Canada Should Harness Its AI Advantage, Not Squander It
Canada faces a pivotal choice on artificial intelligence (AI): It can build on its world-class research and top-tier talent to stay at the forefront of the global AI economy, or it can risk squandering its early lead by prioritizing regulation over deployment. To preserve the early advantages earned through visionary research, homegrown expertise, and strategic investments, Canada must focus not just on developing AI but on putting it to use.
As Lawrence Zhang and Daniel Castro write in a commentary for the Macdonald-Laurier Institute, in an era when AI is poised to improve everything from crop yields to cancer detection, Canada’s central priority should be accelerating AI adoption to enhance economic prosperity and quality of life—not erecting barriers to innovation through overly precautionary regulation.
Countries worldwide increasingly view AI as a strategic tool for economic growth, not a lab-bound novelty demanding excessive oversight. They are prioritizing the tangible benefits of innovation over speculative safety concerns. Yet, as global momentum shifts toward proactive AI development and deployment, Canada remains overly cautious. If this hesitation continues, Canada risks falling behind—not just in AI itself, but across the many sectors where AI will drive transformative change.
Some warn that the government should “handle with care” when it comes to AI, arguing that it is “better to be safe than sorry.” That said, excessive caution can lead to regret, too. The precautionary principle assumes that avoiding hypothetical risks is the safest course—but overregulation and stagnation carry real risks of their own. Countries that constrain innovation in the name of safety risk losing ground in economic competitiveness, technological leadership, and even societal well-being. In a fast-moving world, excessive risk aversion often means surrendering progress to those willing to move forward—and that would be the greater mistake.
Where to Focus Efforts
Scaling AI adoption means overcoming more complex challenges than typical digital upgrades. Unlike launching a website or shifting from fax to email, AI adoption often faces regulatory, legal, or operational uncertainty that can stall progress.
The article outlines which Canadian industries are leading in AI use, which are in the middle, and which are lagging—and contrasts that with where generative AI is perceived to offer the most value. Notably, several sectors slow to adopt traditional AI tools now see high potential in GenAI, suggesting significant untapped opportunities.
Understanding why adoption is slow for specific tasks in certain industries is necessary to improve adoption rates, especially when the barriers to adoption are the result of existing policies. As Zhang and Castro argue, creating clear rules that facilitate AI adoption and do not penalize its use can smooth the pathway for adoption.
The gaps between where AI is being adopted and where generative AI is seen as most valuable point to important opportunities. Early adopters may see limited returns from GenAI due to previous investments in legacy tools. In contrast, sectors with low adoption but high perceived value—such as real estate or entertainment—could leapfrog directly to transformative use cases. Others may still underestimate GenAI's potential. Bridging these divides with policy reforms, targeted support, real-world pilots, and education initiatives can convert curiosity or doubt into genuine adoption.
A third layer of analysis based on occupation-level data further refines where adoption will likely succeed. It shows that GenAI use extends beyond technical roles into creative, educational, and administrative jobs, suggesting that workforce skills and job functions, not just sectors, should be considered when evaluating where adoption is most feasible and scalable. Even industries slow to adopt AI may still benefit if they employ roles that already embrace these tools.
As described in the article, these insights point to a three-part strategy:
- Industries such as finance, professional services, and administrative support are already implementing AI at scale and stand to benefit from deeper integration of GenAI. These industries may have higher rates of adoption due to fewer legislative, regulatory, or cultural barriers than other industries. Continued support for adoption in these sectors will accelerate productivity and set examples for others.
- Industries like real estate, wholesale trade, and arts and entertainment rank AI highly in perceived value but have not yet adopted AI broadly. These industries present ripe opportunities for targeted support that turns said interest into implementation.
- Industries like manufacturing, healthcare, transportation, and resource industries have lagged in both AI adoption and enthusiasm. However, applied AI tools such as diagnostic support, patient triage systems, and workflow optimization can deliver real gains. Demonstration projects, investment in building high-quality datasets, and sector-specific guidance can help shift perceptions, chip away at regulatory barriers, and accelerate adoption.
Recommendations
- Focus adoption-related interventions on industries where AI can deliver the most substantial benefits but where adoption remains sluggish.
- Build data ecosystems to facilitate AI adoption.
- Across all sectors, expanding skills training will be crucial to address the productivity lag.
- Canada should avoid creating layers of pre-emptive rules that discourage innovation or drive AI development offshore.
- To address safety concerns, Canada should instead emphasize evidence-based, post-deployment monitoring rather than overly restrictive precautionary measures.
- Government leadership through public sector AI adoption is critical.
Canada’s AI edge won’t matter if businesses don’t actually use the technology. To boost adoption, policymakers need practical, sector-specific strategies—not broad, theoretical plans.
