Artificial Intelligence Is Already Reshaping Canada’s Economy. Policy Must Catch Up

From: Daniel Schwanen and Rosalie Wyonch

To: Artificial Intelligence watchers

Date: April 21, 2026

Re: Artificial Intelligence Is Already Reshaping Canada’s Economy. Policy Must Catch Up

After decades of gestation, artificial intelligence burst on the scene a mere five years ago, as a field with vast practical applications for businesses, researchers and the public. No longer is it a speculative technology looming in the distant future.  

And its rapid evolution and that of associated technologies presages more efficient models and systems and new applications – and the emergence of more innovators – in the future. 

All governments are also grappling with the potential downsides of how some of these technologies are developed and are used. AI is already transforming the economy. 

The key policy challenge is whether Canada is prepared to shape that transformation in ways that spur continued innovation and encourage productive adoption, while keeping Canadians safe, promoting trustworthy AI systems, and spreading the gains broadly across society. 

Like the harnessing of electricity or the launch of the internet, both widely seen as general-purpose technologies, AI’s economic impacts will likely come from myriad but diffuse, economy-wide improvements.  

Through these impacts, AI could help address some of Canada’s most persistent structural problems: Weak productivity growth, labour shortages, rising healthcare costs, and lagging business investment. But this is far from automatic. Misaligned incentives, regulatory uncertainty, and skills mismatches could easily slow adoption or prevent the emergence of new competitors. 

One recurring concern is how to capture these productivity gains. Canada invests heavily in frontier research and produces world‑class AI talent, yet struggles to translate innovation into widespread productivity gains or commercial adoption. Too many firms, especially small and medium‑sized enterprises, lack the data infrastructure, managerial capacity, or regulatory clarity needed to integrate AI into their operations. So far, productivity gains remain uneven and underwhelming. AI risks becoming another example of Canadian ingenuity benefiting foreign markets more than domestic ones. 

The interaction with Canadians’ skills set is a key issue. AI will not simply “replace jobs;” it will re-bundle tasks within jobs, augmenting some roles while eroding others. Highly skilled workers who can effectively use AI tools may become significantly more productive, while workers performing routine or automatable tasks could face displacement or downward pressure on wages. Ignoring these distributional effects could undermine public trust and feed resistance to adoption. 

Trust itself is a limiting factor. AI systems can be opaque, biased, or trained on problematic data. When applied in sensitive domains – credit, hiring, healthcare, public services – errors or lack of explainability carry real consequences for people’s lives. 

Over-regulation could choke innovation, but under-regulation risks eroding confidence in both markets and institutions. The policy challenge is to manage risk proportionately, focusing oversight where harms are plausible and material, and permit regulatory sandboxes, rather than treating all AI systems as equally risky. 

The international context matters as well. Canada is a middle power in a world increasingly shaped by US, EU and even UK or Japanese technology rules. If Canadian firms face incompatible regulatory regimes across markets, or if domestic rules are significantly more restrictive than those of trading partners, investment and innovation could migrate elsewhere. 

At the same time, Canada has an opportunity to influence global norms by emphasizing practical, risk‑based governance rather than sweeping, technology‑specific bans or firm-specific restrictions. 

So, what should policymakers do? 

First, Canada should prioritize adoption and commercialization. Research excellence is at the core of Canada’s AI reputation, but the productivity payoffs come from commercialization and diffusion. Governments can support these through targeted tax incentives, procurement policies that encourage AI innovation and use, and practical advisory services to help smaller firms develop and integrate AI responsibly. 

Second, rather than regulating based on attempts to divine the future shape of markets for AI models, systems and applications or future harms that may emerge, regulation should be principles‑ and outcomes-based, while remaining technology‑ and firm-neutral. 

Policymakers should focus on fostering positive outcomes, and preventing harmful ones currently covered by existing legal frameworks, such as those ensuring consumer protection, market competition, respect for human rights, or the right to advance liability claims. These should be updated where necessary instead of creating entirely new, overlapping regimes. 

Third, Canada must invest seriously in skills and adjustment supports. That means not only training AI specialists but helping workers learn how to use AI effectively. Wage insurance, reskilling programs, and portable benefits can ease transitions and maintain public support for technological change. 

Finally, governments should lead by example. Public sector adoption of AI can improve service delivery while setting standards for responsible use. 

AI will shape Canada’s economic trajectory whether policymakers engage or not. The choice is between reactive governance that may mean lurching from crisis to crisis, and deliberate, evidence‑based policy that maximizes benefits while managing real risks. The window for getting this right is still open, but it won’t remain so for long. 

Daniel Schwanen is Senior Vice-president of the C.D. Howe Institute where Rosalie Wyonch is Associate Director of Research. 

To send a comment or leave feedback, email us at blog@cdhowe.org. 

The views expressed here are those of the authors. The C.D. Howe Institute does not take corporate positions on policy matters. 

This Memo is extracted from a recent C.D. Howe Institute podcast.