In a recent episode of Hub Dialogues, hosted by Sean Speer and produced in partnership with RBC Thought Leadership, experts John Stackhouse, Senior Vice President at RBC, Janice Gross Stein, founding director of the University of Toronto’s Munk School of Global Affairs and Public Policy, and Jaxson Khan, CEO of Aperture AI, explored Canada’s paradoxical position in artificial intelligence (AI). While the country is recognized as a global leader in AI research, its adoption rates lag significantly behind other advanced economies.
The conversation was based on the RBC Thought Leadership report, “Bridging the Imagination Gap: How Canadian Companies Can Become Global Leaders in AI Adoption,” which examines the barriers to AI integration and proposes actionable solutions.
Here are five key takeaways from their discussion.
1. A cultural and structural challenge to adopt AI
Canada’s slow adoption of AI is not an isolated issue but part of a long-standing pattern of technological hesitancy. Stackhouse pointed out that Canadian firms have historically been cautious adopters, often waiting for others—particularly U.S. companies—to validate new technologies before committing. He likened this to the “second-mouse” strategy, where businesses avoid the risks of early adoption by letting others pave the way. While this approach may have worked with past technologies, AI presents a different scenario. The first movers in AI gain a decisive advantage by accumulating proprietary data and refining algorithms, creating barriers for latecomers.
Khan emphasized that even short delays of six to 12 months could leave Canadian businesses at a severe disadvantage, given the rapid evolution of AI capabilities. The report’s data underscores this urgency: only 6 percent of Canadian small and medium-sized enterprises (SMEs) have adopted AI, compared to 12 percent of larger firms.
This gap reflects deeper structural issues, including limited access to capital, insufficient digital infrastructure, and a risk-averse business culture. Without intervention, Canada risks falling further behind in the global AI race, missing out on productivity gains and economic growth.
2. Fixing the trust deficit: Overcoming fear of AI through education
A significant barrier to AI adoption in Canada is the lack of literacy and public trust in the technology. Khan noted that less than a quarter of Canadians have any formal or informal training in AI, and nearly 80 percent anticipate negative outcomes from its proliferation. This skepticism is particularly pronounced among SMEs, where leaders often lack the resources or expertise to explore AI applications. Gross Stein highlighted the “fear factor” surrounding AI, especially in smaller firms where decision-makers may not fully understand its potential benefits.
To address this, Gross Stein advocated for a cultural shift: encouraging businesses to experiment with AI tools in a low-stakes environment. She suggested that hands-on experience could demystify the technology and reveal its practical value.
The panel agreed that public policy must play a transformative role in bridging this literacy gap. Khan pointed to the U.S. Department of Labor’s executive order on AI literacy as a model, emphasizing the need for nationwide educational initiatives, from K-12 curricula to workplace training programs. Without widespread literacy efforts, Canada’s adoption gap will persist, stifling innovation and competitiveness.
3. Lack of AI regulatory frameworks means less clarity
The fragmented regulatory landscape in Canada—marked by overlapping federal and provincial rules—was a recurring theme in the discussion. Stackhouse argued that this lack of clarity discourages investment, as businesses fear legal repercussions or compliance costs. He cited the need for a coherent, innovation-friendly framework that balances risk management with entrepreneurial freedom. The recent appointment of Canada’s first minister of AI, Evan Solomon, was seen as a positive step, signaling a shift toward a more proactive policy approach.
However, Gross Stein offered a contrarian perspective: Regulatory incoherence can sometimes create opportunities for early adopters. She likened the current environment to a “wedge” that allows innovators to act before regulators catch up, citing examples from academia where ambiguity enabled experimentation.
The panel acknowledged that while long-term regulatory clarity is essential, short-term uncertainty should not paralyze progress. Khan proposed a “safe harbour” model, where businesses experimenting with AI in low-risk areas could operate without immediate regulatory scrutiny. This approach would foster innovation while mitigating risks, helping Canada strike a balance between oversight and growth.
4. Lessons to learn from Hopper, Bell, and Linamar
The report’s case studies provided concrete examples of how Canadian companies are successfully integrating AI. Hopper, a Montreal-based travel platform, demonstrated AI’s potential to enhance—rather than replace—human labour. By retraining its customer support team to collaborate with AI tools, the company reduced ticket resolution times by 75 percent without layoffs. This example underscored AI’s role as a workforce multiplier, improving efficiency while preserving jobs.
Bell’s experience highlighted the importance of leadership in driving AI adoption. The company’s board mobilized within weeks after quantifying the cost of delaying AI implementation, framing the technology as an existential imperative. This “cost of inaction” mindset proved transformative, enabling rapid decision-making and investment.
Linamar’s approach, meanwhile, emphasized the value of experimentation. The manufacturing firm allocated “exploration budgets” for AI projects, leading to unexpected breakthroughs. One notable discovery was an unidentified variable causing noise in electric vehicle gearboxes—a problem that traditional methods had failed to diagnose.
This case illustrates how AI’s unsupervised learning capabilities can uncover hidden inefficiencies, delivering value beyond initial expectations. Together, these examples showcased the importance of leadership commitment, strategic risk-taking, and creating “sandboxes” for innovation.
5. Digital infrastructure in Canada is critical
The panel drew parallels between Canada’s current need for digital infrastructure and the historic construction of railroads, which connected the nation and fueled economic growth. Gross Stein argued that investments in compute power, broadband, and digital literacy are just as critical today as physical infrastructure was a century ago. Without these foundations, Canada’s AI ambitions will remain unrealized.
Stackhouse emphasized the role of provincial governments in supporting SMEs, which often lack the scale to invest in expensive AI resources independently. He also called for greater public-sector leadership, noting that government accounts for over 40 percent of Canada’s GDP. AI could revolutionize service delivery—from passport renewals to health-care administration—but bureaucratic risk-aversion remains a major hurdle.
Khan cited the example of Miovision, a Waterloo-based AI traffic management company. While Toronto and Chicago deployed their systems simultaneously, Chicago achieved a rollout speed 10 times faster, covering 40 percent of its intersections. This disparity highlighted Canada’s “ambition deficit” and the urgent need for decisive action.
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Generative AI assisted in the production of this story.
