As artificial intelligence reshapes the Canadian banking industry, Royal Bank of Canada’s newly appointed head of AI describes it as a “generational technology” driving a broader shift—one the bank is racing to lead. But he also acknowledges that many of the fears surrounding AI are, in fact, legitimate.

Bruce Ross spent more than a decade as RBC’s head of technology before taking on the new role in February. The AI group he now leads—reporting directly to CEO Dave McKay—folds in Borealis, the bank’s in-house research lab founded in 2016, which helped position the lender as an early player in the machine learning field.

That’s helped place RBC among global front-runners. According to AI benchmarking and intelligence platform Evident’s 2025 State of AI Research in Banking report, RBC is Canada’s highest-scoring bank for AI maturity in its index of 50 international banks, and ranks third globally. But three of Canada’s Big Six banks also sit among the top 10 globally for the number of AI researchers, with TD Bank employing roughly 100 researchers—about double RBC’s count.  

Ross said banks’ real edge in the AI race won’t come from access to the same tools, but rather from the data they feed into them.

“One bank might have the Humber River, we’ve got the St. Lawrence,” Ross said in an interview with The Logic, referring to the scale of proprietary data flowing through RBC’s systems. With millions of clients across multiple business lines, he said, the bank has a deeper reservoir of data to train its AI models than its Canadian rivals. 

Competitive pressure means the Big Six are aggressively scaling their ambitions. Since the start of the year, Canada’s largest lenders have been setting targets and touting AI-related productivity gains. At RBC’s CEO conference in January, Harry Culham said CIBC is investing close to 20 per cent of its expense base in technology, including AI. Meanwhile, TD CEO Raymond Chun said the bank is on track to generate $1 billion in value from its in-house AI initiatives.

Ross’s mandate is also tied to a financial target: At its investor day last March, McKay said the bank expects to generate between $700 million and $1 billion in enterprise value by 2027 from AI. 

“We don’t break [the target] down into, say, X per cent for efficiency and Y per cent for revenue growth,” Ross said. Instead, RBC benchmarks AI systems against current performance, with finance teams auditing the gains before they are disclosed publicly.

RBC has also aligned its strategy with Canada’s ongoing push to develop its own sovereign AI. Ross said the bank is building much of its computing capacity in-house to better safeguard sensitive client data rather than relying solely on public cloud providers.

“We have the largest GPU farm outside of the federal government,” Ross said. “That’s on [premises] in RBC-owned data centres, and that’s sitting in Ontario.” 

He pointed to federal efforts to expand domestic computing capacity—including Bell Canada’s planned Regina data centre and the government’s partnership with Cohere—as a positive step. Asked whether RBC might eventually use new sovereign AI infrastructure in Canada, he said the lender is keeping its options open.

Ross described RBC’s AI strategy as unfolding in three stages: boosting employee productivity; improving operational efficiency by cutting costs and speeding up routine processes; and lastly, building new business solutions that can reshape how the bank serves clients and runs its core operations.

Many of RBC’s AI models are rooted in retail banking, such as ATOM—the bank’s Asynchronous Temporal Model—which draws on transaction data from credit cards, accounts and rewards programs to help determine how much credit to offer clients. Other uses are more visible, including app features that warn users about upcoming bills, Ross said.

“AI doesn’t solve every problem that you could possibly have. So we’re careful [about] the way we’ve been taking [products] to market.”

Still, Ross said AI has had an impact on productivity and decision-making across the bank. In software development, he noted, AI has boosted productivity in coding and testing, as well as infrastructure planning and disaster recovery. In commercial banking, AI is helping the bank assess how much credit to extend to customers by factoring in more variables than traditional methods. And in capital markets, RBC has been using its AI-driven trading platform, Aiden, in certain asset classes since October 2020.

For now, however, RBC draws the line at its wealth management division. Ross said the bank won’t put AI agents “directly in front of a client” without a human intermediary.

AI’s rise is already feeding anxiety about junior-level positions. Bank of Canada governor Tiff Macklem warned last month that the technology may be reducing the number of entry-level jobs available, underscoring broader concerns about how quickly the labour market can absorb the shift. 

Ross echoed that concern, saying AI-driven efficiencies could “reduce jobs in some parts of the ecosystem,” though many roles are more likely to evolve rather than outright disappear. The transition will force both companies and workers to adapt, he said, with employers needing to invest in reskilling while employees take responsibility for learning new skills. 

Asked whether RBC has made any workforce changes, including layoffs, as a result of AI, spokesperson Louise Armstrong said in an email that, like all major technology shifts, the introduction of AI means that “current jobs will change and new jobs will be created.”

“The worst place to be is [having a] shrinking business and applying AI to it, because then you have significant job loss. If you have a growing business with a level of efficiency, you should be able to provide other opportunities for people,” Ross said.

He struck a similar note of caution on the AI boom. He stopped short of calling the sector overvalued, but said current valuations are “clearly lofty,” adding that AI is moving fast enough to make any one tool quickly obsolete.