{"id":15222,"date":"2025-06-24T14:54:58","date_gmt":"2025-06-24T14:54:58","guid":{"rendered":"https:\/\/www.rbc.com\/en\/?post_type=rbc_tl&p=15222\/"},"modified":"2025-07-15T17:41:57","modified_gmt":"2025-07-15T17:41:57","slug":"bridging-the-imagination-gap-how-canadian-companies-can-become-global-leaders-in-ai-adoption","status":"publish","type":"rbc_tl","link":"https:\/\/www.rbc.com\/en\/thought-leadership\/the-growth-project\/bridging-the-imagination-gap-how-canadian-companies-can-become-global-leaders-in-ai-adoption\/","title":{"rendered":"Bridging the Imagination Gap: How Canadian companies can become global leaders in AI adoption"},"content":{"rendered":"\n
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As artificial intelligence comes of age, Canada finds itself at a crossroads. While we possess world-class research and a robust talent pool, the country is falling behind as global competitors race ahead in AI adoption. The core challenge is not a lack of technology or talent, but a pervasive \u201cimagination gap\u201d\u2014a widespread inability among Canadian businesses, especially small and medium-sized enterprises (SMEs), to see AI as relevant or beneficial to their operations. Only 12%<\/a> of Canadian firms have integrated AI into their production or services, placing Canada among the lowest in AI adoption in the OECD. Data<\/a> from the OECD also shows that Canadian firms tend to explore a more limited set of use cases for AI than other nations.<\/p>\n\n\n\n

And yet, the upside is clear. A recent Business Development Bank of Canada survey<\/a> revealed that 97% of AI-adopting SMEs reported \u2018tangible\u2019 benefits. And Statistics Canada<\/a> data showed that AI\u2019s impact on task reduction is particularly pronounced in companies with fewer than 100 employees\u2014underscoring significant potential for SMEs. The issue was also high on the agenda at the G7 in Kananaskis, Alberta, where leaders committed to \u201cdouble down\u201d on AI adoption efforts to improve prosperity.<\/p>\n\n\n\n

To better understand why Canadian businesses have been so slow to adopt AI, RBC Thought Leadership partnered with the University of Toronto\u2019s Munk School of Global Affairs & Public Policy and conducted more than two dozen in-depth interviews with senior business, public service and technology leaders in Canada. Here\u2019s what we learned about the barriers that companies, both big and small, are facing. And some lessons from organizations that have taken the challenge of AI adoption head-on.  <\/p>\n<\/div>\n<\/section>\n\n\n\n

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1. First-Mover Dilemma: Certain Costs, Uncertain Benefits<\/h2>\n\n\n\n

Some companies that have been slow to adopt AI are locked in inertia. The costs associated with AI adoption are immediate and tangible, while the benefits seem distant and notional. For chief technology officers, AI initiatives carry fixed, up-front financial costs, as well as reputational costs if the project fails. But, as some of the leaders we spoke with recognized, late adoption carries the risk of lagging behind quick-moving competitors. It\u2019s a double\u2011edged sword: move early and risk losing scarce capital and personnel resources; move late and risk competitive disadvantage.<\/p>\n\n\n\n

Several technology leaders noted that these uncertainties frequently stall approvals by six to 12\u202fmonths. Adding to that, they expressed frustration that Canadian industry leaders often failed to clearly perceive the benefits competitors were already achieving through AI. Technology developers even cited achieving greater success pitching their AI solutions to U.S. based divisions of Canadian companies than their domestic counterparts.<\/p>\n\n\n\n

To navigate these obstacles successful AI transformation leaders recommended clearly quantifying AI investments by contrasting the costs of immediate action versus the cost of inaction. Tools such as \u2018cost of delay\u2019 dashboards help clarify the opportunity costs of not acting sooner.<\/p>\n\n\n\n

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Bell\u202fCanada: Overcoming Inertia Bias<\/strong><\/h3>\n\n\n\n

When GPT\u20114 burst onto the scene in early\u202f2023, Bell\u2019s directors wanted to know immediately<\/em> what waiting to implement might cost them. Within weeks, the AI\u202fGroup President convened two board\u2011level tutorials and unveiled \u2018cost\u2011of\u2011delay\u2019 analysis that contrasted lost productivity with the modest price of pilot projects. The numbers were decisive: capital to fund AI applications was released the same quarter. Real\u2011time speech analytics now mine 100% of the firm\u2019s 50,000 daily customer calls, surfacing friction points that were previously buried in anecdotal samples. This has enabled AI voice and chat agents to handle inquiries with greater accuracy.<\/p>\n\n\n\n

Cultivating a \u2018culture of entrepreneurship and experimentation\u2019 has also allowed Bell to grow innovative AI use cases from the bottom up, developing novel AI applications that vastly improve communication processes, workflows and customer satisfaction. <\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n

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2. AI Literacy: Moving from Apprehension to Opportunity<\/h2>\n\n\n\n

Whether it\u2019s a fear that AI is coming to take their jobs or just a lack of understanding of its benefits, Canadians are skeptical of AI. One recent KPMG study<\/a> found that 79% of Canadians are concerned about negative AI outcomes. And it\u2019s estimated that less than one-in-four Canadian employees have received AI training. Simply put, most Canadians haven’t engaged sufficiently with AI to demystify it.<\/p>\n\n\n\n

While having an AI champion in the corner office or a single business unit dedicated to experimentation and implementation helps, if AI expertise remains confined to a narrow \u2018priesthood,\u2019 widespread adoption stalls. Our research indicates that companies that invest in AI literacy for their staff see faster scale-up of AI projects, stronger employee engagement, and growing organizational confidence. Knowledge is a powerful catalyst for continuous innovation and competitive differentiation.<\/p>\n\n\n\n

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Hopper: Workforce Reskilling for Enhanced Efficiency<\/h3>\n\n\n\n

Rather than using AI to displace its customer support staff, Hopper, a Montreal-based travel platform, trained employees to take on roles focused on AI content, training, and testing. Up-skilling its staff to embed AI into its customer support function not only addressed employee hesitation, it allowed Hopper to handle customer inquiries 75% faster\u2014reducing average resolution time from 15\u201320 minutes to 3\u20135 minutes. It did this without compromising customer satisfaction and led to cost savings of ~90% compared to human-driven interactions.<\/p>\n<\/div>\n<\/div>\n\n\n\n

Canada\u2019s most successful adopters match grassroots experimentation (\u201csuper\u2011agency\u201d employees who already prompt, patch and prototype with GenAI) with an executive\u2011mandated transformation agenda. When only the bottom layer is active, shadow\u2011IT proliferates and pilots stall for lack of budget or risk authority. When only the top pushes, initiatives feel imposed, and staff revert to old workflows. <\/p>\n\n\n\n

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Lumberhub: Bottom\u2011Up \u201cSuper\u2011Agency\u201d in Traditional Industry<\/strong><\/h3>\n\n\n\n

When a chronic pricing lag between sawmills and home\u2011builders kept eating into margins, George McKeown, a PhD chemist turned lumber trader, asked a simple question: Why do we accept this inefficiency?<\/em><\/p>\n\n\n\n

Lacking a deep coding background, he turned to GenAI pair\u2011programmers to develop over 40k lines of code and in less than three months built a conventional react\/typescript web app running on Amazon Web Services that ingest real\u2011time futures data, spits out dynamic quotes for every stock keeping unit (SKU), and auto\u2011generates purchase orders for suppliers.<\/p>\n\n\n\n