the easy emails, which is terrific. Problem is now I have to deal with only the complicated ones and I don’t like it.
Brad Owens (00:11)
I never thought about it that way. We should talk about that.
Jennifer Owens (00:15)
Yeah.
Welcome to Digital Labor Lab, where we are exploring the future of work one experiment at a time. I’m your host, Jenny Owens.
Brad Owens (00:31)
And I’m Brad Owens and on today’s episode, we’re gonna tackle a question of what happens when AI takes over all the simple work and leaves just humans with the only most stressful, exhausting tasks? Not something I would have anticipated, but a really good thing for us to get into.
Jennifer Owens (00:48)
I want to talk a little bit about this pattern that we’re seeing across industries from health care to HR to customer service, where we’re using these tools to automate the low hanging fruit, right? The easy stuff. I want to think about what that does to our human employees as we’re starting to see these digital agents come in and scoop up some of the workforce. Can we talk about this?
Brad Owens (01:06)
Yeah, so AI and automation, they were meant to make jobs easier. But in reality, what we’re proposing here is that they’re actually making them harder and more exhausting. So let’s dig into the kind of what is the paradox of when you automate things, why do jobs actually get harder?
Jennifer Owens (01:24)
Yeah, so let’s think about an example here, right? So I recently spent some time on the phone with customer service. Customer service reps used to have a wide range of stuff that they were equipped to handle, right? Everything from the basics, like how do I pay my bill? Or what are your hours? All the way through the really complex, like I changed my name at 2 a.m. on daylight savings time and now my billing is all messed up.
But now we can use chatbots and we can use automated services to handle the easy stuff. And humans are dealing only with the name change in the middle of a daylight savings time type issues. So let’s talk about why does this make work harder?
Brad Owens (02:04)
Yeah. So let’s think about it in our, in our day jobs. So in HR and hiring, when we have AI that might screen applicants, we might have those applicants that are just the easy ones. Like when you have a unskilled potential role and all we’re really looking for is people who have high rates of replies and are interactive during the hiring process. Those are typically people that would make good, you know,
lower level type positions, maybe factory pickers or warehouse workers and things like that. Those are kind of easy for humans to do, but that also makes them easy for AI to do. Now we also have the flip side of that, where we have a highly competitive job, something that is really difficult to fill. Maybe it’s a high level executive or something like that. So in my day job, typically, if I were a recruiter, I’d be able to handle both of those and I would have kind of a good balanced day. But if AI has taken only the easy positions.
And now I just have to focus on those really hard positions. I’m going to be spent by the end of the day.
Jennifer Owens (03:05)
Yeah, so in my day job, I work in health care. I work at a teaching hospital, right? And this is something that we actually think about a lot as we’re working on artificial intelligence algorithms to assign staffing appropriately to the more complex cases. But what that means at a teaching hospital, and it’s not just Cleveland Clinic, it’s any teaching hospital, is let’s say that you’re working with post-surgery care of patients. And we want to give the healthiest.
the quickest recovery patients to the youngest doctors, right? Your residents, your people who are fresh out of medical school. So they’re getting all of the easy cases. And then the difficult, the complex, the people with multiple chronic conditions, those go to the more experienced clinicians. So actually as you work and you gain experience, what you’re gaining for all of your hard work is a more complex and more draining workload. This creates like this overwhelming cognitive load and humans really aren’t even highly trained doctors.
aren’t meant to only deal with high stress, high complexity tasks all day for a full 12 hour shift.
Brad Owens (04:04)
So then we’re talking about how AI is going to revolutionize business and make everything easier and take all this stuff off our plate. But what we’re discovering here, and I understand you actually did a lot of research on this too, this may not be the case. It may actually make business harder. And this isn’t something that all of the hype surrounding AI is really talking about right now. So we’re here to give it to you straight. So what then is the business impact then? What happens when work becomes too hard?
Jennifer Owens (04:27)
Yeah.
Yeah, so the mental model that I was working with, health care background, was thinking about nursing and doctors during COVID. All of a sudden, we had a new condition that was raising the complexity of patients. It was raising the risk that they were going to have serious long-term effects or even die. And so we know in health care that when COVID happened, our burnout skyrocketed. And I thought, OK, well, is that really true across multiple?
multiple industries, multiple position types, or is it just in healthcare when you have a global pandemic, which is stressful for many other kinds of reasons as well? What I found in my research is it doesn’t matter, right? If you’re a teacher, if you’re a firefighter, if you’re a paramedic, if you’re a doctor, a nurse, a surgeon, the more your cognitive load goes up, and there’s nifty tools that we’re not gonna spend time talking about it to measure the cognitive load. So it’s not just the complexity of the task, can you get your work done in a reasonable amount of time?
How emotionally difficult is that work? Is it really frustrating? Are you finding yourself like butting heads with your coworkers over things, or is everybody collaborating together? As we find that cognitive load going up, work performance across several metrics suffers. Your decision-making abilities suffer. Your ability to switch focus between multiple tasks, and I don’t know a single job these days that doesn’t require some form of multitasking, it’s something that humans aren’t great at doing anyway. We’re really great at telling ourselves we’re good at it.
But as your cognitive load goes up, your ability to effectively multitask goes down. Your ability to sustain focus goes down. And your decision-making, when you’re in those tough life or death type decisions, your decision-making actually kind of crumbles a little bit too. That high cognitive load is terrible for generating high quality work.
Brad Owens (06:16)
Yeah. So think about those doctors. Think about those individuals that, know, they get no break every single decision they have to make. It’s potentially life or death. That is not something we want for our workers. So then when we think about business as a whole,
Business needs to think about automation differently. They should think about this AI infusion and what tasks we take off and, the types of things that we automate. should think about that very, very closely. So what are some of the things we feel like businesses should do different?
Jennifer Owens (06:53)
So I think what’s interesting is we think about digital resources as having linear productivity. If you apply more resources, you apply more compute, you get more product out. If I’ve got an automation that will do one task for every 10 minutes, if I get 10 of those, then I’ll do 10 tasks every 10 minutes. Humans aren’t linear, first and foremost. And secondly, capacity does not scale.
linearly with the complexity of the task, right? We can maybe manage one high complexity task or three or four low complexity tasks. So as we’re thinking about how to incorporate digital labor into our businesses, we need to balance that automation so that you’re not sticking the humans just with the hard stuff. We need to make sure that humans have time built in their day to allow for that restorative mental process, to take off that cognitive load, to make sure that really to make sure that your human beings still have time to take their breaks and talk with their colleagues and do the things that make us human.
I keep thinking about that tweet that everybody likes to discuss in artificial intelligence that I don’t want AI to make music and art while I do laundry and dishes. I want AI to do the laundry and dishes so that I can make music and art. And that’s great. But even a person who is doing music and art all the time still needs those breaks to rest and refresh and bring a different perspective to their work. So we want to make sure that we’re using, we want to kind of balance how you’re using your artificial intelligence and your digital labor, right? You want to make sure that you’re using it
to remove complexity from the complex tasks as well as automating the easy stuff. So if I have like an FTE that is 100 % dedicated to something, can we take 20 % of the complexity out of their highest complexity work and automate maybe their 20 % of the work that’s just so easy that they’re frustrated with doing it. That’s a decent balanced automation platform. I like that approach quite a bit.
Brad Owens (08:41)
Sure. And when we keep coming back to a lot is you’ve mentioned this in the hospital systems for using AI in radiology. So there is technology that will help doctors to process and to read scans faster. So that’s not making decisions. That’s not looking at the thing and actually telling patients outcomes and looking at that. It’s at least augmenting some of the jobs that it’s a little easier.
So we’re not talking about completely offloading an easy task. What we’re doing is we’re using AI to reduce some of the complexity of a task that typically was crazy complicated and making it a little bit easier for them. So we’re not particularly just leaving these doctors with just only the hard decisions. We’re allowing them to have a easier work day for things that used to be complex for them. So
One of the problems that I see in business is right now we, the ROI and the kind of metrics that we’re looking at on this AI stuff is productivity focus metrics. Like, my gosh, they can do all this work. They never take breaks. work 24 seven. we’re talking about offloading all these repetitive tasks.
So that people in business can work on the harder stuff, the stuff AI can’t do. But what we’re talking about here is that’s not always fantastic. That may actually lead to a ton of burnout and actually reduce your business output significantly. So it’s something that you absolutely need to take.
Jennifer Owens (10:13)
So in one of our earlier episodes, I called out the two by two matrix that I use to think about AI use cases. And so it’s just a grid. And maybe we can even pop up a little schematic here. Maybe we might be able to draw that. But on one axis, we have what people are good at and what people are not good at. On the other axis, we have what AI is good at and what AI is not good at. And you really want to focus your work into the two corners of this matrix. You want people doing what people are good at and AI is not.
And you want AI doing what AI is good at and people are not. And then as we’re continuing our discussion, we want to think about, OK, well, if we have people doing only what people are good at, are there ways to use AI to reduce the complexity, to reduce the burnout of that particular workload? And then are there things that AI is doing that people might just enjoy doing? Maybe you’re the kind of person who really gets a kick out of filling out your own expense reports. That’s fine, right? You do you. That’s not my jam.
But if that’s something that provides you a welcome break in your day, you know, from maybe like from the rest of your work, then maybe that’s something that you retain and you don’t use a tool to do that. I think that’s a perfectly reasonable balance to strike.
Brad Owens (11:23)
So let’s wrap this all up then with some key takeaways then. From my perspective, I feel like AI at this point isn’t just changing jobs if we look at it that way. It’s making them more mentally and emotionally draining if we don’t put in check what we’re actually automating. And businesses that look at this automation just as a, we’re gonna increase productivity and allow people to do what people are good at. I mean, they’re gonna face burnout. They’re gonna face high turnover.
They’re going to have declining productivity from all their actual workers. They’re setting themselves up for a big, big problem.
So what I feel like businesses need to understand is they have to rethink how AI is going to reshape their work before you get into the period of burnout, high turnover, declining productivity. So put some effort into.
Jennifer Owens (12:12)
If you like today’s discussion, please subscribe, share, and tell us. I want to know, has automation made your job easier or harder? How do you think businesses should handle this shift? If you’re a business owner, if you’re thinking about adding AI into your labor pool, how are you thinking about what that’s going to do to your human employees, to their workload, and to their job satisfaction? If AI is reshaping your job in any other way, we want to hear your story. Please drop us a comment. Shoot us a message. We would love to hear from you.
Brad Owens (12:40)
If you like this kind of content, there is plenty more at digital labor lab dot com, where we explore the future of work one experiment at a time. You can follow us on your social media profiles of choice at digital labor lab. We will see you next week. Please email us at hello at digital labor lab dot com. If you’ve got some questions you want us to tackle until next time, I’m Brad Owens. We’ll see you next time.
Jennifer Owens (13:00)
I’m Jennie Owens.