AI Could Revive Canada’s Productivity Crisis, Experts Say
Can Artificial Intelligence Solve Canada’s Productivity Woes?
The potential of artificial intelligence to propel Canada’s economic output is a subject of considerable discussion, spurred by concerns raised by Bank of Canada senior deputy governor Carolyn Rogers in May, who declared the country’s per capita productivity levels to be at “emergency” levels requiring immediate attention. Numerous proposed solutions have emerged, with the widespread adoption of AI consistently highlighted as one of the most promising and rapidly gaining momentum. Preliminary estimates suggest that generative AI could contribute as much as eight per cent to Canada’s GDP over the next ten years, according to a report by TD Bank. The Conference Board of Canada estimates a near two per cent GDP boost, anticipating this figure will rise with the introduction of new products and services. However, the path to realizing this potential is not without complexities.
The inherent benefits of AI are compelling. Generative AI systems can streamline tasks, automate content generation, and markedly increase efficiency. Google LLC estimates that the average worker could save around 100 hours per year by leveraging these technologies. Nick Romano, chief executive and co-founder of Deeplite, a Toronto-based AI company that primarily serves semiconductor clients, illustrates this with his own experience: “Translating complicated research at my firm into internal marketing materials now takes no time at all. By feeding the information into my AI agents, I can gain a significant productivity boost.” Daisy Intelligence, another Toronto-based company, specializes in helping retail and insurance businesses discover cost savings and detect fraud utilizing Reinforcement Learning AI systems. For retail clients, they claim to be able to increase total company sales by five per cent, with one client achieving a billion-dollar annual increase thanks to the AI’s assistance.
Despite these potential gains, several factors temper expectations. Canada lags behind its peers in AI adoption among businesses. According to a February report from Statistics Canada, only one in ten businesses plan to utilize generative AI. Among OECD countries, this places Canada in the mid-pack. Several challenges impede widespread adoption, heavily influenced by company size. Small businesses face resource constraints, whereas larger corporations grapple with integrating the technology across vast workforces. “Based on the data, it’s usually large companies that have the greatest challenges in adopting new technologies,” states Patrick Gill, senior director of the Business Data Lab at the Canadian Chamber of Commerce. “Think about the change management involved when you’re trying to get large workforces to try a new app.”
Additionally, a degree of hesitancy—perhaps fuelled by recent events—could be at play. A global IPSOS poll conducted last year revealed that Canadians are more apprehensive about AI’s adoption than their global counterparts, ranking 29th out of 31 countries regarding beliefs about benefits versus drawbacks. This sentiment, termed “skittishness” by Alain Francq, director of innovation and technology at the Conference Board of Canada, is largely rooted in concerns surrounding data privacy and security – areas where AI relies heavily. Trust in the technology’s application and the protection of sensitive data are therefore paramount.
The potential for job disruption further contributes to the cautious approach. The Future Skills Centre, a research institute founded by the Government of Canada’s Future Skills Program, projects that 22 per cent of jobs are at high risk of automation. Gary Saarenvirta, chief executive and founder of Daisy Intelligence, acknowledges this impact, stating, “Every client we do it at, we get violent pushback from the people who want nothing to do with it because they see the writing on the wall. Not that our goal is to replace people—I don’t want to do any such thing—that’s just a reality of the technology.” Romano recognizes a similar impact: “If you’re in a role where it’s a highly repetitive manual process that has been screaming for automation for a while, there will be vulnerabilities. But education is one of the primary gaps we have here. People aren’t afraid of it, they just have no idea how to use it, and how to benefit from it.”
Addressing these challenges demands strategic investments. “You can have the greatest ideas, the greatest technology, even the greatest government framework for adopting AI,” Francq emphasizes, “But unless you have the workforce of the future to implement it and adopt it and apply it, you’ll be dead in the water.” The Bank of Canada’s assessment—that Canada lags in investments for intellectual property, machinery, and equipment, alongside skills training for a growing labour force—highlights the urgency of these considerations. Canada currently sits as the second-least productive country in the G7, with productivity growth at just 0.9 per cent over the past decade—a stark contrast to the 1.4 per cent GDP growth predicted for 2024 by the International Monetary Fund, compared to the United States’ 2.1 per cent.
Ultimately, realizing the promise of AI in Canada hinges on a multi-faceted approach—combining technological innovation with strategic investment in workforce development and addressing fundamental concerns about data privacy and employment. The Bank of Canada’s designation of the situation as an “emergency” underscores the vital importance of moving swiftly to capitalize on this technological shift.
