Using artificial intelligence to increase efficiency is becoming standard in wealth management. Advisors are now looking to use the technology to drive revenue and organic growth, according to panelists at a recent industry conference.
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That shift — from efficiency gains to growth — was a key theme at Future Proof Citywide in Miami Beach, Florida, where executives said firms are starting to push AI beyond back-office use cases.
Jaclyn Stanton, managing director of F2 Strategy, moderated a panel on “Building AI Into the Core” featuring Brock Sutton, head of emerging client capabilities at Capital Group, and Peter Nolan, head of asset and wealth management at Anthropic.
Stanton said firms tend to think about AI in advisor tech as either “above-” or “below-the-line.” The former is used to increase revenue, while the latter focuses on reducing manual tasks.
Up to recently, Stanton said she had generally seen more below-the-line, efficiency-based uses. But that’s starting to change.
“We’re starting to see a shift in more firms doing ‘above-the-line’ AI, where they’re seeking revenue and organic growth as their outcome,” she said.
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The four levels of AI adoption
In his experience, Sutton said there are four levels of AI adoption in the wealth management industry.
The first is what he called “dabblers,” or those firms “that aren’t taking it seriously yet.”
“Maybe they have access to some tools,” he said. “They’re implementing it in some kind of specific places. It’s usually content creation and summarization.”
The second is where advisors start to prioritize AI in three to five areas and measure return on investment over the next year. Sutton estimated that 95% of the teams he works with fall within these first two levels.
The remaining 5%, Sutton said, are in the third category, where firms begin implementing AI across the entire business. But zero firms he has worked with have made it to the fourth level: building from the ground up with AI in mind from the outset.
“We won’t see anyone get there until the technology progress starts to slow down,” he said. “If you build something today versus 12 months ago, some of the ways that you would go about building it based on some of the capabilities … would change.”
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Where to begin?
Advisors who may be daunted by the challenge of figuring out where to start with AI should first take an internal inventory, said Nolan.
“The best time to start is now,” he said. “At least get wise to it. A lot is still taking shape. I wouldn’t panic.”
Nolan said he warns against “fear-buying” multiple AI tools.
“I’d be thoughtful around it, but I’d certainly be learning about its capabilities and making sure that you’re at least understanding and what it can potentially bring to you and your clients,” he said.
He said advisors should take the time to write down all the tasks required to run the business and score them on two metrics, each on a scale of one to 10: frequency (or total time spent) and business value.
“Get people excited about implementation,” he said. “It’s a pain point for them. They’re actually getting some value relatively quickly.”
An AI-enabled tech stack ‘body’
After a firm lists and maps out potential inefficiencies, the next step is deployment.
Stanton said a helpful analogy is to think of the AI-enabled tech stack as a human body: The large language model (LLM) is the brain, the AI agents that perform tasks are the arms and legs, and the data is the nervous system tying everything together.
In this analogy, Anthropic’s LLM “brain” is Claude, which also serves as its chat interface. Some advisors have also begun using Claude Code to fill in gaps in their tech stacks. Cowork, the more user-friendly version of Claude Code, allows users to create the “appendages,” or AI agents.
Last month, Anthropic unveiled a Wealth Management plugin for Claude that includes tools for investment proposals, client reviews, rebalancing and tax-loss harvesting.
With this plugin, Nolan said the goal is not to replace existing advisor tech stacks.
“What we are trying to do is encourage all the builders at wealth tech companies … to start building through plugins,” he said. “They’re equipping builders who are experts. We have compliance people who are administering access and being careful around how they deploy AI to do all of that. So we’re not trying to create a final application.”
Making sure the data is in order
As firms put these pieces together to increase revenue and organic growth, Nolan said they should start with solid data and fundamentals.
“A lot of people are curious about the furious pace of rolling out new capabilities and models,” he said. “I wouldn’t get particularly caught up with that.”
If advisors are intent on using tools like Claude to power the “brain” of their AI-enabled tech stacks, those systems are only as good as the data that connects them.
“Make sure your data is clean and in order, because if it’s not, it’s garbage in, garbage out,” he said. “You’re just going to ruin things if you try to automate something that’s broken.”
