2 weeks Ago By Hessie Jones, Contributor
Technology background with national flag of Canada. 3D rendering The next generation of artificial intelligence is taking the world by storm. We’re seeing rampant innovation from the startup communities innovating from the emergence of large language models.
Businesses in Canada, however, are slower to hop on this band wagon. 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, boost productivity by helping researchers and businesses develop and adopt AI. The Canadian Chamber of Commerce's Business Data Lab (BDL) has released a report highlighting the "sluggish adoption” of Generative Artificial Intelligence (Gen AI) among Canadian businesses.
Currently, only about 14% of these businesses are using or planning to implement Gen 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 Gen 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. Celebrities React To Trump Win: Roseanne Barr Says She’d Work For Trump, Billie Eilish Says He ‘Hates Women’ FBI Warns Gmail, Outlook Users Of $100 Government Emergency Data Email Hack New Chrome, Safari, Edge, Firefox Warning—Do Not Use These Websites The report warns that if Canadian businesses do not accelerate their Gen AI adoption, they risk falling behind their global competitors.
It indicates that reaching a critical mass of Gen AI adoption may take an additional 3 to 6 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 Gen AI 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’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 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," acknowledges Cameron Schuler, of the Vector Institute.
However, he's 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 notes, challenging the notion that Canadian cautiousness necessarily translates to technological backwardness. Shannon Katschilo, of Snowflake Canada, offers 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 asserts. Katschilo characterizes Canada's approach as "crawl-walk-run," emphasizing that Canadian businesses are strategically testing AI's value before deploying it at scale.
Ben Davies, also of Vector Institute, admits 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 notes 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 who attempt to implement new technologies like Gen AI. Davies expresses, "What AI does also adds in a lot of additional complexity. 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 offers 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 stresses that AI readiness extends beyond mere adoption or attracting the best talent but requires a properly trained workforce equipped to implement AI solutions as she states, “I'm passionate about addressing the skills gap that we currently have. 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 focusing 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 notes, "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 US? 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 US persist. The Bureau of Labor Statistics forecasts a 25% increase in software engineering positions through 2031. With US software engineers earning an average of $115,802 USD, significantly more than Canadian counterparts, and 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 9th largest economy in the world and currently holds the 8th 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 4th 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 US 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 US.’ I think that's a good thing.
" And the benefit to Canada, he emphasizes, "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 emphasizes.
"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 explains. "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 observes, 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 9th largest, and we're 8th 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 considers, "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..
