Financial advisors are adopting AI tools faster than most wealth management firms expected, but the technology has not yet become a core part of daily operations. According to LPL Financial executives speaking on a VettaFi webcast, 70% of advisors now experiment with large language models or point solutions, while only 12% have AI deeply embedded in their workflows.
John Stevens, senior vice president of product management AI at LPL Financial, and Miller Staten, head of product operations at LPL, discussed the current state of AI adoption and where the technology is heading next. A poll of webcast attendees found 45% experimenting with tools like ChatGPT, 25% using one or two point solutions such as note-taking apps, 18% not using AI at all, and 12% with AI integrated into daily work.
Agentic AI moves beyond answering questions
The next shift LPL is watching is agentic AI - systems that don't just respond to prompts but can take actions toward a goal with some degree of independence. Staten said leading firms are building agentic solutions for internal operations and advisor-facing capabilities alike.
"This is of course a major change, and the leading firms are building agentic solutions both to improve their internal operations and to help provide advisor facing capabilities," Staten said.
Products that once required large investments and dedicated teams can now be built at a fraction of the cost, with better quality and faster delivery, according to Staten. For advisors, the practical application starts immediately after a client meeting. The agent can summarize meeting notes and suggest next actions, leaving the advisor with the task of approving and finalizing workflows with a single click.
LPL is also developing features that let advisors update account information in one location instead of modifying records across multiple systems. Additional work is underway on account lifecycle management, money movement, and financial planning. For teams interested in the broader AI agents and automation category, these developments signal where enterprise tooling is heading.
Data safeguards remain a priority
Stevens said firms must maintain a conservative posture on client data and privacy. Advisors should look for contractual limits on data usage, data masking and tokenization, identity and access management for AI agents, and additional protective measures before adopting any tool.
The caution is warranted. Client financial data is among the most sensitive information a firm handles, and the regulatory environment for AI in financial services is still taking shape.
Start with one high-friction task
LPL's advice for advisors unsure where to begin is simple: start small. Rather than implementing multiple AI solutions at once, advisors should pick a single time-consuming process with high friction and test AI there.
Email management is the clearest candidate. Stevens said advisors report spending 30% to 40% of their day on email-related tasks. Account maintenance and financial planning also offer substantial long-term savings. The AI productivity gains from even one automated workflow can return meaningful hours to an advisor's week.
When webcast participants were asked how they would spend five extra hours per week, roughly one-third chose personal time and nearly another third said they would direct it toward business growth and prospecting.
Despite ongoing discussion about AI replacing financial professionals, LPL leadership offered a different view. Software can automate routine tasks, but it cannot easily duplicate the personal connection between an advisor and a client.
Why this matters for managers
For managers overseeing teams of advisors or knowledge workers, the LPL data offers a practical benchmark: most of your team is likely experimenting with AI informally, while very few have integrated it into daily operations. The gap between experimentation and embedded use is where productivity gains sit. Managers should identify one high-friction workflow - email triage, meeting documentation, or account updates - and pilot an AI tool there before expanding. The goal is not to replace staff but to return hours currently lost to administrative work.
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