AI adoption among RIAs is moving faster than formal governance. According to 2026 RIA & AI Research Study, AI adoption among RIAs has more than doubled since 2023, reaching 63% of firms by late 2025. Broader industry surveys show that AI usage across financial services is even higher, with many organizations already using AI in at least one business function.

The governance gap remains significant. Only 15% of RIA firms maintain formal AI usage policies, 44% of firms already using AI have no formal testing or validation process, and just 10% have fully integrated AI into their business strategy. 

At the same time, the SEC’s 2026 examination priorities specifically address AI policies, procedures, supervision, and “AI washing,” while FINRA’s 2026 Oversight Report includes a dedicated section on generative AI. For RIAs, an AI policy is now part of fiduciary oversight and operational risk management.

How Should RIAs Assess AI Risks?

Effective policy development should begin with a firm-wide review of current and planned AI use. The policy should separate lower-risk internal productivity tools from higher-risk applications that affect client-facing materials, investment recommendations, or personal client data.

This distinction matters because AI errors can create direct financial and compliance exposure. AI hallucinations in financial tasks have been reported at 15–25% even in production environments, contributing to an estimated $2.3B in avoidable trading losses across the industry in Q1 2026 alone. 

RIAs should therefore define risk tiers, review standards, and validation requirements before AI outputs influence client communications or investment-related decisions.

What Should an RIA AI Policy Include?

An RIA AI policy should give employees clear rules for using AI in daily work. It should define which tools are approved, what types of tasks AI can support, what uses are prohibited, and how the firm protects client data.

These rules are especially important when AI is used for research, marketing, or client communications. Employees should not enter client personal information into public AI systems, research outputs should be checked against reliable sources, and any AI-assisted client-facing material should be reviewed before it is used.

The policy should also explain how the firm trains employees, escalates issues, and updates AI rules over time. This structure matters because only 10% of AI-using firms have fully integrated AI into their business strategy. A written policy helps RIAs move from scattered AI use to a more consistent and supervised process.

How Should RIAs Train Employees on AI Use?

A policy is only effective if advisors and staff know how to apply it in real work. Training should focus on the situations where AI creates the most risk: client data, research outputs, client-facing materials, and decisions that require human review.

This is becoming more important as 59% of advisors believe AI will have a direct, measurable impact on client relationships within the next year. 

What Overlooked Investment Workflows Can AI Improve for RIAs?

AI can be useful beyond research, note-taking, and client communications. One area where it is becoming increasingly relevant is securities class action recovery, which has historically been difficult for RIAs to manage manually. With roughly 1,000 active cases each year, firms need to identify eligible matters, connect them to client holdings, submit claims, and monitor recoveries across many accounts.

That process is easy to miss when it depends on manual review. Platforms such as 11th.com use AI to make recovery work more scalable by identifying relevant cases, matching them to holdings, and supporting the claim and payout process.

With a projected $9B available to claim this year, recovery alpha is becoming a practical way for RIAs to add value without creating another manual workflow.

What Should RIAs Prioritize in 2026?

In 2026, RIAs should treat AI governance as a core part of firm management and regulatory readiness. A practical policy should define approved tools, permitted uses, data restrictions, review standards, testing procedures, documentation requirements, and employee training.

FAQ

Why do RIAs need an AI policy?

RIAs need an AI policy to set clear rules for how AI is used in research, client communications, operations, and compliance. A documented policy helps reduce supervisory risk and supports fiduciary oversight.

What should an RIA AI policy include?

An RIA AI policy should define approved tools, prohibited uses, data restrictions, review standards, testing requirements, and employee training.

How should RIAs assess AI risk?

RIAs should review where AI is already being used and separate lower-risk internal tools from higher-risk uses involving client data, investment decisions, or client-facing materials.

Why is employee training important for AI use?

Training helps advisors and staff apply the policy consistently. It is especially important when AI affects client data, research outputs, or materials that require human review.

What overlooked workflows can AI improve for RIAs?

AI can help RIAs manage workflows that were previously difficult to handle manually, including securities class action recovery.