For its annual Brokerage Report Card, Investment Executive asked advisors and firm leaders across 14 companies to discuss their use of, and familiarity with, AI. Nearly every firm has plans to use the technology to help advisors and their teams spend more time with clients and their families.
Firm size and capacity often dictate how deeply a firm can embed and monitor AI. While several large, bank-owned firms are busy building in-house AI tools to create efficiencies and gain client insights, smaller firms are taking a more cautious approach.
“Most advisors are aware of their organization’s AI policies and developments,” said Katie Keir, project lead for the Report Card and manager of research initiatives with Newcom Media. “About eight in 10 said they’d heard about AI or been trained on AI use. Among those who aren’t, some don’t feel comfortable with these new digital tools and others haven’t paid much attention. Advisors have varying degrees of comfort and knowledge when it comes to AI, Keir added.
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No matter how large a firm or bank is, no single tool or approach will perfectly fit every advisor’s needs, and there will always be some who prefer to do business their own way. Many are vetting AI vendors. Others — the large firms typically — are betting big on proprietary solutions.
Basic AI tools are freeing up advisors’ time, allowing them to serve more clients and strengthen key relationships.
Advisors will have more time to focus on personalizing high-value relationships, said David Bardsley, national wealth and asset management leader at KPMG Canada. Some clients will move between market segments over time and AI can help advisors identify mass affluent clients with upward potential so they can mentor those clients into the high-net-worth segment.
“AI is going to help us identify some of those clients earlier in their life cycle,” Bardsley added. “It will allow us to deliver a level of service that meets market requirements in the mass affluent space far more efficiently.”
Multi-channel strategies
CIBC Wood Gundy has organized data from its low-touch channels to be read by AI and is doing the same for its high-touch channels, said Robert Cancelli, executive vice-president and head of CIBC Wood Gundy.
While the bank’s proprietary AI helps advisors reduce manual tasks and focus on building their business, the human element is still vital, Cancelli said. Non-bank fintechs have developed competing digital offerings, so traditional banks like CIBC need to boost the client experience they specialize in.
“AI … empowers our advisors to deliver more bespoke value and enhance efficiency, but it is not a replacement for human advice,” he said.
In the same vein, ScotiaMcLeod wants advisor teams to meet with more clients and grow their businesses while maintaining service quality, and AI can help, said John McCartney, senior vice-president, global wealth and head of ScotiaMcLeod.
In April, Scotiabank launched its assistive Scotia Intelligence program for internal use, which will be the bank’s foundational AI platform across its lines of business, McCartney said. “There’s a huge opportunity to free up advisor and team capacity to spend more time with clients to deliver more personalized reporting and experience to the client.”
Using AI for back-end efficiency means it won’t replace financial advisors, said Jenni McIsaac, chief experience officer at Canaccord Genuity Corp. In particular, “Wealth advice for the affluent high-net-worth [and] ultra-high-net-worth is going to be more important than ever,” and those groups require high touch.
Affluent clients don’t want to rely on an AI agent for wealth, added Matt Cicci, head of the Private Client Group at CG Wealth Management Canada. Those clients want access to a human advisor, so the firm is using AI to boost advisor efficiency, improving processing and turnaround time for clients.
Quantified benefits
Large organizations want to quantify the economic benefits of each AI application, Bardsley said. That can mean optimizing specific advisor workflows and being mindful about regulatory expectations.
Firms need to find specific uses for AI, Bardsley added. Instead of deploying dozens of AI agents in every business process to unknown effect, organizations should prioritize using AI where it can produce the greatest return.
Off-the-shelf tools like Microsoft Copilot and ChatGPT — both mentioned often by advisors polled for the 2026 Report Card — may not be enough to maintain a competitive advantage, said Steve Galimi, vice-president of wealth management at National Bank Financial.
National Bank has launched an internal AI research engine where advisors can ask questions about investing in specific industries. It’s also placing AI functionality within its customer relationship management software to help advisors manage clients, Galimi added.
As technology advances, firms may need to release AI tools more quickly, Galimi said. Banks generally are designing software that meets 70% to 80% of advisors’ needs at launch and are then providing regular updates over time to close the gap.
At RBC Dominions Securities, the bank’s technology budget has increased every year for the last three years, said David Agnew, CEO of RBC Wealth Management Canada. He wants to help advisors scale and serve even more families, leveraging technology efficiencies. Royal Bank of Canada has a program called RBC Assist, plus the bank brokerage incorporates “the top AI tools,” it said in an emailed statement. In all cases, it added, “We [use AI] in a way [that] ensure[s] client data remains protected and does not leave RBC systems.”
Data and tool alignment
Big banks have scale, which makes development of their own AI tools affordable. But larger firms and banks don’t win by writing code; they win by aligning the data they have with the tools they develop, Bardsley said.
Smaller firms can leverage the same principle by being thoughtful about what data is used with external vendors.
Wellington-Altus Private Wealth started work on its data lake many years ago. Having a proprietary data source, owned and protected by the firm, makes it easier to experiment with emerging technologies like agentic AI.
“Think of a data lake as a journey, not a destination,” said Shaun Hauser, founder and CEO of Wellington-Altus. “You’re finding new areas and new information to put into it.”
Leede Financial isn’t pushing AI use by advisors to boost business growth. Instead, it’s looking to use AI to simplify administrative tasks, said Jim Dale, CEO. “We have been talking quite a bit about it. We did issue an update to our policy on AI, in terms of [which] tools were approved for use and which weren’t for advisors. [We also gave] feedback as to why some tools aren’t approved.”
For now, he added, “We’re working on tools that would help them [advisors] with things like HR [processes] and with documentation that they draw [from] in their client relationships. We’re experimenting with it internally.”
Odlum Brown has adopted Microsoft Copilot. But, “We want to be very careful with how we roll out [AI],” said Andrea Linger, that firm’s vice-president of sales and business development.
The firm is building a training regime around AI for its 100-plus advisors, Linger said. It’s also hired external consultants to help write its AI policy and has pilot groups beginning to use AI tools. Smaller firms can ask their software vendors which kinds of AI integrations are available, and they can work with vendors on how to access capabilities available within the tech stack platforms they use, Bardsley said. But they need to be wary of putting tools in “pilot purgatory,” where something is introduced but not meaningfully adopted.
What about security?
IA Private Wealth offers its advisors proprietary AI — like iAssist through its AX360 advisor dashboard — that was developed alongside the firm’s primary AI partner, Google Gemini. It also uses off-the-shelf enterprise AI, including Microsoft Copilot. Additionally, the firm lets advisors choose software that fits their practice, said Adam Elliot, president of iA Private Wealth.
For now, advisors can choose what they want to use, but iA is developing guidelines, Elliott added. Software choice could become more restrictive in the future if the firm chooses to permit AI use based on a vetted list of tools that meet security requirements.
The final list would need to accommodate different advisor budgets, Elliott added.
In addition to providing a vetted list of compliant software, firms can also speak to software vendors to find out which of them have higher AI potential, Bardsley said. Not all mid-market providers have the same capabilities, so helping advisors understand which tools have the greatest potential and the most meaningful AI integrations will help practices meet longer-term aspirations.
“If you haven’t started to see some of that AI capability become available to you, are they really the long-term partner of choice?” Bardsley said. “Clients in asset management are asking for … a view on which vendors have a track record of deploying uplift capability.”
Trust but verify
There is demand for AI tools at Richardson Wealth, said Julie Gallagher, president and CEO at Richardson since March. The challenge is to build support teams that can train advisors properly.
“We have the capabilities, we have the AI experts, we have the tools. But because our industry is moving so fast on digitalization, we need to make sure that we have the manpower to train our advisors,” Gallagher said.
For its part, Edward Jones (Canada) wants to combine human empathy with machine intelligence, said James Perry, leader of Canada wealth management and field management.
Edward Jones has rolled out a firm-wide training program to make sure everyone has the same knowledge level, said Jason Lounsbury, principal of Canadian products and portfolio management. This includes training on how to use prompts within large language models and other AI tools to quickly find answers, as well as tutorials on AI-powered tools and branch automation.
While advisor training is one part of ensuring compliant human interaction with AI, firms also need to set appropriately controlled environments, Bardsley said. AI outputs have limitations and can be biased based on how a person asks a question, so a human should be in the loop for all AI output.
Since AI is fallible and can hallucinate, every organization should have an internal mechanism under its AI governance where employees feel comfortable raising an issue around AI integrity, Bardsley added.