Canada AI Policy: Will 2026 Broaden Tech Access?

Canada is definitely trying to be a world leader in AI with its “All In” strategy. The goal of this Canadian AI policy is to build a top-tier tech sector. But you have to ask: is this push for inclusivity and broad tech access for real, or are we just building a new tech elite and leaving everyone else behind?

The Ambition: Canada’s AI Leadership Vision

Back in 2017, Canada made a splash by launching the world’s first national AI strategy, the Pan-Canadian Artificial Intelligence Strategy, which established three major AI institutes: Amii in Edmonton, Mila in Montreal, and the Vector Institute in Toronto. These hubs absolutely cemented Canada’s reputation in AI research, pulling in top minds and driving big leaps in machine learning. According to Innovation, Science and Economic Development Canada (ISED), by 2026 the federal government’s total spend on AI, including things like the Digital Research Alliance of Canada (DRAC) and various superclusters, has cleared $2 billion.

The government’s plan isn’t just about churning out research papers, either. It’s also about getting AI into the hands of businesses across sectors like healthcare and agriculture to boost productivity and open up the economy. This means funding programs like the Scale-Up Platform, part of the Strategic Innovation Fund, which has funneled millions into high-growth AI companies, and other programs designed to help small and medium-sized enterprises (SMEs) actually use AI and retrain their staff for the new jobs it creates. The whole idea is that getting everyone to adopt AI will make the entire country more competitive.

But here’s the problem. All that effort is concentrated in a few big cities. Toronto, Montreal, and Edmonton are booming AI centers, but what does that do for a business in rural Saskatchewan or a town in a remote corner of British Columbia? The official narrative loves to talk about the success stories coming out of the big hubs, which makes sense given where the money and people are. But you can’t claim national leadership if the benefits are stuck in a few geographic pockets.

The Inclusivity Challenge: Bridging the Digital Divide

The name “all in” implies everyone gets to play, but that’s not the reality. A 2025 report from the Canadian Internet Registration Authority (CIRA) shows that about 15% of rural homes still don’t have access to 50/10 Mbps internet speeds. That’s a baseline requirement for using most modern AI tools, meaning millions of people and thousands of businesses are already being left on the sidelines of the digital economy.

Even if you have a connection, digital literacy is a massive hurdle. Plenty of people and small businesses don’t have the basic skills to use or even make informed decisions about AI technology. Government training programs through Employment and Social Development Canada (ESDC) exist, but how effective are they really? A workshop that works for tech workers in downtown Vancouver probably won’t be accessible or relevant to a farming community in rural Manitoba, where the needs and available tech are completely different.

Take AI in agriculture as an example. You can have the best AI for managing crops, but it’s useless without a solid internet connection and, just as important, a local workforce that knows how to run and fix the sophisticated equipment. Without targeted investment in infrastructure and training for these specific regions, the promise of AI for them is just talk. Maximizing Canada’s economic potential means we can’t afford to leave huge parts of the population out of the AI revolution, because that means leaving their potential for new ideas and productivity on the table.

Policy Gaps and the Risk of Exclusivity

While the ambition in Canada’s AI strategy is good, critics are right to point out policy gaps that are creating an exclusive club. The heavy focus on R&D inside established academic and corporate clusters, while great for generating patents, creates a powerful suction effect, pulling talent and money into those centers. Without a serious counterweight in regional development, this model will just make existing economic divides even worse.

A big blind spot is the lack of specific, practical strategies for getting AI adopted in underserved communities. There are national programs, sure, but the actual plans for making sure Indigenous or remote Northern communities can benefit are vague at best. As organizations like the National Aboriginal Capital Corporations Association (NACCA) keep pointing out, these communities have unique infrastructure and cultural needs that a generic AI toolkit from Toronto just won’t solve. They need targeted funding and culturally aware programs, but current approaches often miss that completely.

And let’s be real, the obsession with developing next-gen AI often ignores the immediate need to fix basic digital infrastructure. How can you participate in an AI economy when you’re still fighting for reliable electricity or high-speed internet? It reveals a real disconnect between the government’s high-level vision and the reality for millions of Canadians. It’s like we’re trying to design a skyscraper while standing on a foundation full of cracks.

Pathways to Genuine Inclusivity: Recommendations for 2026 and Beyond

If Canada wants its “All In” AI strategy to be more than a slogan, it needs to get serious about inclusivity. That means tackling infrastructure, education, and regional development all at once.

First, a massive, targeted investment in broadband infrastructure for rural, remote, and Indigenous communities is the absolute baseline. Programs like the Universal Broadband Fund have to move faster and deliver service quality that can actually handle AI applications. This is about more than just connecting houses. It’s about giving businesses, schools, and hospitals the bandwidth to actually use these new tools. CRTC decisions on wholesale access are a start, but we need to see fiber in the ground.

Next, digital literacy and AI skills training must be local and specific. Forget the one-size-fits-all national approach. We need to fund local groups, community colleges, libraries, Indigenous organizations, that can deliver relevant training on the ground. It would be far more effective to set up apprenticeship programs focused on AI in forestry or mining (a program in Prince George, BC, on AI for sustainable logging, for example) that are built for specific regional economies and train local people for jobs that actually exist there.

Policy also has to actively push for regional AI innovation hubs outside of the big three cities. This could mean giving real incentives for AI companies to open offices in smaller towns, helping build up local talent. Government contracts could also be written to prioritize AI solutions from companies in underrepresented regions to directly stimulate that growth. Agencies like the Atlantic Canada Opportunities Agency (ACOA) or Western Economic Diversification Canada (WD) need to be given a clear mandate and specific funds to back AI projects in their territories.

Finally, we need a heavy focus on ethical AI development that actively hunts for and fixes bias. As AI gets built into everything, we have to make sure the systems are fair and don’t just amplify old prejudices. This means funding research into explainable AI (XAI) and demanding diverse teams are building these products from day one. The government’s proposed AI and Data Act must have teeth, with real accountability and transparency, so there’s a clear path for recourse when an AI system does harm.

Conclusion

Canada’s “All In” strategy has successfully put the country on the AI map. But for it to benefit the whole nation, the next phase must attack the digital divide and spread the opportunities beyond a few major cities. Without targeted investments in infrastructure and local skills, AI risks becoming another tool that widens the gap between the haves and have-nots.