Financial services firms spent most of 2023 and 2024 running AI pilots that rarely moved beyond the proof-of-concept stage, but that phase has now largely ended as the industry shifts toward practical deployments that are beginning to reshape how advisory teams handle day-to-day work. 

FINRA’s 2026 Regulatory Oversight Report confirms that the most common real-world application of generative AI across member firms is summarization and information extraction. 

At the same time, regulatory attention is increasing, with the SEC’s 2025 examination priorities listing AI alongside fiduciary duty and cybersecurity as key areas of review, signaling that firms deploying these systems without proper governance and documentation are taking on real supervisory risk.

Current Use Cases for RIAs

Meeting support and internal information retrieval have become the most common entry points for AI in advisory firms. Adoption data from Schwab Advisor Services shows how quickly this shift is happening, with AI usage among independent RIAs more than doubling between 2023 and early 2026, and 63% of firms now reporting that they use AI in some capacity.

Alongside this, portfolio commentary has emerged as a more advanced use case, and this reflects a broader shift in industry sentiment, where 85% of advisers now view generative AI as a net positive and 76% report immediate operational benefits.

Compliance and Risk Controls

As AI becomes more embedded in advisory operations, compliance has moved into a core design requirement, especially as regulatory frameworks tighten around how these tools are used in practice. 

FINRA’s 2026 report makes clear that its rules are technology-neutral, meaning generative AI does not change existing obligations, but it also highlights that AI can amplify existing risks, particularly through hallucinations, where systems generate confident but incorrect outputs. 

At the same time, the industry faces a clear governance gap, as 57% of firms allow employees to experiment with AI tools while only 35% have formal policies in place, which creates real exposure when regulators request documentation. This is further complicated by vendor dependence, since most AI capabilities are delivered through third-party CRM systems and compliance platforms, making external oversight a necessary part of governance. 

The SEC has already demonstrated the consequences of weak controls by charging two investment advisers in 2024 for misleading statements about their AI use, resulting in $400,000 in penalties, which reinforces that AI-related marketing claims must be supported by documented internal controls.

AI in Overlooked Areas

For RIAs, AI is not only improving efficiency but also opening up new opportunities to enhance portfolio outcomes and increase AUM, particularly in areas that were previously difficult to manage at scale. One of these areas is securities class action recovery, where many advisory firms have historically struggled due to the complexity of tracking eligibility across large client bases and the manual nature of the process.

In 2025, total securities class action settlements reached $8 billion, yet a significant portion of eligible funds still goes unclaimed each year due to filing complexity and the operational effort required to match claims to investor holdings. Thanks to AI, platforms such as 11th.com fully automate the securities class action recovery process, returning proceeds directly to client accounts and creating an immediate AUM lift for RIAs.

Looking ahead, AI adoption across RIA workflows is expected to accelerate further in 2027 as firms move beyond experimentation and focus on operational efficiency, advisor capacity, and scalable growth. Industry trends show that the strongest adoption will continue to center around structured, reviewable workflows such as meeting summaries, CRM updates, portfolio commentary, compliance review, and settlement recovery, where firms can automate repetitive work while maintaining oversight and documentation standards.

FAQ:

What is the main use of AI in RIA firms today?

The most common use of AI in RIAs is summarization and information extraction, helping advisors quickly process meeting notes, research, and client data.

How are RIAs using AI in daily workflows?

RIAs use AI for meeting preparation, note-taking, CRM updates, research retrieval, portfolio commentary, and client follow-ups.

What are the compliance risks of using AI in RIAs?

Key risks include inaccurate outputs, lack of documentation, data privacy issues, and weak governance over AI tools and vendors.

What do regulators say about AI in wealth management?

Regulators like the SEC and FINRA require firms to maintain supervision, proper documentation, and transparency when using AI. The SEC has already established enforcement precedent in this area, charging multiple investment advisers in 2024 for making misleading claims about their AI capabilities and issuing hundreds of thousands of dollars in penalties.

How does AI impact AUM for RIAs?

AI can increase AUM by improving efficiency and uncovering missed opportunities, such as securities class action recoveries.