Technology background with national flag of Canada. 3D rendering
The next generation of artificial intelligence is taking the world by storm. There’s been rampant innovation from startup communities with the emergence of large language models. Businesses in Canada, however, are slower to hop on this bandwagon.
The adoption of generative AI in Canada varies significantly by industry. Sectors such as finance, healthcare and technology have seen higher adoption rates, whereas manufacturing and retail sectors have been slower to adopt these technologies.
Earlier this year, the Canadian government laid the groundwork to address some gaps to AI adoption, announcing an investment of $2.4 billion to accelerate job growth in Canada’s AI sector and boost productivity by helping researchers and businesses develop and adopt AI.
The Canadian Chamber of Commerce’s Business Data Lab has released a report highlighting the “sluggish adoption” of generative artificial intelligence among Canadian businesses. Currently, only about 14% of these businesses are using or planning to implement generative AI in the near future. The report identifies several key issues contributing to this sluggish adoption, including multiple barriers such as high costs, concerns about data safety and a lack of skilled workers.
There is also a notable lack of trust in AI systems among Canadians. Only 32% express confidence in AI technology, which is lower than the global average of 39% and the U.S. average of 40%. This skepticism could further impede the adoption rates necessary for enhancing Canada’s economic growth. The potential impact of generative AI on productivity is significant; estimates suggest it could boost Canada’s productivity by 1% to 6% over the next decade, depending on how quickly businesses embrace this technology.
The report warns that if Canadian businesses do not accelerate their generative AI adoption, they risk falling behind their global competitors. It indicates that reaching a critical mass of generative AI adoption may take an additional three to six years, a timeline that could be too slow to keep pace with advancements in other countries. It emphasizes the urgent need for collaboration between businesses and policymakers to promote faster adoption, addressing Canada’s productivity challenges and ensuring the nation’s economic competitiveness.
Canada is home to pioneering AI research and talent. According to Deloitte’s 2023 report:
- Canada ranked first in the five-year average year-over-year growth rate in AI talent concentration compared to G7 nations.
- Canada was third among G7 countries in terms of its level of per capita VC investment in AI enablers, developers, and users, trailing only the U.S. and U.K.
- Canada leads the world in terms of bringing more women into AI roles, achieving the highest YoY percentage change in female AI talent globally since 2019, including 67% in 2022 alone.
- Canadian AI researchers produced more AI publications per capita in 2022 than any other G7 nation.
Canada’s leadership in AI talent, research and investment reveals an enormous promise and yet there exists an irony in Canadian business’ peculiar hesitation in AI adoption.
To understand the status of business in Canada’s AI landscape, I engaged with leaders of two organizations shaping the country’s technological future. From Vector Institute, one of Canada’s AI research organizations, I reached out to Cameron Schuler, chief commercialization officer and VP of industry innovation, and Ben Davies, chief information officer. Providing perspective from the enterprise technology sector was Shannon Katschilo, country manager of Snowflake Canada, a leading cloud computing and data platform company.
Canadian Conservatism Or Risk Aversion?
We start with the notion that generally Canadian businesses are fast followers and have a degree with risk aversion that influences the level and speed of adoption.
“Canadians are generally more conservative,” acknowledged Schuler. However, he was quick to point out that this conservative approach hasn’t hindered certain sectors from achieving global leadership. “The five large Canadian banks are in the top 50 globally for adopting AI, with RBC ranking third in the world,” Schuler noted, challenging the notion that Canadian cautiousness necessarily translates to technological backwardness.
Katschilo offered an even more optimistic view. “I don’t feel at all like Canada and what we are deploying is behind. I really believe that we are ahead,” she asserted. Katschilo characterized Canada’s approach as “crawl-walk-run,” emphasizing that Canadian businesses are strategically testing AI’s value before deploying it at scale.
Davies admited many companies are taking a passive approach to AI adoption, saying, “A lot of this is just seeping in by default, so a lot of companies are taking the position that their vendors are going to integrate AI into their platforms and they will get the benefit via their existing arrangements.”
The Infrastructure Challenge
A critical factor in Canada’s AI landscape has been infrastructure limitations. Davies noted that Canada’s global AI maturity ranking has dropped from fourth to eighth place “because of lack of public infrastructure.”
Integrating AI into enterprise systems, particularly for companies running legacy infrastructure means older systems can deter organizations that attempt to implement new technologies like generative AI. “What AI does also adds in a lot of additional complexity,” Davies said. “If you’re actually trying to onboard AI systems or build them yourself, you’re looking at an ecosystem of additional systems and tools to make that work, which really complicates your infrastructure and systems environment. It’s not for the faint of heart.”
However, he offered more optimism for companies seeking practical solutions: “That being said, a lot of the big commercial platforms that are offering AI as a service and have robust APIs give you good alternative avenues for integrating AI into existing products and services.”
The Skills Gap Conundrum
Davies also noted that talent shortage is also a contributing factor to AI Adoption, adding, “There’s a distinction between building AI capabilities in-house versus adopting commercial solutions. The key challenge with internal development is talent. AI specialists are hard to find and expensive, so that is a significant barrier for companies wanting to build their own AI solutions.”
And yet, according to Bloomberg, Toronto has moved up to fourth in the top 50 tech talent markets in North America for 2023. According to a recent CBRE report, the city added 95,900 tech talent jobs between 2018 and 2023, at a growth rate of 44%. This surge in employment reflects the rising demand for artificial intelligence skill sets across various sectors.
The issue is less about talent scarcity, but rather which organizations are successful at attracting these AI specialists.
Katschilo concurs and stressed that AI readiness extends beyond mere adoption or attracting the best talent but requires a properly trained workforce equipped to implement AI solutions. “I’m passionate about addressing the skills gap that we currently have,” she said, adding, “72% of Canadian organization report an AI skills gap according to a Tech Nation Canada report released recently. When I’m in front of executives, one of the questions I ask is ‘how many of your people are up skilled on AI?’”
In her discussions with executives, Katschilo focuses on three crucial areas: the number of employees upskilled in AI, the existence of comprehensive training and enablement programs and whether AI teams reflect Canada’s diverse cultural makeup. “These may not have been KPIs that organizations were tracking towards in the past,” she noted, “but I do believe that this is going to be critical for organizations to be successful in this new world.”
Is Canada Experiencing A Talent Drain To The U.S.?
BetaKit recently revealed the North American software engineering talent landscape is experiencing significant growth and demand. In Canada, projections show a need for 44,300 additional software engineers and designers by 2031. While major Canadian tech hubs like Toronto, Vancouver, Ottawa, Montreal and Waterloo are producing strong talent, salary disparities between Canada and the U.S. persist.
The Bureau of Labor Statistics forecasts a 25% increase in software engineering positions through 2031. U.S. software engineers are earning an average of $115,802, significantly more than Canadian counterparts, and companies are increasingly looking to tap into Canada’s skilled software engineering talent pool.
Vector Institute’s data staves off concerns about the talent drain, and according to Schuler, “Over 90% of master’s graduates coming out of AI programs are staying in Canada because opportunities are here.” This retention rate suggests that Canada’s AI ecosystem is maintaining its ability to nurture and keep high-level talent, despite fierce global competition.
Davies asserted Canada’s position in the global AI landscape is often underestimated, highlighting, “Despite being only the 39th largest country by population, Canada ranks as the ninth largest economy in the world and currently holds the eighth position in AI maturity, according to the latest Toronto ranking… I do think that in Canada, we’re particularly good at wringing our hands about our competitive positioning.” He emphasized that the country is “definitely punching above its weight class” in the AI sector.
While Davies conceded Canada has slipped from its previous fourth place ranking in AI maturity, this decline is attributed primarily to a lack of public infrastructure, and where the $2.4 billion funding support can be allocated.
Both Davies and Schuler are aware that Canada often compares itself to the United States, the world’s largest and most innovative economy–which may not always be a fair comparison. Nevertheless, both stressed that Canada’s AI ecosystem remains robust and mature, performing well even among G7 countries.
When it comes to startups finding partners or clients for their AI solutions, the opportunities south of the border are perceived as shorter sales cycles, and a stronger willingness to test new technologies. For Canadian startups seeking revenue opportunities in the United States, Schuler stated, “When you look at a company that is selling software the world is your market. And the U.S. market is 10 times the size, so I don’t have a problem with them saying, ‘We need to go build something that we sell in the U.S.’ I think that’s a good thing.”
And the benefit is to Canada, he emphasized. “That means revenue is coming back to Canada so, I’m not challenged by that.” Schuler remarked that previously, investors often included stipulations in term sheets requiring startups to relocate to the United States. However, this is no longer a common practice. Entrepreneurs can now remain in Canada, which is increasingly recognized as a favorable environment for launching companies.
The Integration Challenge
Beyond skills and infrastructure, organizations face significant operational challenges in integrating AI. “Data control and data quality is the utmost importance,” Katschilo emphasized. “There is no AI strategy without a data strategy.”
Katschilo shared an example of successful AI implementation in finance and described a case where a client’s data and IT team collaborated directly with the CFO to develop an AI solution that provided enhanced data insights for decision-making. The key to this success was the close cooperation between business and technical teams throughout the project.
Katschilo noted the importance of cross-functional collaboration, “It was about getting the business and the data teams at the same table, really scoping this out.” She noted that the IT team played a crucial role in enabling the project, while finance acted as an internal translator, explaining the technology’s implications to other departments.
Katschilo stressed that successful AI adoption requires stakeholders to focus on the business impact, ensuring effective cross-team collaboration and clear communication of the technology implications to effectively align technical expertise and business objectives.
Vector Institute’s approach to this challenge has been practical and hands-on. “We take research code and create engineering-ready solutions,” Schuler explained. “What would have taken companies six months took six weeks because they worked with Vector.” This acceleration of implementation represents a crucial advantage for Canadian businesses looking to adopt AI technologies efficiently.
Canada’s AI Future
As Canada continues its methodical march toward AI adoption, both perspectives suggest that the nation’s measured approach might ultimately prove beneficial. “We’re seeing a more conscientious approach to how we’re looking at AI,” Katschilo observed, while emphasizing that adoption rates are trending upward, with enterprise AI deployment increasing from 34% to 37% between April and November 2023.
Vector Institute’s Davies puts Canada’s position in perspective: “By population, we’re the 39th largest country in the world. By GDP, we’re the ninth largest, and we’re eighth in terms of AI maturity.”
The convergence of government investment, strong academic foundations and a growing ecosystem of innovative companies suggests that Canada’s AI future is promising, if distinctly Canadian in its approach. As Katschilo considered, “The era of enterprise AI is here, and we’re just scratching the surface.”
The challenge for Canadian businesses will be mindful of their approach to AI adoption while accelerating implementation to remain competitive globally. With new infrastructure investments, a strong talent pool, and a growing number of success stories, Canada’s “crawl-walk-run” strategy might just prove to be the right pace for sustainable, and responsible AI innovation.
The next few years will be crucial in determining whether Canada’s cautious strategy will translate into a competitive advantage as AI continues to evolve.