Impact Podcast | Are you prepared to lead in the modern world of work?

KANINA BLANCHARD (KB): Exclusive insights, actionable strategies and ideas that ignite change. You're listening to the Ivey Impact Podcast from Ivey Business School.

JULIAN BIRKINSHAW (JB): Hello and welcome to Dialogue with the Dean. The flagship series on the Ivey Impact Podcast. I'm Julian Birkinshaw, Dean of the Ivey Business School, and in this series, I speak with Ivey's leading faculty about the research shaping business and society today. 

In this episode, Rob Austin, Professor of Information Systems and Ivey's Evolution of Work chair, takes the hot seat to explore three questions every leader should be asking right now. How does the evolving nature of work change how we need to manage it? What's special about creative work, and how does that affect how we should manage it? And why is neuroinclusion in work settings not just a moral imperative, but a competitive advantage? A distinguished and sought after expert with decades of influential research, Rob is sure to bring a wealth of insights and perhaps a few surprises to our conversation.

I'm excited to get underway. Rob, welcome to Dialogue with the Dean. It's great to have you here. 

ROB AUSTIN (RA): Well thank you for the invite, Julian. It's great to be here. 

JB: Thanks. So you've had a long and distinguished career. Just tell us a little bit about what brought you to Ivey. 

RA: So I came to Ivey in 2016. I've wandered a bit in my career. I started in industry with the Ford Motor Company and a software company called Novell. But I eventually found myself a Professor at the Harvard Business School for about 12.5 years. My wife's career took us to Copenhagen Business School for a while. I spent a couple of years as the Dean of the Management Faculty at the University of New Brunswick.

But in 2016, I think we just had a confluence of factors that brought us back here. A lot of it had to do with our kids wanting to go to universities in Canada. So, and we're a two-career family. It worked for both of us. 

JB: It's great to have you both here with us. We'll dive into the first section which is around knowledge work. What do we mean by knowledge work? Let's just start with it with a basic question. 

RA: So usually, I mean, there's a famous definition that goes all the way back to Peter Drucker in 1959 or something like that. He says it's work with the mind, not with the hand or something like that. But I usually describe it as work where the value creating transformations happen in the realm of symbols, ideas, and so forth. It's largely intangible work. 

JB: And that must be most work now. I mean, you know, certainly in a place like Ivey where all knowledge workers, right? And indeed, most big companies nowadays, I would say that was the norm, not the exception.

RA: No, I would totally agree that increasingly work that matters the most is knowledge work,
right? I mean, if you go back decades and decades, then there was a lot of physical work that was involved that was key to business. But today, much of what even industrial companies do is knowledge work.

JB: That's right. And obviously manual work, working with the hands, it still exists, but it's an increasingly small percentage of the total amount of work that that's done.

So you gave me a paper to read in advance on this social measurement of knowledge work. What's the what's the issue there? What's the challenge that, I think, is actually a very practical challenge for everybody, but just unpack us.

RA: It is mentioned earlier that much knowledge work, and I think this is pretty intuitive, is intangible, right? So, one of the distinguishing characteristics of knowledge work relative to physical work is that it is harder to observe, right? So, it involves inputs that are not necessarily easily visible. It involves transformation. Most of the work that's done is not easily visible. I would say we are, and you mentioned this before, increasingly in a world where the people who are managing or supervising not only can't observe the work directly, but may not even understand it, right?

So I had a CEO, he was frustrated. He said to me once, about his software developers, he said, "sometimes they're pounding away at their keyboards, sometimes they're standing in the hall sipping coffee and talking." He said, "I don't know what any of that means," right? And so I think that's increasingly the challenge. The specific challenge is how do we evaluate work that we can't see very well and can't understand very easily?

JB: Yeah. So, if you compared a knowledge worker to a manual worker, you know, and you go back to the famous car factories of 100 years ago and, you know, the famous concept of scientific management, where we we do time and motion studies and we watch how that manual labourer is doing the job, and we try to sort of optimize that. We can observe and we can see when the slacking off, we can see when they do a good job. Your point is, we have no idea what's going on inside that software programmer's mind and whether he or she is working or whether their goofing off, right?

RA: That is certainly true. But it's also true that they, the workers, are much more qualified to design their own work, right? They're also unlike, I would say, many physical workers, more interested in their work and therefore more intrinsically motivated to take it in new directions, to improve upon it right? And to make their own improvements to the way they're working. 

JB: Right. And one of those basic principles, it was called Theory X, Theory Y years ago, is that if work is intrinsically interesting, then you design a management system which is basically just to to give people the freedom to do the interesting work and just assume that they're going to do a good job. Now, I guess there's limits to that. 

RA: Yeah, I think there's limits to it. But I do also think that we, you know, there still continues to be a fair amount of, polarization, in, confusion on that point. Right. So, one of the things, I worked with, a former colleague, Dick Nolan, at Harvard Business School. You know, Dick and I, in something we've written recently, distinguish between what we call managing by holding accountable and managing by giving people a reason to give a damn, right? 

Obviously those line up pretty well with we're going to carrot and stick people in a sort of, that would be Theory X, right? And then in Theory Y is we're going to give people, we're going to rely on their intrinsic motivation. One of the dimensions, I think is very important, but under appreciated, is that if you overemphasize the incentive part, that if you and some people, I think, do think that management is mostly about arranging incentives, I think there's potential to do harm in knowledge work.

JB: Yeah. So to go back to the question, how do you actually evaluate knowledge work? The obvious answer is, we evaluate on outputs rather than in.

RA: So yeah. Outputs, that's important. The trick is though, is that outputs are sometimes very elusive, right? So what is the, what is the output of a pharmaceutical researcher, right? That's not very easy to specify. And so we usually end up using proxy measures, right? Which are not very- don't completely encompass what we're looking for them to do. So, maybe we pay them for patents, or something. But then we get this sort of problematic behaviour sometimes, right, where we divide up a big patent into five patents to get credit for five. And it's not really what you want your researchers to be working on. 

JB: And the more you measure those specific things, the more those things become the target and you actually lose the essence of what it is you're trying to do.

*MUSICAL BREAK*

JB: Let's talk about generative AI, because it's top of mind for all of us. It's clear that knowledge workers are using ChatGPT and its ilk to do their jobs better. And there's lots of data now on individual level productivity improvements that are being achieved- often 20, 30, 40% improvements. This directly leads into, sorry, feeds into the conversation we've just been having, which is, should companies be attempting to, almost like, sort of extract that value by essentially employing fewer people because they know those people are now working, more efficiently and effectively using ChatGPT?

RA: Yeah, I think I come down pretty firmly in the camp that generative AI should be augmenting human capabilities. In some cases there will be some replacing. But I think, you know, there's a lot of fear about that, right? And the fear can have its own dysfunctional consequences, I think. 

Was it, in 2023 because ChatGPT emerged in 2022, November? You know, you probably know this story. CNet was doing very early things and interesting things by having, they figured, quite rightly I think, that they were going to be impacted by it. So, they were having, generative AI create simple stories and they were signing stories. You know, the byline was written by AI. But about May of 2023, their staff revolted, right? They marched into the senior office and said, you know, "you're overstating what AI is doing, not crediting humans enough." And I think, you know, they did really great work on what was possible, but they forgot about the people part. So I just also think that, you know, separating out- there's a long history, in economic theory for example, of efforts. 

The paper that comes to mind is Alchian and Demsetz, 1972. Basically, it's really hard to separate, when two people are working together, to say who's contributing what. You know, I think that's going to be true when generative AI is being used to augment human capabilities. 

JB: So, and of course, I agree with you that we've got to find a way of harnessing AI in a way that is augmenting. But even if it's augmenting human capability, there are going to be many functional activities where we just don't need quite as many people in those roles. And for me, the interesting question is, to what extent should companies be actually kind of pushing quite hard on that, and saying we need fewer people, or perhaps we just don't need to hire quite so many? Or, because the risk is if you push too hard on that, you then get a bit of a backlash along the lines of what we've what we've been saying, which is people are feeling that somehow they are having the essence of their work taken away, and then they almost rebel. They don't rebel, but they just, you know, they don't work as hard as they could. Their discretionary effort is no longer put into the task. 

RA: You haven't given them a reason to give a damn. 

JB: Yeah, exactly.
 
RA: Right, going back to an earlier conversation. Yeah.

JB: Okay. Now look, I mean, I don't know if you got anything more to offer on that, move to the next topic?

RA: Well, I do think, you know, AI and the increasing digitalization of work does give us more potential things to measure, right? So there's all this idea about digital traces. Everybody produces a digital exhaust as they move through the world. So there's a lot more things that will tempt us to measure them. But, we have to keep in mind that all of these things have some of the problems that we talked about before. That we can't solve a human problem with a just a simple technological solution.

*MUSICAL BREAK*

JB: I'm going to move us to our second topic. I mean, there's much more we could have said about that, but let's talk about creative work. How we spark innovation. I'll just, to give us a bit of a sort of a foundation. I read a paper you wrote in the Organization Science Journal, which is a top journal in academia: Accidental Innovation: Supporting Valuable Unpredictability in the Creative Process.So just give us a couple of sentences of what the key, kind of, finding in that paper is. 

RA: Yeah. So the key idea there is that innovation when it's successful, and especially when it's impactful, depends more on randomness than we like to think, right? It depends more on accidents. And in fact, if you look at the history of invention and discovery, you will find that a great many big innovations in history have something accidental, fortuitous in their background.

That doesn't mean we shouldn't devote management attention. There's things we can do. But, there is a tendency to sort of look back at discoveries and make them seem more rational or reasonable or, you know, linear than they really are. Yeah.

JB: And so we've got the famous case of the post-it notes and penicillin that pretty much everybody has now heard. These were accidents. And obviously you can't just say to somebody have lots of accidents. I mean, what is kind of coming out of that research? How do you turn that observation into something that's kind of interesting and perhaps managerially practical.

RA: Yeah, No. That's a very interesting question and an important challenge related to, some of the things we were talking about before. But, so, yeah, there is an exercise, there's a lecture that I like to give in Exec Ed, where we talk about accidents in the history of invention and discovery. And then I propose that we try an exercise and I tell them I'm going to explain the exercise before we do it. 

So step one is I want everybody to stand up. I said, "don't do it yet, but step two is I want you to get your hands on a phone or a computer or whatever else. Step three is I'm going to count to three. I'm going to go one, two, three. And step four is when I say three, I want you to throw your phone or your computer as hard as possible against a wall or the floor. And step five is we're going to look at the results to see if we've invented anything but that's great."

And nobody wants to do this obviously, right? Every once in a while I get somebody that says, "I hate my phone, I'll do it," right? But then we explore and I think it's exactly the question you just ask. Why. given that we know that randomness is so important in innovation, why aren't we willing to do this?

And I think the key insight is that, harmful randomness is much more common than helpful randomness, right? So we can't, as you said, just run around through organizations saying cause accidents or, you know, shake things up, or be looser as an innovation manager. I think what we have to do, it's fundamentally about what happens in an organization after something unexpected happens, right?

Now, in most organizations, after something unexpected happens, the human, the organizational tendency is to race ahead to something else. Move the attention on as quickly as possible so that nobody sees that something unanticipated happened, that something didn't go according to plan. And in fact, I like to note sometimes that, you know, even when we do pay attention afterwards, we call those efforts frequently post-mortems, which in Latin means after death.
And the sole purpose of a postmortem usually is to make sure it absolutely never happens again, that something happens off plan. And I would suggest that is not the right approach, given that randomness is so important that instead, what we see in innovative companies is, after something unexpected happens, people have a tendency to kind of lurk and go: hmm, why did that happen?

I'm dating myself, but many years ago there was a late night TV host, Arsenio Hall, and he used to do this segment called: Things That Make You Go Hmmm, right? And that's what we need in organizations. We need to, after something unexpected happens, we need to go, hmmm, let's think about that.

*MUSICAL BREAK*

JB: And linking back our little segment on creative work to the longer segment on knowledge work. We all live through Covid, working from home. And there's obviously a push to get people back into the office. And so my question is, it links to both. Did the work from home experiments tell us anything important about the value of being in person, both for doing creative work and for, you know, doing knowledge work? 

RA: Yeah, I think the work from home experiment, told us like mixed things, right? So I think in some cases it worked very well, and in some cases, you know, I've had a lot of students during Covid call me up, former students, and say, "I'm in my first job. I'm struggling terribly because I don't know anybody," right? I mean, "I only see people on zoom and there's no going to lunches or seeing them at the coffee machine or whatever."

So, there are some serious negative impacts. There are some very favourable positive impacts. And I think we're going to talk about neurodiversity in a few minutes. Neurodistinct people or neurodivergent people, some of them remote worked extremely well for. Not all of them, right? We can't generalize even there. So the only thing that worries me about the return to office thing is I think, you know, we have to be careful not to, I mean, same thing with teaching remotely, right? We have to be careful not to lose the learning that we gained. So to the extent that some things worked well and some things didn't, I think we ought to work, you know, we ought to try to do something about the things that didn't work, but we ought to try to lock in some of the things that did. And a lot of what I see happening out in the world right now seems a little bit too monolithic, right? Everyone will return to work. 

JB: And as you say, that can't be the solution. I mean I think linking to your work on creativity, I mean, a lot of what you said about, seeing and, and taking part in things which didn't work and learning from it, there's a social element to the learning there, which I think probably did not work very well when we were all sitting in our homes.

RA: I think that's certainly true. You know, one of the things, one of the constructs that I played around with in my research over the years is the idea of ensemble, right? We take it from the arts, and I've literally studied ensembles, I had a doctoral student who studied string quartets. And you're absolutely right that when that creative process is really going well, somebody does something different and then somebody else fires off on that and does something else a little different, and then a third person. There's a sort of a wonderful chain reaction of newness that happens when a creative process reaches a point that you could call ensemble, right? And we don't get that when- proximity is important in that. 

JB: And there were many things which we did a decent approximation of over zoom, but truly creative work. I think that was sort of the one thing that really required us to be, you know, back in the same place, working together as you said in an ensemble way.

*MUSICAL BREAK*

JB: Let's turn to the third topic, around neurodiversity. So you're going to have to define what we mean by neurodiversity, and then we'll talk a little bit about some of the work you've done on it. 

RA: Sure. So neurodiversity, it's a moving target, the definition. But it generally refers to, a variety of conditions that have historically been considered disorders. So for example, autism, dyslexia, dyspraxia. Increasingly these days we broaden it to include things like social anxiety disorders. The neuro part suggests that there's something neurological to it. But, I think that connection is a little looser than it used to be. From the context of organizations and employment, I think the crucial thing is that it has the potential to interfere with our assessment of people's performance, their talent levels, their skills.

And so the idea is, there's a lot of people out there who have a lot of skill, who are quite deserving, who when we interact with them, something in the interaction interferes with our discernment of their talents. 

JB: Got it. And it does link back to our earlier bit of the conversation, because if you use traditional performance measurements, a lot of these people are falling short on some of that stuff, but they've got other talents which we're not recognizing.

So talk about the paper: How Neuroinclusion Builds Organizational Capabilities. You work with EY, Microsoft, SAP, perhaps, among others. Tell us what the story in this paper is.

RA: We have had the good fortune of being able to work with some real thought leaders out in industry. You mentioned some of the companies just now. And there are many others. You know, this whole thing started in about 2004, and there was a firm that was started in Denmark, just for the specific reason of employing people on the autism spectrum. And that company has since trained, I think all of the companies you just mentioned. That's how their programs got started. But, most of them have been doing it about ten years. And so we've kind of been there on the sidelines watching how all of this has unfolded. And there has been an evolution in the way that they, you know, set up these programs, run these programs, and so forth. Overall, I think they've been wildly successful. There is a question, however, of whether they're big enough yet, right? I mean, one of the questions we asked in October of last year when we met with them all on the Microsoft campus was: "how can we multiply the impacts of this ten times, 100 times, a thousand times?" Because there's many, many more people who could be potentially impacted and much more talent out there to be assessed.

This paper though, we one of the things we were pointing out, and this is not intuitive to people who think about this, right? First reflex is to think of this as about eliminating bias. And that is true. That is part of what we need to do. But the big thing we were focused on on that paper is that we actually create organizational capabilities when we broaden the kinds of people we're willing to include in especially conversations, for example, about innovation.

JB: So can you give us an example of where you see this working well?

RA: I can give you a specific example. At SAP, there is what's called The Founders Prize, an Innovation prize, the Hasso Plattner Award, for the best innovation in SAP every year. It's usually given to a team of people. The only person who's ever won as an individual was recruited through, SAP's Autism at Work program. And I wish I could think of his name right now, but because he deserves the credit, but a brilliant guy, who wouldn't have even been in the company except for a program like this.

JB: And so, I mean, of course, it links directly back to your welcome creativity because true creativity involves thinking outside the box, looking at problems from new angles. And your point is that neurodiversity is almost literally the the language we use for seeing things in different ways is not right. 

RA: That's right. And, you know, one of the ways I think it's useful to think about innovation processes, and this is taken from, as a creativity scholar, 1960 or so, Donald Campbell, he said, you can think about it in Darwinian terms, right?

The first part is you got to come up with ideas that are sufficiently different, and people have trouble with that. I mean, the most easiest thing to think of is something like something we've already thought of, right? So first you need somebody to think differently, to come up with different ideas. But then there's also in Darwinian terms, what we call selective retention. That's like what do we decide to keep? What do we decide to set aside. And that also is hard for humans in human organizations. So, some famous organizations like, Xerox Parc in the early 70s invented things, discovered things that are worth trillions of market capitalization now, but they commercialize very little of it because they couldn't look at it and see the value. That's selective retention.

So, being able to come up with new things is the first thing. Being able to look at value in unfamiliar forms and recognize the value, that's a second way that thinking differently helps. 

JB: And obviously to the extent to which your organization takes neurodiversity series as an asset, as a potential asset, you're more likely to build those those capabilities.

RA: Yep. And that's one that we talked about in the paper. Another is that we find, almost to a company that we've interviewed and worked with, we've done field work. They will say, "this program has made us better managers. The management capability that we have has improved across the board." And when we probed this, what they say is, well, as a manager, first of all, it makes me think carefully about my supervisory style. So it makes me more introspective when I have under a distinct person working for me. But even more important is if somebody is on the autism spectrum, I sort of have to ask a question how should I arrange the conditions in which they work so that they can contribute to the maximum degree? And if I don't do that, then they may not be able to contribute very well, right? It's required with people on the autism spectrum. 

But then they say, well, then it dawns on us that that's not such a bad question to ask about all of my employees, whether they're neurodivergent or not, right? And so they'll say, this is the way I manage now and my teams are more productive. A hypothetical I often raise in lectures is what if through that mechanism, you could increase the performance of your teams because of more effective supervision? 5%? 10%? 15%? Would that be a big deal for your company? I think it would be, yeah.

JB: It goes all the way back to the start of our conversation about knowledge work, which is,  your job as a manager or a leader is to to get the most out of your people, without being able to kind of peer inside their brain and actually figure out how it works. So you've got to put in place the conditions that help them and everybody's different, right? I mean, and so good leadership is about actually helping everybody to realize their potential or whatever it is.

*MUSICAL BREAK*

JB: Look, we must we must wrap up. We've covered a lot of ground. 

RA: And it's been great. 

JB: We could talk longer, but, if there's one takeaway from today that you'd like our listeners to remember, can you reprise that for us now?

RA: I don't know if there's a single one, but for sure, back to our be careful with measurement, measurement especially of knowledge work, creative work, is powerful, but that's not always good news, right? So, you know, what do they say? What's that old saying? What gets measured, gets managed. But that's not necessarily good news, because you may be not managing something that can't be measured. And so, be careful. 

Give, as a manager, I really increasingly these days like the idea of managing by giving people a reason to give a damn, right? And I worry about some of the things you read in the business press. Things seem to be getting more transactional in managing people in some companies, it's pretty easy, you know, to, to do harm, to do to some capability, that's human knowhow. History that doesn't show up on the balance sheet. That's one of the reasons it's so tempting to cash it out, right? But if we start to get transactional, it's really hard to pull that back. Right? It's hard to get those that giving a damn, this commitment to the company, commitment to the cause, hard to get it back. 

JB: No, I think that's spot on and it's a great place to finish. So thank you very much. 

JB: You've been listening to Dialogue with the Dean from Ivey Business School. My thanks to Rob Austin for sharing such fascinating and wide ranging insights, and to you, our listeners, for joining us. If you enjoyed the conversation, don't forget to follow the podcast, share it with a friend or colleague, and stay tuned for more in-depth talks with Ivey faculty on the ideas shaping business and society. Until next time, goodbye.

KB: This was Dialogue with the Dean an Ivey Impact Podcast series. For more insights from Ivey, including thought leadership on critical issues and additional podcast episodes, visit Ivey Impact or subscribe on your preferred podcast platform. Thanks for tuning in.