Bridging the Growth Gap with AI Innovation

Speaker 1 [00:00:02] Hi, it’s John here. As you may have heard on our first episode, AI is going to be a theme right through the season. It’s transforming every sector and every part of the country, and we’re excited to learn where the opportunities are, as well as where the dangers may lie. And so we’re pleased to present this special bonus episode of Disruptors to you. It’s an edited recording of a recent session of the Business and Higher Education Roundtable hosted here in Toronto. The roundtable represents some of Canada’s biggest employers and leading educators and tackles some of the most pressing issues facing Canadian prosperity. The Roundtable’s recent annual meeting focused on AI and kicked off with a special presentation from Joel Blit, Associate Professor of Economics at the University of Waterloo. It’s called Navigating the AI Revolution. You’ll also hear highlights of a panel discussion with Dave McKay, RBC CEO, Anthony Viel, CEO of Deloitte Canada, and Mara Lederman, COO of Signal One, a leading AI start-up. The conversation, hosted by Valerie Walker, the roundtable’s CEO focused on several themes, including the hype around AI, workforce implications, the practicality of adoption, ethics and accuracy, and Canada’s challenges in becoming a global leader in AI applications, as well as AI research. Have a listen and let us know what you think.
Speaker 2 [00:01:32] Today, I want to talk to you about AI and how AI is going to basically change the economy, and even more so, going to change the world. But more importantly, I want to talk to you about what the opportunities are going to be for you, for your organization, and hopefully also for our country. So I’m going to start by talking about what I think is Canada’s grand challenge at this point in time. But then I’m going to start talking about what the big opportunity is to change the trajectory. Specifically, I’ll talk about AI but before I get there, I want to go back in history a little bit and talk about other general purpose technologies, because they’re going to give us some insights as to how AI itself is going to be developed, is going to develop over time, is going to be adopted, and the opportunities that it’s going to bring to us if we grab them. Okay, so that’s the lay of the land. Because this group really cares about jobs and skills, I’m going to have a few things to say about that at the end as well.
[2:23] Okay. So let’s start with Canada’s challenge. Obviously, the challenge that I’m speaking about is Canada’s innovation, productivity and growth gap. What I have here, I’m showing you Canada’s labour productivity and that of the other G7 countries over the last half century. Right. Or last 50 years. And specifically what I’m doing is I’ve normalized all of them at the year 1970 to be 100. And so I’m showing you the growth in productivity over the last 50 years. And what you can see is that Canada’s growth is dead last over this period. Now, there’s many reasons for that. The industrial mix, the fact that we’re not investing as much in technology, some would say that our managers are not as sophisticated as in the US, but by and large I would argue that it’s a lack of R&D and a lack of innovation. So the culprit, lack of business R&D, if you look at R&D intensity, what you see is that Canada again is dead last. It actually gets worse than that. This is R&D intensity by business, right? So government actually does a decent job, but it gets worse than that because we’re actually the only G7 country that has actually decreased their R&D intensity over the last 20 years. Now, it won’t surprise you then that we’re also dead last when it comes to innovation as measured by Triadic patents. We’re tied with Italy in this regard. And so if you put this together, of course, we’re not going to be doing all that well in terms of economic growth. And maybe the biggest thing that I worry about is the things that us Canadians we identify with, which is our public services. So things like our quality health care, public health, our quality public education, those are very things that are going to be at risk. We may not be able to afford them unless we change the trajectory. And so that’s fundamentally what we’re talking about. It’s almost an existential threat to how we identify as Canadians. All right. So bad news over.
[4:07] So there were several articles in The Economist last week saying how AI is going to accelerate science, scientific research, accelerate innovation. It’s going to help us solve climate change, is going to help cure disease, all of which I think are to a large extent true. But we also hear about the end of work and we also hear about an existential threat to humanity itself. So I am going to talk about AI in a second. As I said before that I want to go back into history and look at other general purpose technologies, because I think that they have a lot to tell us about how we can ground the opportunities of AI. So when I talk about general purpose technologies, as economists, what we mean is technologies like the steam engine, like electricity, like computers and the ICT revolution. And these technologies all have three things in common. Right? So the first is that they’re pervasive. And by that, I mean that they’re not just going to be in one sector of the economy or one industry. They’re not going to have just one application. They’re going to be broad based in a whole bunch of different areas. The second thing is that they undergo ongoing improvement. So it’s not a technology that is going to arrive and be fixed and therefore deployed. It’s constantly going to be improving, getting better and changing. And the last one, which is also really important, is, it is subject to innovation, complementarities. And what I mean by that is that these technologies are not just evolving themselves. There’s complementary technologies that are evolving as well, and there’s an interplay between them. So all of these characteristics imply, first of all, that general purpose technologies are going to be transformational. They’re going to have a huge impact on society. They’re going to change things. I’m sure all of you knew that before. But the other thing is that it usually takes decades to truly feel the impact. And so that’s the first lesson, right, is that it takes time. And so we shouldn’t expect too much in the short run. But the other lesson from history, I think is even more important is that the adoption of this technology is going to follow a fairly predetermined path. And so this I call the three phases of adoption or the three Rs.
[6:02] And so in the first phase, you have that the new technology is displacing old technology. But crucially, the processes, the business models, they all stay the same. It’s just that the technology is replacing the old technology in specific tasks where those technologies are being used. It’s the second phase that I call the reimagine phase, where the processes themselves, the business models, are fundamentally shaken up because they’re reimagined around the new technology, around the capabilities of new technology, where you see a lot of product even grew as you see the economy and society being shaken and things coming out the other side a lot different. The third phase of adoption, is recombine, we’re not going to focus so much on this one today, Right, because that is further in the future. But recombine is where the technology combines with other technologies to create entirely new technologies. So the example I want to use here is electricity. If you go to the late 1800s, you had these factories. They were all powered by the steam engine, right? And you had one big steam engine there and you had a lot of different stations in the factory. And each one of those stations had to be connected by a line shaft to the single power source. Now, if you fast forward a couple of decades now, we started getting the electric engine. But fundamentally, you can see that the factory layout is the exact same. Okay. Now, that brought some brighter improvements because the electric engine is a little bit cheaper to run, it’s more reliable, etc., but relatively minor. Now, it took another 20 years until people realize, hey, wait a second, With electricity, I can fundamentally reimagine the way that I structure my factory. And so specifically, you could now have at every workstation, a little electric engine. And that meant that now you could organize the factory floor in a much more logical way where the output from one station became the inputs of the next station and then the recombine phase. Of course, we know that electricity, combined with advanced materials, created transistors, semiconductor chips, telecommunications, etc. etc. In the interest of time, I won’t go into too many other examples, but at least let me talk about computers for a second, at least fairly quickly. Right. If we back up to 1987, computers have been around for a couple of decades, and yet they really weren’t having a macro impact. They were nowhere in the productivity statistics. It was known as the computers productivity paradox. It was the machine of the year, but yet there was no sort of macro impact. And the reason, of course, was that people were using it a little bit, but they were still stuck in phase one. So in the first phase, computers replaced human computers. Right. So the term computer is actually used to refer to people that sat at desks and did arithmetic all day. Right. And then we had electronic computers, obviously that replaced them, but they also replaced a lot of people in the back office, bookkeepers, clerks, etc., etc. Okay. But the business models, the way that firms are organized, really didn’t change. It was only until the second phase re-imagine where now we start to see the decentralization of corporations. So with computers you can maintain better control. We saw companies venturing abroad because again, with better communications you could now communicate over larger distances. And we started seeing new models like customer relationship management, where you can start organizing a business around customer segments, for example. Right. And of course the computer combined with our technology, you got the Internet, smartphones, etc..
[9:25] And now the question is how does this apply to AI? Let’s start with very high level and then we’re going to do a bit of a deep dive into each of these different phases. Okay. But at a very high level. Your job as an executive, as an organizational leader, is to move your organization as quickly and responsibly but as quickly as possible through these phases. Right. And so phase one, you need to be thinking, okay, how can I drive efficiencies? And this is going to result in things like cost cutting, increasing volume, increasing quality. But then at the same time as you’re doing that, you have to be thinking, okay, I know what’s coming down the line, eventually people are going to dream up of how can I do things fundamentally different using this technology, right? So phase two, the re-imagine phase. And as a leader, you need to be thinking along those lines as well. Maybe you need to be leading in being the organization that actually does it before someone else does it and disrupts you.
[10:26] As you’re doing all of this, of course, you need to have your eye on the horizon. You need to have a scan function that is actually say, okay, well, how is this technology going combined with other things, in this case AI, to create entirely new things, entirely new technologies that are going to, again, fundamentally, radically change everything. So let us talk about replace, in order to think about what you can actually replace and do more efficiently. You have to understand what the technology can actually do. And so what do large language models do? Well, they’re very good at writing. They’re very good at ideation, they’re very good at background research, coding, at data analysis, and to some extent also math. And the crazy thing is that this list seems to be expanding every other week. Right. And they seem to be doing all of these things better and better. So you want to engage in phase one, you want to engage in the replace phase. Well, the first thing to do is. All right, well, let me look at my workflows, let me look at my processes. Let me break those down into tasks and then let me see which of those tasks are things that the AI can do. If it’s a task that the AI can do, let me do a return on investment analysis. And then starting at the top of my ROI list, I’m just going to start knocking them off one by one by one and automate more of my process. Okay. I want to point out that it sounds like the AI is just coming in and you’re getting rid of people. You’re dumping in AI. Obviously it’s not going to be that simple. What is going to happen in most cases is that the person is going to start working with the AI, right? And so this is where some people make the distinction between labour augmenting versus labour displacing. Personally, I think that’s a bit of a false dichotomy because what’s going to happen is if it is labour augmenting, the person is going to be able to be twice as efficient, get twice as much done in an hour and less demand for whatever they’re doing increases two fold. You’re not going to need as many workers. And so even if it is purely human enhancing, which by the way, I think it mostly is, there are going to be some job losses and there really is no other way around it. Now, a lot of what I’m saying today is I want to get you guys out of the replace phase and really thinking about phase two, because that’s where the real changes are going to happen. But for AI, I want to point out that there actually begins to be had just from this phase alone, which is actually relatively easy in the grand scheme of things. And so this is some early evidence of what these gains that can be had are. Right. So there’s a paper that shows that in writing tasks, you can increase productivity so the speed of writing with the use of ChatGPT by 59%. In the area of programming, you can increase coding speed by 56%. In customer service, you can increase the number of cases resolved by 14%. And even in the health care sector, chat bots seem to be offering better responses to customer queries than doctors do. And here’s the kicker, their responses have also been judged to be a lot more empathetic. Yes. One other thing I want to mention here is, to a large extent, these technologies are helping the least skilled. So if you are a phenomenal writer, these technologies are not going to help you much. If you’re a relatively weak writer, they’re going to help you a lot. They level out the playing field. Okay. All right. So this is the replace phase. And now we get to the re-imagine phase. I know what you’re all hoping. You’re all hoping I’m going to tell you exactly how you can re-imagine your own corporation, your own organization. I can’t do that because you are the experts on your organization. I certainly have some thoughts across different industries. What I’m going to do instead, I’m going to give you some examples. If we go back to the last sort of big technology, I would argue it’s the internet. You might argue it’s mobile technology, but let’s go with the Internet. We saw three really big companies come out of that age, Right. How did they come out of nowhere to become really big companies? Well, if you look at the case of Amazon, what they did is they engaged in this re-imagined phase before anyone else. And if you look at Meta, they did the same thing when it came to social networks and how people interact with each other. Okay. Phase three, in phase three, again, I’m not going to talk very much about it, but these technologies have combined to create entirely new things. So, for example, A.I. Robotics, 5G and Iot are combined to create collaborative robots. These are going to be entities that have the cognitive capabilities and the flexible sort of physical capabilities and are going to be connected to the Internet. You’re going to have access to everything. They’re really going to change everything. Right? But of course, that’s a good ways off. All right. So let me just say a couple of things about the impact of jobs. I think this framework I led you guys through helps us to also think about what the impact on jobs are going to be. And specifically in the replace phase, it’s actually fairly predictable. Because all we need to do is we need to ask which jobs have. So we look at all the tasks that are involved in each of these different jobs. And some jobs are going to have the majority of their tasks being things that these technologies can do. Those jobs are going to be in trouble. Other jobs, not so much because these technologies, you still need humans in the loop for now. When you go to the reimagine phase, at that point, it’s really hard to say. So, for example, I could never have predicted that the Internet was going to get rid of retail jobs. It wasn’t clear there was no link there until Amazon fundamentally redefined the industry. It wasn’t like oh these, you know, the Internet, the kinds of tasks that these people know that that wasn’t the analysis at all. So it’s really hard at that point to see what’s going to happen. But I would say one thing as well about winners and losers and about skills. I think society has a tremendous opportunity for organizations, for a country. But we can’t forget that there’s going to be winners and losers. Not everyone is going to be affected equally. And so I think it’s important for us to keep that in mind and make sure we have in place the kinds of programs that can help everyone. So who are going to be the winners and losers? Well, it basically depends whether your skills are complementary to AI or whether AI is going to replace those skills, whether it can do those skills. So what are some of the complementary skills? Things like and entrepreneurial skills. If you’re an entrepreneur and you have ideas, you are now super empowered. You can use these technologies to implement all kinds of things quickly. On the other hand, things like writing, background research, reading and programming, those are going to be competing with AI. So that’s all I want to say for that. In the interest of time, I’m just going to conclude and share with you what I think are some of the key takeaways. I think we really are at an inflection point here where we have a technology that’s going to transform our economy and our society. And for all of you that are leading organizations, it’s an opportunity to increase efficiencies, in other words, to, you know, focus on phase one. But you should really see that only as a tip of the iceberg. You really need to be thinking about phase two. In other words, there’s a once in a generation, maybe once in a lifetime even, opportunity to really transform your business. And if you’re really ambitious, actually your industry. And the question is, who’s going to do it? Right now, I started to talk by saying in Canada, we have a real problem here. We need to address it. We need to change the trajectory. But to do that we’re going to need our small and medium and large organizations to engage in these first two phases. And of course, that’s not going to be easy because they’re running all the time and don’t have the capacity. They might not, not know about AI. I think there’s a big role here for higher ed and for government to get involved, make sure that happens. My dream is there’s going to be the next Amazon, Google or Meta of the AI age is going to be coming out at some point and I want them to be Canadian, at least one of them to be Canadian. Okay, so one parting note. And I think this is especially relevant for higher education institutions. We really have not changed much in the way we do things in many, many years. If you’re not willing to be a disruptor, then you better be prepared to be disrupted. So I’ll stop there. Thanks so much. [18:31] Speaker 3 [00:18:39] Felt like Joel kind of threw down the gauntlet there for the post-secondary folks in the room. That’s amazing. Joel, thank you. Thank you. I’m really pleased now to invite our esteemed panellists to stage. You already know Dave McKay. Dave BHER’s biggest champion, Anthony Viel, who goes by AV. Here, he’s the CEO of Deloitte Canada. Deloitte just put out two days ago a big report on the impact and opportunities of Canada’s AI ecosystem. And last but certainly not least, Mara Lederman. Mara is the chief operating officer and co-founder of Signal One, and prior to Signal one, Mara was a professor at the University of Toronto Rotman School of Business and was one of the leads of the Toronto site for the Creative Destruction Lab. I am so thrilled to have you up here. I could just maybe get everyone to give them a real round of applause to get them excited about talking. And I think my first question and I’m going to throw it open to all of you is just to like, what did you think about what Joel said? How are you feeling now?
Speaker 4 [00:19:40] So it was a great presentation. And, you know, I think Joel brought to life everything that economics has been thinking about, you know, not just in terms of A.I., but really drawing on the history of what have we seen as the patterns from technology innovations in the past. If you had asked me a year and a half ago, I would have said, I absolutely agree with everything Joel said. Now I would qualify that. I absolutely agree that the field of economics says everything that Joel told you that it said. And when, as Joel said, the returns to the replace phase are kind of small, they’re not nothing, but they’re not as big as the transform phase. It’s like, why am I going to do all that work for something that might be small? So then you’re like, Well, I can give you this big idea. Transforming. I’m definitely not doing that work. And so I think that’s probably, you know, the what I can offer is there are so much under the hood there that makes this so, so very hard, even when all of the intentions and the incentives are in the right place.
Speaker 3 [20:37] AV that’s a good segway to you. You can speak to it.
Speaker 5 [00:20:38] I was inspired and enthusiastic about what Joel was talking about and why I am. I believe that the reimagine phase is going to create jobs. We digitized all the analogue and we did that for a good 15 years for returns, good returns, good efficiencies for sure. We haven’t really grasped that re-imagine phase yet with digital, let alone AI, but digital and the opportunities it presents itself. So it’s going to take a long time. I think the message that you had in your presentation don’t be lulled into inaction, and that’s our message at Deloitte.
Speaker 3 [00:21:50] Deloitte put out a report just a couple of days ago. What were the main themes, the key takeaways for you that came out of that assessment of Canada’s AI ecosystems?
Speaker 5 [00:21:58] I think that the headline statement is 86% of Canadian organizations are worried about the risks of privacy, confidentiality, biases, poor quality results. They’re all legit and they’re all in this first phase. I’m not talking about iRobot taking over the world and we become enslavement to technology. I think that’s something we can talk about in the decades to come. But immediately, you know, the privacy part is legitimate concern on that. The poor quality results, the hallucinations, but also the model deterioration. The ChatGPT4 in some tasks is worse than ChatGPT 1. And nobody can explain why. So there’s a legitimate, legitimate concerns that I think back up that 86% of respondents are worried about the risks.
Speaker 3 [00:22:24] We have three executives from companies up here. My question is, you know, we’ve got a room full of companies and post-secondaries. When you’re thinking about some of these barriers, if it’s still adoption around digital tech or adoption or at least understanding of the potential of AI, generative AI, in particular. When and how do you think about post secondaries and how you would want to work with them now? How have you worked with them? Where do you see that relationship going?
Speaker 4 [00:22:50] So I can start I mean, I think there are such an incredible roles, our partnership for partnerships between post-secondary and between enterprise. I think I would have said in the past that post-secondary does the research somewhere in the middle we have programs like CDL to help with the commercialization and then we sell it to large enterprises. That’s too small a view. There is this huge part I’ve learnt in the middle that goes from the research to the development. I think where that development part which is so, so much part of the commercialization has to be done in partnership with potential customers and that’s something I didn’t even appreciate what I saw all these startups from CDL and say, okay, the next eight weeks get one kind of a customer. But what we realize is like, you can’t build a product for a technology product for your customer just as the technology company. You need to be almost embedded and working closely with your customer to understand all of your blind spots to what practically needs, you know, to work to make it work. So I think that’s one form of partnership that so far we need to bring more enterprises. We do this at our company. With our first two hospitals we’re design partners and that meant something very specific. We knew right from the outset, you’re getting something that’s unproven. Right? Now, you’re getting some great deals in terms of that. But here’s all the stuff you’re going to help us with, and they are effectively co-designing the technology with us. So I think that’s kind of on the development side, so much opportunity. The other thing I learned is on these areas, like the risks you talked about, we need the research on the cutting edge, thinking aright around algorithmic bias, how do we regulate these things? But even that needs to be paired with doing it in practice, right? So much of what we do around model validation, retraining, monitoring borrowed from what my co-founder did when he built a AI inside of one of the other leading banks. He never would have figured that out. You know, in graduate school, it was the need to put it into practice in a in a regulated industry, in an organization with very strong risk management practices, that they’re like, okay, how do we figure out to take what the computer scientists have taught us and turn it actually into technology that can do this? So I think that’s the second place where higher education and enterprise really need to work hand in hand, specifically around A.I.
Speaker 3 [25:09] Dave I throw that same question to you and the relationship and partnership potential that exists here.
Speaker 6 [00:25:16] One of those use cases we’ve already started to re-imagine, and this is where we need university help, and that is the coding one — that you pointed out. So we’ve already challenged ourselves to solve a major problem in our company, and that is we’ve lost control of our tech stack to third party software providers. So, our big idea is if I can code software 75 to 80% cheaper than I did before, then I’m going to get back in the game that I was doing 25 years ago of coding my own software. That’s the reimagine. And I want it and I’ll fund it. So you can see the disruption. And every other CEO of every other major company wants the same thing. But controlling the code is going to be really critical. So what do we do? We’ll bring more co-op students in and get us to 75% ChatGPT code. Then I need a systems design person to architect the system and then we’ll code it ourselves really efficiently — and I don’t have to pay that economic rent. Billions and billions of dollars a year. So that re-imagine, I think it is that two-to-three-year horizon as well. Maybe five, maybe ten. But we’ll start on it now. Anyway, that’s an example of how exciting this technology is and kind of rethinking the world and why we’re going to need students who have broken that bias to rethink the world. [90.6s] Speaker 3 [00:26:46] So you’ve heard it here first, folks. Dave’s getting back into coding, so that’s sort of my takeaway.
Speaker 6 [00:26:55] [00:27:35]Always a coder. Started a coder, will finish as a coder — full circle. [4.6s]
Speaker 3 [00:26:59] AV, I saw you nodding and taking some notes. I’m going to ask you to build on that and then I’m giving you guys a heads up. I’m coming out to the audience. This is where we pivot into the panel and audience conversation. [00:27:50]
Speaker 5 [00:27:10] I think Dave’s point, augmenting human and technology is a good strategy and you should continue to do that develop through your programs. The folks that are not scared of technology embrace technology.
[27:25] And if I could pick up just my point earlier as well as is, I get really nervous when people in Deloitte start innovating too far away from the end user, the client, the patient, what have you. I think we shouldn’t forget that. And purple people are the ones that can get to the to the crux of that. But I agree with Dave too, that white collar productivity has been terrible for 40, 50 years. Terrible. And this is an opportunity and where we can get some productivity and white collar and I think it’s continued around this area, leveraging and augmenting technology that’s going to be able to do that.
Audience [28:05] In the field of of AI, you know, because we’re talking a lot about how we marry the education to ensure that we’re ready to actually tackle that. I know we’re talking a lot about college and university, but I actually challenge that. I think we need to start we need to start in the schools with Gen AI right now, like when I think about when I was developing code 30 years ago, it was horrendous. It was like, you know. See And was not the most easy thing to do right now. Now you can start to teach kids from school to actually prompt code so they can create code. If we wait for college and university, we’re coming too late. The other thing I find that we have to think about is, is AI, where we’re going to focus. So when I look at other countries, you know, just quick examples. If I look at India, they decided long time ago they were going to focus on IT and there was government grants, there was like the whole thing channel on how they will become the best country to actually have IT and they did it. So is AI our focus? And if that is our focus, what are we going to do to ensure that, as India did, what it or many other countries very decisively shows what they’re going to be good at? We’re going to get to it because we’re going to have to start in every different angle to get to. So I’m not even convinced yet that as a country, we’re very clear on that yet. We want to be good at so many things, but you cannot be good at everything.
Speaker 5 [00:29:32] One of the outcomes of our research, if I may, we found that one in ten of the leading gen AI researchers reside in Canada today, and we’ve grown that talent around those luminaries, if you will, faster than any other G-7. Now, whether we deploy that, whether we keep them in-country, whether we through incentives and otherwise, I think is over to you said, you know, government’s got a role to play here as well to do that. How do we make Canada an attractive proposition for those talents?
Audience [00:30:05] Indeed, it was used to add something quick, like one of the things that is a little bit painful is a lot of the fathers of AI are Canadian. How many big companies do we have in Canada right now at a global stage? Zero, which is really painful.
Speaker 6 [00:30:25] A question for the educators. I think it’s really important, are you flipping your educational methodology on its head and starting with the output from the machine, then asking your students to validate it and the research and the learning is how do you prove true or false, and what’s the quality of that answer? I mean, is that something that you’re talking about and thinking about?
Audience [00:30:52] Yes, absolutely. I just came back from a week in Sydney at the Times Higher Education World Academic Summit, where in fact, much of the agenda was devoted to this question of how we prepare our students for a world, a working world in which gen AI is going to be a routine tool that they’ll be using. The issue of prompt engineering came up, and I think it was agreed that we could probably come up with a better label, maybe prompt design or prompt creation, because, you know, it speaks exactly with the skill set that’s actually going to be required in order to do this well. So I wanted to come back to a wonderful case study. You know, your experience and the difficulty that you’re encountering and you know, you emphasize the importance of connecting technology users and technology producers together in a kind of co-creation process, which we’re not very used to doing in this country. You know, the undeniable kind of sorry track record that we have in this country of business enterprises being very slow to adopt technologies that would enhance their productivity, I would say also slow to embrace a training agenda, which is a complementary input that one requires in order to make use of new technologies so effectively. So, chronically, we have underinvested in training and we’ve also seen businesses undervaluing and the importance of investments in the fixed capital that is at the cutting edge. And so, you know, the question it raises is how do we change the demand side of this, this process? How do we get more enterprises to do what RBC is doing and help them to understand not just the opportunities but the threats? Because if they don’t recognize the importance of making these kinds of investments, they’re not going to survive very long. So I welcome comments from all of you on that particular issue and just say that this session today has done a fantastic job of zooming in on, I think the number one challenge that the Canadian economy is going to be facing in the next little while.
Speaker 4 [00:33:07] So I think the first thing is nothing, nothing like a crisis gets people to do stuff. And I would guess Vivek and others might know this better. There was probably incredible collaboration between academia and health care systems during COVID, and ideas probably went from research to implementation sort of faster than has ever happened before, right? So it is certainly possible when it is necessary. So then that asks the question, well, why isn’t it happening the rest of the time? And I have a few ideas on it. I think on the demand side, I think personally I think we have a lot of industries that aren’t sufficiently competitive and we’re not that big a market. I mean, if we had a ton of, you know, have tons of regional banks, right? We don’t have a ton of banks. We don’t have a ton of airlines that we could count them on less than one hand. And so I think obviously competition is a threat to keep getting better. And there are some industries I think don’t have enough competition. We don’t have probably as much competition for talent in Canada as we should. Our MBA students overwhelmingly wanted to stay in Canada. Now that is great for the Canadian employers and a huge fraction of them went to the big banks. And I love that you hire our students, but I wish you hired them away from them wanting to go to the US. And then it’s hard for all the reasons people here have said it’s hard absorptive capacity training. People are busy, right? They don’t have a lot of time on middle of doing their jobs to do things. And now the last point I’ve spoken too much. Just coming back to your point about transformation, I can’t think of an example where the true transformation around a new technology, now I could be wrong, came from an incumbent. So what are the ones where we think of new technologies transform business. Amazon transformed retail; start-up. Uber transformed transportation; start-up. Netflix transformed entertainment. It wasn’t Blockbuster, all of a sudden say, we’re going to close every store, right? And so maybe, Joel’s probably thought about whether there is or there isn’t. But part of why this is hard, you know, I want to transform an emergency department. That’s not going to happen for 20 years. Amazon’s going to be more likely to do that than, you know, my technology company with some incumbent. Could you imagine if, like you said, one day we’re closing every one of our branches, That could be a transfer.
Speaker 6 [00:35:26] [00:41:07] We’re disrupting ourselves.[0.3s]
Speaker 4 [00:35:28] You’re disrupting yourselves. But on the scale, on the examples that Joel brought.
Speaker 6 [00:] [00:35:32]Disruption is slower. [1.0s]
Speaker 4 [00:35:35] The scale, the impact on the humans that you kind of care about as a leader, it’s very hard to do that. And I think that’s why it’s so hard to get to that transforming.
Speaker 3 [00:35:44] This has been fantastic. A huge, huge thank you to the panel. Can I just get everyone to give our panel a round of applause.
Speaker 1 [00:35:56] That was an edited recording of the annual meeting of the Business and Higher Education Roundtable held in Toronto on October 5th. And this is Disruptors, an RBC podcast. I’m John Stackhouse. Talk to you soon.
Speaker 3 [00:36:15] Disruptors and RBC Podcast is created by the RBC Thought Leadership Group and does not constitute a recommendation for any organization, product or service. For more Disruptors content, visit RBC dot com site Disruptors and leave us a five star rating. If you like our show.

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