Canadian AI Tech: From R&D to Commercialization
- Canada is a research and talent leader in the AI space. However, Canada must put greater emphasis on the commercialization and adoption of AI technologies, rather than focusing solely on R&D.
- Canada needs to generate AI-related intellectual property that can be commercialized by high-growth Canadian firms to enhance the application of AI in our businesses and government.
- Industry-academic collaboration is essential to drive adoption of Canadian AI technologies within our local SMEs, which will increase their productivity, revenue streams and overall competitiveness.
Government and business leaders need to strengthen and accelerate global competitiveness in AI. We need financing, a strong digital infrastructure, and high-quality data to support vibrant innovation ecosystems that will support AI start-ups and enable the commercialization of new technologies.
Are Canadians world leaders in the AI sector? What must we do to increase our global competitiveness in this crucial sector for the future economy?
Canada has been an early research and talent leader in the artificial intelligence (AI) space. We’ve been fortunate to stake out a prominent place at the forefront of machine learning research, largely because of the work of people like Yoshua Bengio of Université de Montréal, Geoffrey Hinton of the University of Toronto, and Richard Sutton of the University of Alberta. These pioneers have nurtured Canada’s AI industry by nurturing talent in Montreal, Toronto and Edmonton. As a result of their efforts, Montreal has one of the highest concentrations of researchers and students of deep learning in the world, while Toronto has one of the highest concentrations of AI start-ups in the world. We are a hotspot for big data analytics and machine learning, attracting significant investments by such global heavyweights as Google, Microsoft, Facebook, and Uber. With these companies announcing millions of dollars in investments in AI research hubs across Ontario, Quebec and Alberta, Canada is recognized as having the research strengths, the talent pool and the start-ups to be a global leader.
“Canada’s policy efforts in AI are incredibly encouraging but tend to focus on existing areas of strength, such as research and talent, while not adequately addressing gaps, such as the commercialization and adoption of our AI technologies.”
On the flipside, according to Deloitte Omnia AI’s report Canada’s AI Imperative: Start, Scale, Succeed, not enough Canadian businesses are investing in AI and when they do invest, most struggle to move past experimenting with AI and actually unlocking its true value. Canada’s policy efforts in AI are incredibly encouraging but tend to focus on existing areas of strength, such as research and talent, while not adequately addressing gaps, such as the commercialization and adoption of our AI technologies.
For Canada to be a global leader in AI we have to be able to maximize the commercial impact of our domestic AI research. And if we want to lead globally, we have to lead economically. In order to lead economically, we have to be able to realize the economic benefits of the AI research and AI intellectual property (IP) that is developed in Canada.
If you look at the large tech giants like the Googles and Facebooks of the world, they are leading in AI and translating that into economic impact for the United States. The United States is dominating the sector. Canada needs to look at our promising AI start-ups and scale them into tech giants if we want to do the same.
Since the release of the Pan-Canadian Artificial Intelligence Strategy, how have you seen the conversation around AI evolve?
The Pan-Canadian Artificial Intelligence Strategy, which was announced in 2017, has doubled down on research and talent. Although this is very important, it alone is not enough to maintain our position as a global leader in AI in the long term. It is important to increase the number of AI researchers and graduates, and to establish nodes of scientific excellence in Edmonton, Toronto and Montreal. But unless we move beyond a focus solely on AI research, Canada might end up providing research and AI talent that drives other countries’ growth, without realizing the economic benefits locally.
“Unless we move beyond a focus solely on AI research, Canada might end up providing research and AI talent that drives other countries’ growth, without realizing the economic benefits locally.”
This is where the narrative has started to shift. Canada is on a shortlist of global AI leaders thanks to our research institutions, our tech hubs supported by industry, and our government funding. However, the conversation is starting to evolve on two fronts: commercialization and application. Canada needs to commercialize AI by supporting more than research. We need to start producing AI intellectual property (IP) that can be commercialized by high-growth Canadian companies. We also need to develop leadership in the application of AI in areas such as connected and autonomous vehicles, healthcare, finance, and supply chain management, which has an associated supercluster.
What tangible actions must Canadian governments, businesses and academia take to support the development of our AI sector?
Government and Canadian business leaders need to support the commercialization and adoption of AI. In other words, start buying from Canadian AI start-ups. The most powerful thing that government can do is start being a customer of Canadian AI technologies. Similarly, Canadian business leaders should start buying from Canadian AI start-ups and move from experimenting with AI to implementing and adopting it into their business models. Furthermore, increased investments that help accelerate the commercialization of AI technologies and the adoption of those technologies in Canada, at the very early stages where the risk is highest, warrant government co-investment with industry.
Academia should continue its pursuit of world-class AI research and the promotion of AI technology and development. But in order to drive adoption and commercialization of new technologies, it should increase collaboration opportunities with industry through industry-academic collaborative research and development (R&D) projects and collaborative technology development projects. Academia should start engaging more on industry-driven research challenges and collaboration opportunities.
There are some very key AI opportunities domestically. Canada has one of the leading financial sectors globally and there is a huge opportunity for AI in fintech. There are also many opportunities for AI in healthcare. So, if we can get governments, Canadian companies and banks to start buying AI-enabled technologies from Canadian start-ups, this will provide us with a competitive advantage. Other sectors including the resource sector–mining and energy–offer strong AI opportunities. That being said, the global marketplace is also very important.
“The most powerful thing that government can do is start being a customer of Canadian AI technologies. Similarly, Canadian business leaders should start buying from Canadian AI start-ups and move from experimenting with AI to implementing and adopting it into their business models.”
If I had to summarize the key steps government and business leaders must take to strengthen and accelerate Canadian global competitiveness in AI, I would emphasize the following aspects. Firstly, we need to accelerate the commercialization and adoption of AI technologies by Canadian firms, and we need to double down on investments and talent. This should not be limited to AI research talent but must also include AI tech and application talent. Secondly, we need strong digital infrastructure, such as cloud and the internet of things (IoT), to help drive the adoption of AI by our businesses. Thirdly, we need healthy pools of capital looking to finance all the way from start-up to scale-up, and this must be both public and private capital. Fourthly, we need high-quality data because without data it will become increasingly difficult to be a global leader in AI. Finally, we need vibrant innovation ecosystems across the country to support AI start-ups that are commercializing new AI technologies.
In terms of commercializing AI, what is Canada doing right and what are the most immediate challenges we have to address?
In terms of what we are doing right, I can speak mostly from an Ontario perspective since that is where OCE is focusing our efforts. We have a very robust innovation ecosystem that brings together academic institutions, regional innovation centers and businesses, all the way from start-ups to scale-ups, to large multinational enterprises that are making investments here. The accelerators and incubators – MaRS Discovery District, Communitech, Invest Ottawa, VentureLab, and so on – contribute to a very robust, broad, and deep innovation ecosystem in Ontario. This is important because in order to have tech start-ups, and specifically AI start-ups, grow and prosper, we need a vibrant innovation ecosystem, and that’s something we’ve done right.
In order to drive the successful commercialization of AI technology and the adoption of those technologies, you need to connect supply and demand. The supply piece is in the innovative AI start-ups. The AI talent that is coming out of the academic institutions supports that supply. The demand is from businesses and companies, both large and small, that act as receptors for the talent and accelerators for their technologies. So, the main need is connecting supply and demand across the AI ecosystem.
“If we are looking at attracting large tech giants to set up R&D operations in Canada, it needs to be on the condition that there be a benefit to local SMEs.”
Connecting big and small firms is important. If we are looking at attracting large tech giants to set up R&D operations in Canada, it needs to be on the condition that there be a benefit to local SMEs.We must therefore focus on initiatives that have local SMEs engage with these large companies. In many cases, that engagement can lead to Canadian SMEs being able to access the global supply chains of these large firms, which really does provide a very significant economic benefit to local firms. We need to bring SMEs to the table and establish a platform for collaboration where they are engaged and they are benefitting so that the benefits are not just limited to the large firms.
What are the challenges our SMEs face when looking to integrate AI into their operations?
There are a number of challenges. The first challenge has to do with the fact that SMEs “do not know what they do not know”. There is a lack of understanding among SMEs and businesses, and arguably also at this point in time probably also among consumers, about what the benefits of AI are and what the technology’s potential applications are. In addition to this, there is significant competition for AI tech talent. This is not necessarily just AI research talent but if you are an SME and you want to adopt an AI-based technology into your business to make it more productive or to introduce a new revenue stream, you need to have people that are well versed in AI to be able to do that. Right now, there is extremely fierce competition for AI talent and this is impacting SMEs’ ability to integrate AI.
“Right now, there is extremely fierce competition for AI talent and this is impacting SMEs’ ability to integrate AI.”
We must therefore connect our local colleges and universities with SMEs. This is absolutely necessary because having that type of industry-academia collaboration leads to many benefits. One is that it provides a bridge for talent. It provides students in colleges and universities with interesting training opportunities whereby they get to work on industry challenges. It also provides local industry, SMEs specifically, with access and exposure to AI talent that they may not necessarily have had otherwise, which is absolutely critical.
This is not an easy process and it does not happen naturally – there needs to be a concerted effort to do so. Many local SMEs have a tough time navigating the complexities of universities and colleges, but colleges and universities are doing a lot of great work in terms of industry outreach. OCE does a lot of work with them to bring in new industry partners and help facilitate and support collaborative R&D projects, as well as training opportunities for students. This is because the benefit to SMEs leads to a real economic benefit because it translates into talent, recruitment, increased sales and increased productivity in the SMEs that participate. We need more of these types of industry-academic collaborations to drive Canada’s competitiveness in AI.