Canada’s AI potential is, by now, beyond dispute. Canadian AI researchers enjoy global acclaim and produce the most academic papers per capita in the G7. Institutions like Amii, Mila, and the Vector Institute are advancing the frontier of what’s possible. By some estimates, Canada has the highest concentration of AI talent in the world.

Yet these advantages have yet to translate to the kind of tangible economic benefits experts expect from AI. Canadian per-capita GDP growth is the lowest among G7 nations, and productivity has deteriorated (with the rift between the U.S., in particular, widening). Canadian businesses trail global peers in AI uptake, performance, and commercialization. “We have amazing strengths in research leadership, which has put us in a very strong position, but in my mind, that is really only a starting point,” explains Alex LaPlante, Vice-President of Cash Management Technology, Canada at RBC, who also serves on the board of Mitacs and sits on the Government of Canada’s AI Strategy Taskforce. “Our challenge now is much less about invention, and much more about diffusion, scalability, and execution.”

Experts believe human capital plays a critical role in solving this challenge. In 32 taskforce reports and more than 11,300 public submissions gathered by the federal government to inform its forthcoming AI Strategy, talent emerged as a major lever, with contributors calling for (among other things) the development of more robust pipelines, stronger links between academia, industry, and government, and the codification of AI talent as a strategic national asset. 

It is a matter of some urgency, according to Stephen Lucas, CEO of Mitacs, a non-profit that funds collaborative research projects and connects emerging talent with organizations in Canada’s innovation ecosystem. “We cannot ignore the AI impact gap,” he says. “Every day that we don’t focus effort on educating and supporting people so they can deploy and scale AI safely and effectively means we’re further behind, offensively and defensively, in terms of its utilization.” 

If Canada wants to translate its considerable AI prowess into real economic gains, experts believe organizations must deploy talent more broadly, strategically, and effectively than they have done to date. How? Consider how new approaches to finding and equipping talent can help address three of the most persistent contributors to the AI impact gap: Low trust in the technology, slow adoption among firms, and sluggish productivity. 

The trust gap

For all the leading-edge work being done in the country, Canadians, by and large, have yet to embrace AI with open arms. A 2025 Abacus Data survey found that 61% of Canadians believe AI poses a threat to employment, personal privacy, and societal stability, nearly twice as many as the share that sees it as a tool to improve life in Canada. In a 2025 evaluation of 47 nations conducted by KPMG and the University of Melbourne, Canadians ranked near the bottom in terms of AI knowledge, efficacy, training, and regular use. Only three countries (Finland, Japan, and Czechia) demonstrate more citizen distrust toward the technology. “A lot of Canadians are feeling unprepared right now for AI and the pace that it is coming at us,” explains Elissa Strome, Executive Director, Pan-Canadian Artificial intelligence Strategy at CIFAR. “That’s where a lot of issues arise.” 

Strome is one of several experts advocating for a human-centric campaign to bolster Canadians’ trust in AI. That includes improving general AI literacy, so people better understand the core functions, capabilities and risks of the technology. More public engagement can also play a role; Strome is a fan of town-hall discussions that allow citizens to voice concerns, ask questions, and have direct conversations with AI technologists and decision-makers. “This is a human endeavour, not a purely technical one,” she says. “You can’t just say, ‘Everybody go adopt AI.’ You have to build a culture and learning and goals around it.”

Put another way, experts contend that those leading the advancement of AI need to do a better job of showing their work: Of explaining not only what is happening, but also how it will affect the stakeholders involved, what safe deployment looks like, and, importantly, what benefits will come from it in the short and long terms. “If we want to be able to convince people that we’re using this technology for a reason, we need people who not only understand the tools, but also understand, at a fundamental level, other people,” says Noel Baldwin, Executive Director at the Future Skills Centre, a federally funded research organization housed at Toronto Metropolitan University. 

This trust-building requires the creation of organizational skill matrices that prioritize both pure technical chops and so-called “soft” capabilities like critical thought, responsibility, and deep empathy. “When organizations train and deploy skilled talent from a broad range of sources, it helps to create receptor capacity,” Lucas explains. “It encourages the day-to-day demonstration of safe use, which is necessary for people to understand the technology, shape useful use cases, and build trust.”

The adoption gap

A major contributor to Canada’s AI impact gap is the fact that relatively few Canadian companies are using the technology in a sustained and substantial way. Overall, adoption of AI among Canadian businesses doubled from 2024 to 2025, but the rate still sits at just 12.2 per cent, well below G7 and OECD averages. While most Canadian tech scale-ups and many large institutions (notably, financial institutions, telecoms, and aerospace firms) are all-in on AI, the vast majority of smaller companies in the country exemplify what C.D. Howe Institute researchers describe as a “persistent adoption deficit.” 

The reasons are, by and large, practical. Canadian small- and mid-sized enterprises (which comprise more than 99% of domestic businesses, employ nearly 64% of private-sector jobs, and generate more than half of the country’s GDP) are generally ill-equipped to seamlessly integrate technology as potentially transformative as AI,  often lacking resources to hire teams of data scientists, expertise to develop implementation strategies, and acumen to know where to begin. 

“SMEs are very much being left out of the technical side of AI in our economy,” comments Iaian Archibald, Executive Director of DeepSense Halifax, a Dalhousie University-housed applied research platform that connects startups and SMEs with post-secondary interns to help lead practical AI projects, including through partnerships with organizations such as Mitacs. “AI has advanced so quickly that even when you’re paid to be on top of it, it’s very hard to understand evolutions in the technology and the breadth of use cases.” 

It would be a tricky situation for SMEs to navigate in a market flush with skilled prospects, but in the context of an international AI talent race, experts say it’s critical for organizations to think more creatively and cast wider nets. “There’s so much work that needs to be done in AI, at every level, and Canada does not have the capacity in the private sector to deliver that right now,” Archibald says. “There’s a real need to transfer the expertise of academia and the talent within student bodies to the rest of society.”

For organizations, this can take the form of convening basic or intermediate AI training for existing employees who are heavy on domain expertise, but lighter on technical skills. It can mean bringing in emerging talent on a project basis, to translate pilots or individual experiments into firm-wide adoption. It can involve partnerships with colleges, universities, and CEGEPs to give AI-fluent interns and co-op students practical experience, from hyper-technical PhDs to humanities and social science majors who’ve been using LLMs and machine learning for years. “Firms today do not have to bring in expertise permanently to expand their capacity,” explains Baldwin. “There are mechanisms that allow expertise to flow in and out as organizations are trying to solve problems with AI, and they can be very powerful.”

Many smaller organizations are starting to see just that: According to Lucas, 81 per cent of the more than 4,000 AI-focused internships supported by Mitacs last year took place within SMEs. “It’s not just about providing a student with an integrated workplace learning experience,” he says. “It’s giving the organization the expertise of both that student and the faculty they’re working with, to solve a specific problem that they want to solve and are willing to co-invest in.”

The productivity gap

AI has been widely pegged as a solution to Canada’s pervasive productivity underperformance, but to date the technology’s impact on this critical economic metric has been minimal. 

Experts say that’s due, at least in part, to misalignment born of AI’s exponential growth curve: In many organizations, technological capabilities have simply evolved faster than the strategic and operational apparati needed to direct AI to applications that improve efficiency and output. “AI has the potential to improve the productivity of an organization’s overall business strategy, both in enabling better ways of producing products or services and in making individual employees more productive,” says Strome. “But developing a business strategy for AI is harder than just picking up tools and starting to play with them.”

According to LaPlante, it’s time for organizations move beyond toying with AI because it’s there (or because everyone else is doing it), and towards environments in which the technology becomes an agent of strategic productivity, whether the goal is to generate new revenue, create new markets, improve processes, enable faster decisions, enhance client outcomes, or all of the above. “We require larger cultural shifts within our organizations to get the most benefit out of these capabilities,” says LaPlante. “And how do you drive a cultural shift? You drive it through talent.” 

Experts say the human capital imperative here is twofold. First, companies can ensure all senior leaders, board members, and people managers are sufficiently AI-literate to understand, with clear eyes, how the technology can be integrated to advance productivity. “It doesn’t mean that everyone has to become an expert,” Lucas says. “But senior leaders should understand the ways in which smart talent strategies, especially those that combine different types of expertise, can accelerate the path to productivity and lower the risks.” 

Second, organizations can be structured so that people with diverse skills and perspectives have opportunities to work together on AI projects that address real, relevant pain points. Archibald sees this work every day, in the collaborations between DeepSense’s AI-fluent interns and seasoned domain experts within the SMEs that enlist their help (and, often, hire them permanently). “It’s like a force multiplier,” he observes. “Deploying AI talent like this is probably the primary lever we have to increase productivity.” 

Data from a Mitacs-Statistics Canada study backs this up: A 2024 report found companies that had partnered with Mitacs to embed talent experienced an 11 per cent spike in productivity, a 9 per cent increase in revenue, and a 16 per cent rise in sales.

A different path forward

No adaptation to technological change is without effort, especially not one as consequential as AI. But experts say organizations that approach AI integration as the human challenge it is, and adjust their talent strategies accordingly, will be better positioned to reap the economic spoils of this transformative technology. Furthermore, they’ll help the country to do the same. 

“As we focus on our sovereign capability, on pursuing other markets, and on strengthening economic resilience, we need to bend the curve on the continuously widening productivity gap with the U.S. and other comparator economies,” says Lucas. “I think AI provides Canada with an extraordinary opportunity to do that, but realizing it means we need to do some things differently, including the ways we deploy talent.”

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