Impact Podcast | What turns new technology into real transformation?

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. On today's episode, we're exploring digital transformation - not as a buzzword, strategy slide or budget line, but as an experience many leaders know well. It often begins the same way: a substantial investment in a promising new workplace technology is approved. The system goes live organization wide, and expectations are high. Over time, however, a realization emerges. Implementation has occurred, but the broader transformation remains elusive. So why is that? To explore that question, I'm joined by Nicole Haggerty, Ivey Associate Dean of Faculty, Associate Professor of Digital Innovation and Information Systems, and a leading scholar in digital transformation. In our conversation, she draws on her new research in Canadian healthcare to unpack how transformation unfolds in practice, where it stalls, and what it takes from leaders to see it through.

Nicole, welcome to Dialogue with Dean. It's a pleasure to have you here.

NICOLE HAGGERTY (NH): Thanks, Julian. It's great to be here.

JB: So we're going to dive into your research and then look at transformation in the current market environment. But before we do so, just give us a sense, what should listeners be expecting to walk away with from this conversation?

NH: I'd say probably the biggest message I would give from this research, and other research I've done over many years, is that transformation is never about technology, right? It is always about how people use technology to create new workplace processes and routines that create value for organizations. And the biggest challenge most organizations and leaders face is they think of it too much as a technology project.

JB: Okay, good. And we're going to get into some of the... literally the kind of behavioral changes that are required in order to make these tech projects work. 

And just a bit about your background, I gather you had a significant career in the private sector before you became an academic?

NH:I did, yeah. 

JB: And what motivated your shift?

NH: I'd say that my major motivation was when I was in the private sector, I realized that a lot of change that needed to happen in organizations was being driven by technology, and it kept failing. The IT project failure rate remains remarkably high given how long we've been studying it. And, in my private sector work, we were trying to generate value for Fortune 500 companies using technology, customized technology. And, we realized, or at least I realized, you know, this is really curious to me. Why is this continuing to be such a struggle, especially for the business side of the house?

JB: Right.

NH: Because it's really about the technology itself. And that's what I came back to study.

JB: Cool. Yes, many billion dollar projects have basically, you know, happened and lost a lot of money for the governments and private sector organization.

NH: Yeah. And when I came back, it was ERP systems in the 2000s. Today it's you know, hey, how much are organizations investing in AI and when will we see value from it? 

*MUSICAL INTERLUDE*

JB: So two of your academic papers, and we're going to try to get the essence of what you studied. One is called Ruptures During IT Enabled Change. And this was a study in, I think, ten hospitals over a number of years. So what's the story here? What is a rupture?

NH: I was really, I'm very interested in sort of the micro dynamics of change. So a lot of people, yourself included, study change at a very high strategic level. I went into hospitals, first and foremost, because the Canadian health care system needs to figure out how to use technology effectively. But in this particular study, we were interested in following the design phase. How does design unfold in ways that serve the ultimate value generation organization? 

JB: So the design of a new computer system, in simple terms, is that right? 

NH: Yeah! Implementation of electronic health records. That's really what it was. So ruptures, the interesting thing that we observed were these moments in time when the individuals, both the technology side of the house, the managerial side of the house, the clinicians would have moments where they would have failures, they would have surprises, disappointments, confusion over something that was happening, in the design phase. And it turns out those moments, those ruptures, are moments to pay attention to because they will later, at the moment and later, they reveal things, that need to be overcome in order for value to be unleashed at some point in the future. 

JB: And, so when you say failure, you're not saying the I.T system, the database, doesn't work. You're saying there is a gap between what the I.T system, for example, was, was intended to do and perhaps what the users might have perceived. Can you give us a real example?

NH: Yeah. So I'll give you a few, two examples. One super simple. We were observing a meeting in which a radiologist and, an internal medicine specialist were having a debate about a new way that the system, the electronic health record, would interact between these two providers when ordering an X-ray.

And so, in the past, on a paper system, the, internal medicine specialist would just, use a paper system and make check marks on the system for what they wanted, or they might not. They might leave it to the, you know, the radiologist would get the information and they couldn't schedule properly because it didn't have all the information they needed to make a schedule. There was a real debate between the two about what the system should make possible or not possible. The radiologist wanted the system to make internal medicine people log every exact thing that they needed at the moment that they were ordering thee x ray. The doctor was saying we can't possibly do that. We don't know everything at the time we're ordering the x ray. It'll slow us down; we won't be able to provide care. So they had this great debate over it. In the end, the team said we were going to make physicians do this. It's their responsibility. They have to fill out this information. We'll cull it to the best five questions that we ask in order for radiology to do their work.

But later on in testing, the radiologists saw that as they were using it, they're like, oh, this is actually going to be a problem. So that rupture was a moment to uncover this workflow that had to be automated in a way to make these two groups to work together. And they didn't agree. So that rupture came for a disagreement about whose responsibility it was to do particular work.

JB: So, in terms of implications, you know, much wider than this...It's often tempting in a disagreement to kind of paper over the cracks, to sort of try to kind of fudge it and move on. You're saying this is actually, sometimes, really gets to the point, even though it's a micro issue. It speaks to the bigger question about will this technology ever fulfill its ultimate sort of purpose or objectives?

NH: It goes to the micro moments where, workflow and the way in which two places that have not worked together very likely to work together in order to achieve value at the end, but smooth flow of patients through the hospital to get the images they need. It really speaks to how you navigate that disagreement between the two of them so that the system will work effectively. So it is a very micro moment, but it's quite revealing in terms of some very common inter-professional challenges.

JB: And the point is that if the system is designed in a kind of, slightly bullheaded way, then the physicians and others will just reject it. They will stop using it.

NH: They will reject it. They'll find a way around it, or worse, it will impair the work. It will impair the work of providing care to patients in the moment.

JB: Which is why I.T. change, digital change, is difficult. Because, you know, it's tempting to say we want to just sort of push through this new system, but you're trying to push it through with a whole bunch of people with existing processes, existing understandings of what's needed. And they've got to all kind of be brought along on the journey. 

NH: Well, and I think of it, if I go up to the sort of strategic level. The level that you're at. You know, leaders in an organization say we need to implement this technology to create this value. We need electronic health records to improve quality of patient care and the value of information we can extract from the system.

So we've had very high leadership level saying we need to accomplish this. But in the micro moves, in the workflows that happen within a hospital setting, or financial institutions, an energy company, in those workflows is where that value starts. It can't get created at the end,  if it doesn't get created in that messy middle, in the micro moments.

JB: And so keeping your eye on these ruptures, which manifest themselves, what is disagreements typically? Are there others?

NH: Disagreements, surprises. Another rupture that occurred was literally almost the rejection of the system in the emergency department. So if we look at in the setting that I was studying the emergency department was is, delivering emergency services the front door of the hospital? So we think of what's the value creation process, right. The delivery of emergency services, either people back out of the system, with treatment, or into the hospital with further care that they need. In the moment that the system was launching, even after multiple efforts to understand what did the emergency physicians need? How many devices did we need in in the emergency room? How are we going to manage the flow of patients? They did all of this preparatory work, checked all the boxes that you would have in a project management, you know, or implementation structure. But when the system went live, they discovered that there was just problems that were happening there. 

A leader could cast that as, as resistance. They're just resisting the system. We've seen that in lots of places. But, when the team went in to study what exactly was happening, they realized that they had not anticipated that the relationship between the way the doctors operated, the way they thought, their diagnostic reasoning, this way the system was operating time lags and even space constraint. These were legitimate problems. They needed to pause the implementation for a bit, and add different resources.

JB: You know, it's very helpful because anybody who either studies or leads change programs knows that, you know,  how do you how do you manage it? You try to get to understand the possible sources of resistance, and you build sort of allies, and you try to make sure there's coalitions to support it. You're saying resistance sometimes is: I really don't want to change the habits of a lifetime. Sometimes it's actually built a much more legitimate, technical, sometimes quite technical reasons for why something isn't going to work. And you've got to sort of separate out the two. 

NH: Yeah. And if you follow the traditional guidelines, it's about, you know, how do we communicate the change? How do we train people. How do we educate them? And it will be based on high level communication strategies. This change is happening. We'll Train you on the features that you're going to use.

And all of those things are very, sensible surface level things. Well, my research points out is it's actually you've got to get into the messy use right system in use, to really understand what's happening.

*MUSICAL INTERLUDE*

JB: The other paper, which also came out last year, is called Generative Mechanisms of I.T. Enabled Transformation. I think you're also in a hospital setting for that. Tell us a bit about what that paper shows.

NH: So, if the first paper was focused on, sort of how individuals come together, how they sense make around these ruptures. What happens? How do I learn what's going on? The paper that was set in the hospital lab, the next paper on generative mechanisms, was really trying to uncover: Why does change happen? What is the underlying cause of change happening? So that leaders can pay attention to managing the underlying cause and not just sort of the surface level.

So in that case, we followed, the change of the hospital lab environment that was both automating and infomating. They were both automating the process of dealing with samples from patients, and then how those samples would be processed and the information that would be taken from them and fed back to the electronic health record. And we identified four generative mechanisms.

And they're going to sound in some ways very obvious, I think. Which is, you know, you start with generative mechanism, inertial change. How do you break down inertia in an organization. And we often think of that as the act of sharing a new vision, new strategy. And what we saw and I would, you know, draw on, the sense making lens here. Yeah. It's common. We need to share the vision of the technology, but it's that how you do it that matters right. So it is true that, breaking inertia is a really important part of causing change in an organization. But how do you do that? Is it just we're going to convey a new  vision? Or do we work, in this case, the leaders worked with the employees there to reformulate what the organization was going to be. We're not just going to be a new digitalized hospital lab. We're going to be something different. And we're going to build that vision together.

JB: Got it. So, and as you said, there's four of them, and perhaps we didn't get through all four of them, but your point is that you're mapping out through this very careful research, the processes by which an IT transformation works, specifically in terms of the way that leaders and people in the organization are kind of making sense of this new reality and trying to sort of get their heads around the bigger objectives of it. Is that right? 

NH: Yeah. And it's a discovery process. I think that's probably one of the more important observations about it. When you're trying to transform, when you're being disrupted, and you have to create a new way of being, you can't predict what that's going to be. Sometimes you can't even imagine what the possibility is.

And so these this emphasis that we were taking to be in the messy middle, to follow organizations for a period of time was to unpack and uncover what are the learning processes that are happening here? How do people learn their way into the future they're trying to create? And that learning process that runs through both not quite described in exactly the same way, in both those papers, is the thing that leaders need to pay attention to.

*MUSICAL INTERLUDE*

JB: So, and this does of course now open up a conversation about the wider notion of digital transformation - whether in a hospital, we're in a university and a business. And I've, as you say, studied some of these things myself. My most recent book was all about, you know, successful and failed digital transformations. Particularly kind of internet based businesses. What are the kind of key takeaways for a business leader? So, you know, many of our listeners will be working in, you know, commercial companies. They will be engaging in some sort of digital transformation process, almost like as we speak. And we'll get into AI specifically later. What are the other the warning signs of where something's going awry, or perhaps the evidence that things are working in terms of the IT transformation?

NH: I think there's a few things. I think, first and foremost, that silence is a death knell. Not hearing anything about what's happening as transformation is happening, is a very bad sign that things are going wrong. Or, organizations need to learn as they go and leaders need to track not what they're learning, that's important, it's not just that, but how they're learning. How is learning happening as I am going through this digital transformation process? So they should be looking for signals like, you know, how many ruptures. How many are happening and where are they happening? And they're not just a simple disagreement. I mean,  interprofessional disagreement that can happen. But, when something escalates back to the team, we need to rethink this. Leaders should be tracking. Where's that happening? What are the what are the pieces of this puzzle? Let's unpack it and understand it. And then let's, you know, try something new and move forward. So I think tracking ruptures is one thing that leaders need to track.

JB: So we'll make it super practical now because, we Ivey, you know, recently announced that we're engaging in an artificial intelligence transformation program. We provided, essentially, high quality, large language model access to everybody. We're announcing various other initiatives. So, what's your advice to me in terms of how do I ensure that this particular digital transformation process works?

NH: Well, I think it's about having an eye on: what's the value you're trying to create. What value are you trying to create? Is it Student experience? Is it global opportunity? If we link it to the Bold Ambition. You know, it is what is the new way of casting business education? And then, if that's the value, then everybody needs to have their eye on: how do I contribute to that value and how will my adoption of artificial intelligence, using it in some fashion,
how is it going to contribute to that mission? So there's a there's a translation of what your vision is, and the value that you want to see. It may be that it has to be built collectively, we have to figure that out together. I think the other thing I'd observe is, you know, putting the AI in place and making it available is going to increase people's work. People are 100% occupied. 

JB: Yeah. That's right. 

NH: So we're asking them to do more. 

JB: Yeah. In the short term they're definitely doing more. 

NH: They're going to do more. And then we have to figure out then what's the path to reconfiguring the work that has to be done. Because it's not about, contrary to what you know, maybe this is a controversial opinion or not, I don't think it's about saving money by reducing headcount at all. I think the transformation afoot for most organizations, and for us in particular, is reconfiguring the work we do to deliver value. 

*MUSICAL INTERLUDE*

JB: So, let's move beyond just our university setting to the settings of many, many businesses. AI is positioned in some people's minds as a productivity tool. And, indeed, a great deal of the expenditure is premised on the idea that we're going to be able to get a productivity benefit more with less. And yet you have, I think, correctly said that certainly in the short term, what's happening is people are still doing their jobs and they're now, often using AI to complement their work - without it actually having any demonstrable impact on productivity. So, how do you think this plays out? Do you imagine that productivity will grow? Will it will be enhanced through the roll out of AI systems? 

NH: You know, I think there are some very low hanging fruit, where an AI agent in customer service might be to do something of value that will reduce maybe the number of people who need to do that work. But I think for most organizations, and for the vast amount of work that they do, that is value creating work, it's going to be you have more to do. Because now you're not only using AI to reduce the amount of time, let's say I use AI to reduce the amount of time I have used in the past to analyze an Excel spreadsheet. Now that time has to go - the time I spent to go - into auto validating that the results I got are correct and appropriate, that I have used the right tool, that I'm doing something that the organization values. There's governance mechanisms, there's a whole bunch of new work that has to actually be created, in order to see the value from that, right?

JB: I mean, I think I agree with you. But, you know, the big proponents of AI are saying, you know, you don't get it. This technology, you know, coding mechanisms, creating spreadsheets, these can now be done 90% by the AI. And there's only really maybe 10% of the work left that  the human has to do to about it. And even that potentially is going away. So there is quite a gap between, you know, the lived experience of, you know, people like us in terms of using these technologies today versus the visions of some of the AI protagonists - that work is never going to be the same again.

NH: So let me tell you the number of protagonists - and I'll recast them as the generation of the desktop computer people who said this will transform work. Who then morphed into the enterprise systems people at Y2K who said this will transform work. And then those were the internet and the World Wide Web people and the e-commerce people that said this will transform work. They are all moments. We go through social media, mobile computing, cloud computing, AI, generative AI... They all claim these massive transformations, and the work does get changed.  Unquestionably saying the work doesn't get changed. What I'd say is It'll be sometime before any incumbent has transformed sufficiently to see reduce headcount while creating the same value that they're gonna create right now.

We could talk about your research, and what it means to be a fast follower. Or, you know, I think you called it Performatively Agile in your work, right? 

JB: Yes. And many of our listeners, rather, won't have read my book, but yeah, I make the point that that even though it's kind of cool to be a first mover, it sounds like what you want it to be. The reality is that most first movers actually fail. And right now we're living through an era where OpenAI with ChatGPT was clearly the first mover in terms of, you know, they launched ChatGPT and whatever, November 2022. And they seem to have a real head start. But right now I wouldn't put any money on them actually winning. I think it's much more likely that, unfortunately, you know, a Google and Microsoft or Meta wins this race and that that actually, you know, these guys at OpenAI might find themselves being acquired. The Netscape Navigator of generative AI, if you see what I mean. So I do believe that the smart strategy for a company, whenever there's a new technology come along, is to be absolutely monitoring and trying out as we are doing, we are dabbling in all these new technologies for  the student experience. But we're not putting huge amounts of money into it.
We are we need to understand what's possible. But the idea that we would throw a lot of money at this right now is something that I absolutely resist. Let's figure out where the world's going before we invest. 

NH: And I'd just add, you know, the strategy level is going to look at it from that perspective. The next level down, the people who report to you, are the people who are going to generate the changes that will come from the technology. So, they need the leadership to know: what are we aiming at? Broadly speaking, what are we aiming at? But whether the AI in itself is just productivity driven or whether it is complimentary and it creates it, it makes the pie bigger. It makes the pie bigger, and it makes things possible. So I think the people who will win are the people who reimagine how work can get done to both achieve their current value, but create new value they didn't imagine. And they can't imagine from where they are because they need the experience with their technology to see it. 

JB: So going back to your first paper, the concept of ruptures, what might be a sort of rupture we might see in terms of the application of AI to, you know, to learning or to our internal processes? I mean, obviously, you wait to see what actually happens. But just give an example of how this concept of ruptures might apply in the AI world.

NH: Well, I think, you know, if we started getting multiple senior leaders together, whose work could be reconfigured by aligning workflow across them, that's where you'd see a rupture. It would be, wait a second, who owns this? Is this a my thing or a your thing? And then how do we make these handoffs?
How do we use AI to trigger these these flows and hand offs? And, now if the AI is taking up that work, what does that leave me to do? And so now I need to talk about, you know, I don't know about you, but if I had more time, there's 100 things that I would want to be able to do that I think of as the higher value add things. But I'm caught in the messy middle of getting these smaller things done.

JB: I mean, and here I completely agree with you, which is that the idea that each of us within our own job, or indeed within our own function, figures out ways of using AI. That's kind of easy. And it's kind of fun to some level of having a new toy to play with. But, as soon as you say, we're now going to rethink the entire system using generative AI mechanisms, that almost by definition means that there's going to be kind of winners and losers. There's going to be some functions which grow in stature and, and jobs and others which lose out. And then the process of change then becomes really difficult. And that's obviously where, you know, many system wide changes have failed in the past.

NH: I think that's absolutely true. And I wouldn't want people to, you know, see that change as necessarily meaning that jobs go away. Work might go away.  But, you know, work has to be reconfigured. But this is, what I'm really curious about over the next little while is, you know, if I look at human resource departments that say, well, we need more talent managers. So we look at this organization, we look at where our jobs are, like, where is the work? How do we configure that work and call them jobs? And then those jobs then we look for people to yeah, our students are facing this now. Work changes and then the jobs change. What are we hiring into? So actually, the power is in the hands of the employees today who master this technology today and figure out the new ways of working.

JB: Right. New jobs will emerge that didn't exist before. And, of course, it's impossible to sort of say exactly what those jobs will be. But there will be job growth, and there's always going to be some dislocation. This is just the process of creative destruction, as we call it, whereby, you know, as some job activities go away, others come along in their place. So, for example, it's an easy bet that work in AI security and governance, and that whole area of putting guardrails around it, there's going to be a huge growth in jobs in that area. Whether we want it or not. Because we've unleashed this thing AI, which we don't know quite how it's going to work. And, at some point, we've got to put some much more explicit guardrails around it.

NH: Absolutely. Yeah.

*MUSICAL INTERLUDE*

JB: So we've had a great conversation, I think, about the process of technology change. And, as you said at the start, it's all about the people. Tell us how you teach this to our students before we close. 

NH: So, you know, I love working with students. And, I love working in the classroom to sort of, get people to describe experiences. How I teach it in the classroom depends on who who's in front of me. So if I'm with an executive audience, it's easy to get organizations and leaders to describe experiences where their a-ha moment came from, a failure or event where they were thinking purely about what the technology would do or not. And then it turned into a big people issue. In the undergraduate class, where people have less experience, I often start with where they are, which is more around the technology and the. How many of you have had to work in an organization where you had to get data from a bunch of different systems and put it into an Excel worksheet? Now, why would I start there? Because it makes it very real to them about why information systems - and information is broken in organizations - so why systems can have a role. So what are the informational demands of the strategy? Why do you have to pull that information into from different systems, into one. So we break down the complexity of why organizations are in the state that they're right. And then when we talk about implementation, I bring in the  human issues right at the start. I really try and get students to understand that this isn't a technological, but it's a human endeavor and involves technology. And you have to understand how the technology works. An enterprise system is a different beast than an AI. The enterprise system is very regimented and understanding what creates resistance there is different from a technology that where the worker themselves have to generate the value from it. So we would work through what are some of those major differences. And then, for most of them, they're going to leave and walk into a project management job. They're going to leave and walk into a technology change where they're going to be in the messy middle. I'm going to teach them how to pay attention to things that matter.

JB: Right. And they will actually understand better than many recent graduates what's coming their way. And that's the whole point. And I realize I'm preaching to the converted here, but the whole point of an Ivey education is that we're not just teaching the technical skills, we're teaching people to see how those technical skills actually sort of manifest and get sort of implemented in a human system. 

NH: And one of the things that I think I value a lot about our school in this on this front is if you look at the top business schools, maybe half of them have a course that is dedicated to teaching students about leveraging technology in a way that is managerial oriented. That includes both a little bit of the technical understanding of it. How does technology work? You don't have to build it. And frankly, in the future AI is probably going to do a lot of that. But you do have to understand how it operates, how value comes from it, and your role as a leader  in generating that value. So many business schools leave it within an operations function or as a sidebar to accounting. But, Ivey for a long time, since when I went to the school, invested in making sure every student left with an understanding of this topic and I think it's crucial. 

JB: Perfect. We will finish there. Thank you very much. 

NH: Thank you.

JB: You've been listening to Dialogue with Dean from Ivey Business School. My thanks to Nicole Haggerty for sharing her research and insights. And to you, our listeners, for being part of this important conversation. If you enjoyed today's episode, be sure to subscribe, share, and stay tuned for more conversations with Ivey faculty on 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 IveyImpact.ca or subscribe on your preferred podcast platform. Thanks for tuning in.