Why don’t Canadians use AI?

I’ve seen many articles and videos about how to use artificial intelligence (AI), and why you should use AI, and when to use AI. They all reference how easy it is to pick up this new technology and get started. And yet, Canada still has a huge AI adoption gap. This gap is highlighted in the AI strategy released by the federal government this year. Canada sits near the bottom of international rankings. Out of 47 countries surveyed, Canada is 44th on AI training and literacy, and 42nd on trust in AI systems.

As an AI scientist and Canadian, I’m left wondering: how is this possible when so many innovations underlying modern AI—from deep learning to reinforcement learning—were pioneered in Canadian research labs? Why aren’t Canadians adopting and using AI at the same rate as other developed countries? What are the actual barriers to adoption? As I discussed with friends and colleagues, I realized that there are some common barriers and some that might surprise you…

Social Stigma #

AI is taking a reputational hit right now. Does it use too much water? What about power demands? Environmental and social concerns are on the minds of many Canadians. The potential negative impact of this technology is not something folks want to be associated with. This halts curiosity before it starts. Using AI for small tasks, from a Google search to a document summary, can be seen as condoning the worst actions of the industry. And it leads to a situation where people may be willing to use AI, but not willing to be vocal about how much they are using it.

If we are to establish ethical use cases for AI, we need to start with curiosity and not condemnation.

Intimidation Factor #

Like any new technology, AI has a learning curve. And as it continues to evolve, that learning curve is steepening. This leads to both real and perceived learning gaps. There are tangible skills to learn: some are difficult, and some are not. What website do I go to? How do I get it on my phone? Knowing how to prompt a chatbot is easy; knowing how to integrate it into a legal brief, a research synthesis, or a project roadmap without introducing hallucinations or IP leaks is the real hurdle. But there is also an intimidation factor beyond those questions. Simply put, this new technology can feel big and scary. But digital literacy has always been built through hands-on experimentation, not theoretical mastery. The antidote to intimidation is low-stakes play and continuous practice.

Imposter Syndrome #

AI reveals an underlying fear in many of us: that somehow we may be expendable and replaceable, or that imposter syndrome might convince us we don’t belong. I believe these fears are defensive reactions to the nuanced emotional reflection of: “Workers convince themselves that AI can’t replace true human judgment, but secretly worry that much of their day-to-day work is formulaic enough that it could.”

I believe that the future of work rests not in carving out a career that AI can’t touch, but in augmenting your approach with tools that allow you to do more than you thought you were capable of doing. A growth mindset is the greatest remedy for imposter syndrome. And it is necessary in today’s economy. AI will challenge everyone to keep learning. And growth is the key to not only adopting AI now, but continuing to use it as it continues to evolve. You have to start somewhere.

Structural and Economic Barriers #

Beyond personal and cultural hesitation, there is an institutional reality. Canada has a well-documented techno-economic paradox: world-class foundational research institutions paired with one of the lowest tech adoption and capital investment rates among G7/OECD countries.

Most Canadian businesses are small- to medium-sized enterprises, which face capital, bandwidth, and talent constraints. AI adoption is seen as a cost risk rather than a growth driver. Many core sectors operate in protected oligopolies. This leads to low competitive pressure, which means companies are less motivated to invest in disruptive technologies.

Canada excels at basic research but struggles with commercialization and IP retention. Even breakthroughs developed in Canada are often acquired and commercialized by foreign companies, which leaves Canadian companies having to buy back the costly tech. Thus, the majority of the budget needs to be directed to tech procurement rather than employee literacy and change management.

Canadian public policy supported supply-side AI investment (talent, compute, research institutes), but neglected demand-side adoption (incentivizing firms to integrate and train their workforce).

Overcoming Adoption Barriers #

I see our adoption gap not as a failure of capability, but as a dual challenge: systemic inertia in our institutions and a crisis of confidence in our workforce. We have spent the last decade building a world-class foundation, but our workplaces remain hesitant to pick up the tools.

We don’t need to trade Canadian prudence for reckless tech obsession. What we need is a shift from passive observation to pragmatic application—supported by policy that drives innovation, and a workplace culture that replaces social stigma with open curiosity.

Canada gave the world modern AI. It’s time we bring our usage out of the shadows and finally give our own workers permission to use it.