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Executive Summary
As the AI ecosystem in Canada continues to mature, consolidation and churn are showing which AI strategies are successful. In the latest edition of our annual map of the AI ecosystem in Canada, we see that the number of net-new companies is falling, startup funding is switching to later in the lifecycle and AI solutions providers are taking a rapidly increasing share of the overall enterprise solutions market.
While Canada continued to add new AI startups in 2018, the growth rate slowed significantly. The previous two years saw AI startup growth defy gravity amid a larger slowdown in new company generation across the tech industry; and, 2018 is when the industry finally came back to Earth. Yet funding remained strong, as 2018 was the strongest year to date for AI funding in Canada with an estimated US$660 million raised across 98 rounds. Early-round funding such as seed and Series A declined as a share of total funding, even as the overall numbers rose.
Businesses big and small are trying to figure out how AI can best boost their bottom line. Corporate investment continues to grow, and the number of large organizations with AI research labs in Canada is now above 70. Our research shows that AI adoption is rising, increasing as a share of the overall enterprise solutions market by more than 50% in 2018. And there is still much room to grow: revenue from AI solutions represents only 5% of the total enterprise solutions market.
Talent remains a major concern for the AI ecosystem, in Canada and around the world. Our 2019 Global AI Talent Report showed that Canada remains in the top five for the number of high-impact AI researchers and in the top five for AI-related job openings. The number of AI scientists is growing, yet there remains a global AI talent shortage. For AI startups, talent is a serious barrier.
The slowdown in startup growth can be seen in most other comparable economies, in both the AI sector and the broader tech/startup sector. AI startups simply resisted the slowdown longer. When it comes to talent, Canada remains a top player in a world where demand is growing faster than supply. As AI startups continue to grow, even with the total number slowing, and with increasing recruitment efforts by the big players emerging in the ecosystem locally and arriving from abroad, it is unclear if the current AI talent pool can sustain the current rate of growth in the AI ecosystem without continued effort to develop new experts.
Key Statistics
- The number of AI startups in Canada grew 5% in 2018 vs 28% the year before
- Revenue for AI enterprise solutions companies in Canada rose 65% from the year before, and have grown to represent nearly 5% of the enterprise solutions market revenue overall
- US$660 million in 98 investments across Canada, up from US$288 million in 58 deals the year before
- Canada is in the top 5 for the number of high-impact researchers, and talent remains scarce
The Canadian AI Startup Ecosystem
The number of new AI companies in Canada has grown over the past few years, even as the broader tech industry has seen a worldwide slowdown in new company generation. 2018 was the year in which AI startup growth began to follow the trends of the industry as a whole. Overall growth dropped to 5% in 2018, versus 28% in 2017, bringing the total number to more than 680.
The 2019 numbers will be available in next year’s report. We define Canadian AI companies as independent companies that use AI as a core component for their product and have their headquarters in Canada.
The major factor in this slowdown in growth for the total number of AI startups is a rising attrition rate for those developed early in the current AI market cycle, while the birth rate of new AI startups has remained steady. If we date the beginning of the current AI boom to around 2011–2012, we can see how the current crop of startups is nearing a significant inflection point. According to the Startup Genome project, around 90% of startups will close within the first five years, and we are at or past that point for many new companies created during the first years of the current boom. The natural attrition and challenges of building a startup, especially the question of product/market fit, are major barriers, as is recruiting talent in a competitive market.
This chart shows the relationship between birth and death rates of startups. In the Canadian AI ecosystem, we are near the point where the growth of the birth rate slows as the death rate rises, yet growth rates are still positive. As the high-growth period transitions to a more mature, stable growth, attrition will naturally rise. Given the potential for AI and the ongoing interest in the sector, we can forecast a period of slow growth amid continuing efforts from government, investors and others to expand the ecosystem. 
Though we did not track specific numbers, we’ve observed a number of startups that have pivoted out of AI or have stripped AI-related claims from their marketing material. Based on our continued monitoring of the AI ecosystem, we believe those startups pivoting out of AI are responding to two observable drivers: the lack of AI talent and a lack of product/market fit, specifically around data preparedness. We will discuss the talent issue in depth further in this report, drawing from our 2019 Global AI Talent Report.
As for product/market fit, we believe at least part of the challenge of addressed market needs is due to a lack of preparedness by clients. Many companies have yet to take advantage of the opportunities provided by AI. According to a New Vantage Partners survey of Fortune 1000 executives, more than half of companies say that they are not yet “treating data as a business asset” and “are not competing on data and analytics.”
Talent and data preparedness are two factors of the AI ecosystem that are easiest to measure, but there are other significant challenges and opportunities affecting AI startups. Others, such as the AI hype, the gap between expectations and reality for AI applications and its impact on the market, can hardly be 
tracked using a single metric. There are other, more nuanced issues that also come into play: data regulation and agreements around data rights, competition from the big tech companies that offer many interlinked digital services, limitations in current technology, and trust and explainability in AI. In sum, this is still a period of discovery for the market, working to figure out how to make AI work.
Enterprise Investments and the AI Solutions Market
Canada remains a destination for international organizations looking to build their AI efforts. We estimate that around 70 large organizations had a research lab or other AI-focused group in Canada in 2018, up from 50 in the previous year. In the context of this mapping effort, most of our capacity to know about private AI labs depends on public announcements, and as more companies are embracing AI and setting up internal labs, this number could undercount private investments or others for which there are no public announcements. As more private labs are established, the number will become less accurate.
Even if growth in new AI startups is slowing, our market intelligence research shows that the overall market for AI solutions is accelerating. In 2018, revenue for AI enterprise solutions companies in Canada rose 65% from the year before, and have grown to represent nearly 5% of the enterprise solutions market revenue overall (including software, hardware, cloud and other services). That’s up from less than 2% in 2015.
AI Investments 
Though AI startup growth slowed in 2018, the number of funding deals continued to expand. 2018 was the strongest year to date for AI funding in Canada, with an estimated US$660 million raised across 98 rounds according to startup-tracking website Tracxn.com. What changed was the maturity of the companies being funded: early-round funding such as seed or Series A declined as a share of total funding, even as the overall numbers rose.
Seven of the ten most active investors in 2018 are based in Canada, continuing last years’ trend of investment coming predominantly from within Canada as opposed to international sources. The top ten sources were Real Ventures, BDC, Panache Ventures, 500 Startups Canada, MaRS IAF, TandemLaunch, Plug and Play tech, Techstars, Innovacorp, and Inovia Capital.
When we look at investments over time, we can see the maturation trend more clearly. 2014 to 2017 was a story of acceleration, with early-stage deals taking more of the overall share. During 2018, while the absolute number of early-stage deals has stayed the same as in 2017, the overall weight of these deals decreased significantly. Startups are attracting investment and attention later in their life cycles, a key indicator of a maturing market.
Talent
AI talent is in high demand around the world, and Canada remains in the top five for the number of high-impact AI researchers.
In our 2019 Global AI Talent Report, we reviewed the authors and publications from 21 leading AI conferences and integrated data from other online sources to develop an overview of the global AI talent pool. Canada is in the top five for both supply, the number of high-impact researchers (as determined by citations), and demand, in terms of job openings seen on Indeed.com.
The AI talent pool is highly mobile, with about one-third of researchers working for an employer in a country that was different from the country where they received their Ph.D. Canada is a destination for global talent, attracting workers who were trained abroad, and the overall number of researchers is growing. Yet Canada isn’t retaining talent, as the country sees more outflow of post-graduates than the average of the 18 leading countries for AI research. See our 2019 Global AI Talent Report for more, including methodology on how we determined high-impact researchers and other international comparisons.
We see no sign of the AI talent shortage reported in our previous reports resolving itself, despite new investments such as new AI-focused university programs as well as online courses and other retraining solutions aimed at helping mid-career workers reorient towards AI. Incumbents and large companies have attempted to solve the problem by increasing salaries or providing other methods for attracting AI talent such as workplace perks and extended employee benefits. For startups, those options are not always practical. We believe a lack of capable AI talent is a serious challenge for all companies in the AI ecosystem, and it is particularly acute for startups.
Government
The Canadian government has been a key supporter of the country’s AI ecosystem, providing support that included investment and research incentives. The United Nations credits Canada as the first country in the world to adopt a national AI strategy, dating to the $125-million investment in AI announced in March 2017, and federal and provincial governments have continued to unveil new support for AI research and commercialization. Other countries have followed suit, including the United States, the United Kingdom, and China, which have now all announced programs aimed at developing homegrown AI industries.
As AI is starting to impact more and more of the economy, it becomes important to not only understand the funding coming from governments but also the different ways governments act as a structural actor within the marketplace. In Canada, as we have noted in previous reports, the federal government has been a central player in organizing the pipeline between the academic world and the AI industry.
In 2018, the Government of Canada announced new initiatives on AI regulation, including algorithmic accountability and data protection. The Directive on Automated Decision-Making and an Algorithmic Impact Assessment (AIA) incorporate principles of administrative law and fundamental justice such as transparency, procedural fairness, the right to an explanation, and due process. A version of this Directive and accompanying AIA could inform policies for private sector deployment of AI. By leading the way on policy, Canada can continue its differentiation as the home of multi-stakeholder cutting-edge AI research.
Canada in Context
When it comes to talent, Canada remains a top player in a world where demand is growing faster than supply. As AI startups continue to grow, even with the total number slowing, and with increasing recruitment efforts by the big players emerging in the ecosystem locally and arriving from abroad, it is unclear if the current AI talent pool can sustain the current rate of growth in the AI ecosystem without continued effort to develop new experts.
Our data clearly shows how the Canadian AI market is maturing. The growth rate of net-new AI startups is slowing down, and natural attrition and other obstacles are beginning to affect the startups founded early in the modern AI boom. Established companies are adopting AI and its penetration rate in the overall enterprise solutions market continues to grow — with 95% of the market still to make its way into. Startups that survived the challenging early days are attracting investment and attention later in their life cycles. AI is proving its capacity to create value and is consequently being integrated by companies outside the startup world. While Canada remains a destination and a source for AI talent, it is still unclear if the talent pool can meet the demand.
Methodology
Following the logic of our last two reports, we have continued to keep an eye on the evolution of the Canadian AI Ecosystem by looking at investment trends, doing a census of AI startups, large companies having AI labs and other such actors of the ecosystem, as well as look at efforts from the government to help the ecosystem. As an added element from the last year, we have started tracking market size and market penetration of AI. Our report covers the period from Jan. 1, 2018 to Dec. 31, 2018. We define “Canadian AI companies” as independent companies that use AI as a core component for their product or service, and have their headquarters in Canada.
This report uses data from Crunchbase, and Tracxn, with additional analysis and data from Element AI’s own AI-powered scrapers and data analysis tools.
Acknowledgements
Research by Yoan Mantha and Jae Yong Yune.
Written by Peter Henderson and Yoan Mantha.
Data visualization and design by Wei-Wei Lin.
Special thanks to Chelsea Ma and Benoit Hamelin as well as Waterloo EDC, Real Ventures and Invest Canada for their continued support and insights.