Raymond James Canada technology chief Jeff Newton explains how AI is reshaping how advisors spend their day
For Jeff Newton, Senior Vice President, Head of Technology at Raymond James Ltd in Vancouver, artificial intelligence is not a technology problem - it is a business one.
Speaking to Wealth Professional, Newton explains that the firm's entire AI strategy is organised around a single question: how do you give advisors more time with clients?
The answer, he says, is taking the friction out of the back office so that the people on the front line can focus on what AI cannot do - judgment, empathy, and the kind of trusted relationship that clients actually come to an advisor for.
Thinking about AI in three buckets
Newton frames Raymond James Canada's AI deployment across three distinct categories. The first is personal productivity - generative tools such as Microsoft Copilot, ChatGPT, and Zoom's AI meeting summaries that help advisors and their teams reduce administrative overhead. The second is what he calls application AI: the capabilities that technology vendors are embedding directly into their platforms.
"If you are a technology vendor out there and you're not talking about AI or implementing AI, there's something wrong," Newton says. Raymond James activates those capabilities as they launch, including enhanced optical character recognition and planning suggestions inside its financial planning software, and AI features within market data provider FactSet.
The third bucket is the most complex: bespoke internal builds targeting specific friction points where no off-the-shelf solution exists. Newton points to the firm's tax preparation team as a prime candidate for AI-driven automation.
The firm's trust business is another example. Unlike many Canadian trust companies, Raymond James does not require assets to sit on its own platform, which creates a back-office challenge of pulling statements from multiple broker-dealers into a consolidated trust view. "We use AI to kind of help with that," Newton says.
Governance and the third-party risk problem
For Newton, the single biggest information security challenge Raymond James faces is not internal misuse of AI, but third-party data breaches.
Several vendors, large and small, have been compromised over the past year. "We're very concerned about what happens to our data, where it's stored, how long is it kept," he says.
The firm runs a rigorous vendor risk assessment process before approving any technology for use in Canada, a review that can take several weeks. A parallel AI governance process applies the same logic to AI tools specifically. The number of AI products formally cleared for use across the firm remains deliberately small and is growing slowly.
A firm requirement is that AI providers do not train their models on Raymond James data. Newton notes that many of the most promising AI providers are effectively fintechs, capable of doing remarkable things but potentially lacking the governance maturity and cybersecurity depth the firm demands.
He points to CIRO, the Canadian Investment Regulatory Organization, having itself been the victim of a data breach in 2025 as an illustration of how broad this risk has become.
What AI cannot do - and where advisors still win
The question of whether AI will displace financial advisors comes up regularly in wealth management circles. Newton's view, developed from the inside of a major Canadian dealer, is clear.
"We don't think that AI replaces financial advisors," he says. "AI is great at processing information. It's good at automating administrative tasks, it's finding patterns. That's not what financial advice is. That's not why clients come to a financial advisor."
Newton draws a distinction between what AI provides - intelligence at scale - and what advisors provide: judgment, context, empathy, and an understanding of family dynamics and long-term goals that no model can replicate. He uses the firm's own technology teams as an illustration.
Early assumptions that generative AI could replace developers have not played out. Senior developers are still essential because AI-generated code requires experienced human judgment to evaluate whether it is fit for purpose. "You still need senior developers because they're the ones that have the experience to understand what the AI output is in order to evaluate whether it's good or not," Newton says.
The practical implication for advisors is what Newton describes as a "supercharging" effect: AI removes administrative friction, allowing advisors to service more clients with the same effort, deepen existing relationships, and build their books of business.
The FNZ transformation - AI under the hood
The development Newton is most excited about is what he describes as a core platform transformation: the firm is replacing its books-and-records system, which he calls "effectively doing a heart transplant on the firm."
Raymond James Canada is implementing the FNZ platform in a move that will make it the second FNZ customer in Canada after BMO. The current plan is to go live roughly six months after BMO's scheduled early-2028 launch.
What makes the programme significant from an AI standpoint is not the platform switch itself but how AI will be woven into it. "When I'm talking to advisors about what's coming, it's less about, hey, you're going to use an AI thing," Newton says. "You're using the platform and AI is sort of embedded. It's in the DNA of the platform itself."
The goal is that advisors benefit from AI capabilities without needing to know they are using them. Post-migration, the platform is expected to surface nudges, next-best-action prompts, and data-driven insights drawn from an advisor's full book of business. The longer-term vision is cross-functional AI automation spanning front-office advisors, compliance, and operations.
Managing adoption across a mixed advisor base
Newton is candid about the adoption challenge. The average age of an advisor at Raymond James Canada is 55, and the firm is managing two very different cohorts at once. One group is cautious; not necessarily resistant to change, but carrying a heavy load of regulatory demands and new technology alongside the task of running their own businesses. The other group is all in. "They want Raymond James to implement this stuff faster," Newton says. "I need this yesterday."
The challenge, as Newton frames it, is that AI is not really a technology to be trained on but a skill that requires a change in behaviour. And those AI capabilities are themselves moving targets. Something that failed in Copilot two weeks ago may work today as Microsoft and the underlying models continue to improve, which makes point-in-time training quickly outdated. "So it becomes a challenge when we're trying to roll out some of these tools," he says.
The firm's response is to focus less on one-off training sessions and more on building ongoing familiarity, helping advisors understand what AI can and cannot do, and integrating those capabilities gradually into daily workflows.
"Raymond James as a financial institution has to be pretty conservative," Newton acknowledges. But he sees the longer-term trajectory clearly: AI will become the infrastructure, not the feature. Much like the internet in the early 1990s, it will eventually just be there and we will stop talking about it.
