By David Rosenberg, Head of Machine Learning Strategy, Bloomberg’s Office of the CTO
Artificial intelligence isn’t just a buzzword in Canada’s financial sector — it’s quickly becoming the norm. According to a recent KPMG report, a remarkable 82% of Canadian investment firms are already tapping into AI, outpacing many of their global counterparts. The message from industry leaders is clear: AI is no longer a nice-to-have. It is actively reshaping productivity and transforming how investment decisions get made.
Yet, for all the enthusiasm, measurable returns remain elusive. For many firms, regulatory constraints and trust issues are proving as significant as the technology itself.
Productivity Tops the List
At a recent Bloomberg forum in Toronto, more than 60 senior AI financial executives were asked what excites them most about AI. Their responses highlight clear priorities:
- Productivity gains (40%)
- Streamlined workflows (28%)
- Better decision-making (26%)
“AI is allowing us to realize efficiencies, and we have gained considerable experience across several areas of our business. However, I believe it will still take a little more time for efficiency gains to translate into meaningful ROI for firms,” said Fera Jeraj, Chief Technology Officer at Canaccord Genuity, who spoke on a panel at the event about the practical applications of AI in the financial industry.
Adoption Is Strong, but Uneven
Despite the momentum, adoption is far from uniform, the executives polled said:
- 32% are lagging compared to their peers
- 29% are in line with competitors
- Just 6% consider themselves “well ahead”
Much of the disparity comes down to infrastructure. Firms with modern, cloud-based systems and strong API integrations are embedding AI more seamlessly. By contrast, those constrained by legacy technology are struggling to keep pace.
Trust and Compliance: The Biggest Obstacles
The largest barrier isn’t enthusiasm or funding — it’s regulation and trust. Nearly half (44%) of those polled cited compliance concerns as their top challenge, followed closely by a lack of trust in AI-generated outputs. In finance, errors are unacceptable, said Jeraj, adding: “If you’re advising a client on portfolio construction or risk, you can’t afford to be wrong.”
My own work in Bloomberg’s Office of the CTO has focused on assessing developing technologies related to AI and how to apply them to the financial domain. Our priority is helping clients improve discoverability — to find data quickly, speed up workflows, and make sharper decisions.
Where Firms See Value Today
Even with barriers, AI is already reshaping day-to-day operations. Executives highlighted current applications such as:
- Summarizing client conversations for advisors
- Processing vast volumes of content in capital markets
- automating repetitive checks in compliance departments
What’s Next
Looking ahead, Canadian executives surveyed see two waves of AI adoption:
• Near term: Back-office efficiencies, such as compliance monitoring and risk management
• Long term: Proactive, client-facing tools that anticipate needs and deliver predictive insights
For now, unpredictability remains the key obstacle. As Jeraj noted: “You can ask an AI system the same question twice and get different answers. That’s the challenge. As outputs become more reliable and transparent, confidence will grow.”
The Bottom Line
Canada may be ahead of the curve in adopting AI in finance, but firms are advancing with caution. Productivity gains are real, and efficiencies are emerging — yet measurable return on investment (ROI) is still just out of reach.
With regulation, trust, and integration challenges looming large, the coming years will determine whether Canada’s financial sector maintains its leadership position, or stalls under the weight of caution.
David Rosenberg is Head of Machine Learning Strategy in Bloomberg’s Office of the CTO
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