Thanks very much.
I want to echo the other panellists' comments about how pleased we are that the committee is looking at these issues and also that we have the opportunity to speak to you today. I will try to restrict my comments to some high-level issues that I would like you to keep in mind. I didn't have time to submit a brief, but I will provide a follow-up report that gives more detail. I think other witnesses have talked a lot about specific technologies and specific examples. There are some broader policy-related issues that I would also like to discuss.
There are a couple of important concepts that I think we have to reinforce because they really would go a long way to shaping policies that would address the innovation gap. It's very important in my mind to differentiate between entrepreneurship, which challenges the status quo and creates something new, and innovation, which requires the adoption and use of those new things in some way to transform systems, businesses, or consumer behaviour. You can have entrepreneurship and you can have lots of wonderful technologies, but if you don't pay attention to end-user needs, to organizational issues, and they're never used, you do not reap the benefits.
I would argue, in terms of the policy frameworks, in terms of where we invest research dollars, that there is quite appropriately a great deal of focus on developing technology, and there should be, but we need to put more focus on how to actually roll those technologies out effectively, whether it's a combination of tax credits, training, or some other things I'll discuss. I think that's critically important.
There's a second thing that I think is very important, and this comes from not just doing research but working very closely with business leaders, community organizations, and so forth. We have to think about impacts, even though it's very hard to predict what the impacts will be. Early in my career I worked with the Institute for the Future in Menlo Park, California, and I worked with Don Tapscott, who wrote all sorts of books, and I would say that they were good at anticipating change but probably no better than Star Trek. If you actually think about how the world has changed, some of the technologies you've been hearing about, whether it's mobile computing, additive manufacturing, or virtual and augmented reality, much of that was prefigured by science fiction. So I'm not saying that it's easy to anticipate what is going to happen, but we have to try.
McKinsey has done a really good study, I think, of disruptive technologies and tried to identify some of the potential impacts.
Again, disruptive technologies are not the same as advanced technologies. Not all advanced technologies are disruptive, and not all disruptive technologies are particularly complex. To be disruptive, the technology has to be applied to change fundamental business models. That's basically the definition. For example, I would argue that robotics, in and of itself, is not disruptive. Robotics has transformed the automotive industry for many years. Taking robotics and introducing it into the hospitality industry then becomes very disruptive because that's an industry that, up to now, has not used robotics. I think it's important to really keep a focus, not just on developing these technologies but thinking about how they will transform how we live, play, and work.
I also don't want to be alarmist, but I think we have to think, not just about the potential advantages but the potential disadvantages. The good news is, the recent study by Frey and Osborne, which looked at the impact of computing on jobs, did not suggest that politicians were an endangered species. However, they did say, through a detailed analysis, that 47% of the current jobs in the North American economy are potentially at risk. That's fundamental.
Of course, there are opportunities for new kinds of jobs as well, but we have to look at both sides of the equation and that has implication for policies, that has implications for how we invest, and that has implications for how we train and educate our students.
I want to read from a couple of things, and this is the first.
We have historically felt that low-skilled jobs might be vulnerable, they might be outsourced, that low-skilled auto workers, for example, were sort of the inevitable casualties of automation in the manufacturing sector. I think we will hear more about how that is, perhaps, a real misconception. But many have thought that highly educated knowledge workers weren't at risk. The Associated Press has announced that the majority of U.S. corporate earnings stories for their business news report will eventually be produced using automation technology. This is a major publisher.
To free journalists to spend more time on things like beat reporting and source development, they discovered that automation technology from a company called Automated Insights would allow them to automate short stories of 300 words to 500 words about the earnings of companies, and instead of providing 300 stories manually, they could provide up to 4,400 stories automatically for companies.
Diane Francis, the journalist, sent this article to me. It says people who think journalism is not in trouble need to give their heads a shake.
The second point that I think is critical is that research is the foundation of innovation, without question. We may not be getting the outcomes we would like from all of our research investments, and there are reasons for talking about that further, perhaps in the questions.
Ryerson, certainly, has invested heavily and has many of the technologies you've heard about—cloud and context-aware computing, advanced manufacturing, virtual reality, and so on, but again, those technologies are not in and of themselves disruptive. We have to look at their applications.
One of the ways Ryerson has done that is by challenging the traditional paradigm of lab to market. We completely support the importance of foundational research, but lab-to-market models, where you assume that scientists and researchers will develop things that will be commercialized, is a high-risk proposition if your objective is commercialization. We, instead, have focused on much more iterative market-driven models for research, which produce significant results that are tied to user and organization needs.
On a panel I was on, someone said recently that if you want to drive research, invest in research; if you want to drive commercialization, invest in commercialization. Right now the current models of funding university-based research reinforce the behaviours that we've heard don't necessarily drive innovation. They reward publishing articles. They do not reward patents, and they most certainly don't reward setting up small businesses. We have to think about the structures and how they align with what we say we want to achieve.
I would note that a lot of effort has gone into trying to turn professors into entrepreneurs. You've met some who have made that transition—Hossein Rahnama, who is from Ryerson, is an excellent example of a Ph.D. entrepreneur. However, lots of people become professors because they want to stay in their labs and they want to write papers. I say we should let them do that, but build the structures that will help identify the research that has commercialization potential and bring in the people who know how to do that and how to start companies and grow them. Right now the current structures don't necessarily support that.
Obviously, Ryerson is very committed to supporting the creation of start-ups. We have one of the leading incubators in Canada and, indeed, in the world. We have partnered with the Bombay Stock Exchange to set up an incubator in India to provide soft landings for Canadian entrepreneurs going out and Indian entrepreneurs coming in.
We've been very successful with a number of federal government funding programs that we're grateful for, from FedDev to CAIP, and so on, and we're partnering with groups like the Ontario Chamber of Commerce to help scale up existing businesses.
I think that one of the things we really have to come back to is an example from health care. It's another quote saying:
The future of medical computing is bright. Obstacles to the practical use of the computerized medical record exist.... We have a golden opportunity to avoid a new round of escalating medical costs.
Does anyone want to guess when that was written? It was written in 1990. We've had the technology we need to transform health care for the 25 years I've been in this industry, but it is the organizational and human factors that are a huge issue.
I just wanted to close—