Canada Invested Billions in AI. So Why Is Adoption So Slow?

In 2017, Canada became the first country in the world to launch a funded national AI strategy. Backed by an initial $125 million, it was a move to cement a permanent seat at the head of the global table, and for years, that early lead seemed to pay off. We built world-class research hubs in Toronto, Montreal, and Edmonton that attracted the brightest minds in the field and dominated academic journals.  

Since 2024, the federal government has committed roughly $3.3 billion to AI infrastructure and sovereign computing. It was a major investment to build computing power and the basic tools needed to compete globally. However, as we move through 2026, the landscape is shifting in ways that require us to look at things differently. The challenge has evolved: we have moved from the era of recruiting elite talent in our labs to building the infrastructure to support them, yet we are still missing the bridge to using these tools in the daily economy. 

The Learning Curve 

The data confirms that we are in a quiet transition. Statistics Canada reported in 2025 that about 12 per cent of Canadian businesses used AI to produce goods or deliver services in the last year. While this is a steady increase, growth is mostly seen in digital sectors like finance and information technology. 

A 2025 survey from KPMG Canada adds context to this trend. While over 90 per cent of business leaders say their organizations are exploring AI, only 2 per cent point to a measurable financial return. This reflects a period of careful experimentation, as most firms move through the early stages of adoption. But the outlook is shifting from curiosity to commitment: three in 10 respondents expect a return within the year, while six in 10 project a timeline of one to five years. For a local company, this transition requires building the digital foundations needed to move from a successful pilot to a profitable operation. 

This creates a growing divergence in the national economy, as some early movers are already institutionalizing this technology. Many of the country’s largest organizations have moved past simple pilot projects to set up dedicated AI divisions, often led by newly appointed Vice Presidents of AI or Chief AI Officers. For these leaders, the goal has shifted from testing the tools to scaling them across thousands of employees and daily operations. 

Refining how we use these tools is in everyone's interest because it's what makes the country more efficient. Canada's productivity has trailed other countries for a long time, and the Conference Board of Canada suggests that using AI more widely could boost our economy by 14 per cent over the next decade. We know that productivity drives wage growth and helps us keep our quality of life. 

Power and Practicality 

When we talk to the people tasked with making these systems functional, the conversation usually centers on data rather than theory. To run a reliable AI system, a business needs clean and organized data, but decades of old processes have created a logistical hurdle where modern applications are being forced into an outdated infrastructure. 

This friction extends to the national level, where Canada faces a shortage of domestic computing power. Canada’s share of global compute capacity has sat at less than one per cent among G7 peers. Without the ability to do heavy lifting here at home, we often find ourselves exporting our data and buying back the results from other countries. 

While the government is now reviewing proposals for large-scale, sovereign data centres, these projects face a significant physical reality: the electrical grid. With global data centre energy demand projected to double by late 2026, our provincial grids are facing unprecedented pressure. Until we can effectively plug in these supercomputers without straining the local power supply, our progress wil remain tethered to foreign interests.  

The Canadian Blueprint 

Canada is excellent at pioneering new ideas, and our challenge is to be consistent in how we use them. If we want to move forward, we should view AI as a basic utility like electricity or the internet. 

First, we need to help smaller firms get their data ready, supporting companies outside of the tech sector as they transition to AI-ready infrastructure. Second, we should simplify the human side of this work through clear national standards that give businesses the confidence to move from trials to investment. 

Finally, our goal should be to support a uniquely Canadian model that prioritizes privacy and people. A more deliberate, thoughtful approach can be a strength that allows us to build systems that we can rely on for the long term.