AI adoption among RIAs has accelerated quickly, but many firms are still choosing tools without a clear framework. According to the 2026 research, AI adoption among RIAs has more than doubled since 2023, reaching 63% of firms. Broader financial services surveys place AI usage even higher, with 88% of organizations using AI in at least one business function.

The challenge is that adoption is still ahead of strategy. Most RIA users rely on standalone generative tools, while only 10% of firms have fully integrated AI into their business strategy. 

At the same time, the SEC’s 2026 examination priorities focus on AI policies, procedures, disclosures, and “AI washing.” For RIAs, choosing AI tools is now a compliance and business decision, not just a technology decision.

What Should RIAs Consider Before Choosing AI Tools?

RIAs should start with specific use cases rather than general interest in AI. A tool should solve a defined problem, such as reducing meeting documentation time or improving draft preparation.

This matters because AI value depends on where the tool fits into the firm’s workflow. Some advisors report saving more than 10 hours per week with AI, or roughly 500 hours per year. Those gains are more likely when the firm chooses tools for clear operational needs instead of adding software without a defined purpose.

How Should RIAs Evaluate AI Data Security?

Data security should be one of the first questions in the selection process. RIAs need to understand what information enters the tool and whether client data is used to train models.

The strongest tools should have clear data policies and reliable security controls. Vendor review should also consider whether the tool connects safely with existing systems. This is especially important because 44% of firms using AI still do not have formal testing or validation processes.

How Should RIAs Measure AI Tool ROI?

AI tool selection should include a realistic view of cost and implementation. Subscription price matters, but it is not the only factor. Training time and workflow changes can affect whether the tool actually improves productivity.

RIAs should measure whether the tool saves advisor time or reduces operational errors. Scalability also matters because 68% of advisors expect AI to have a transformative effect on financial advice within three years. A tool that works for one advisor should also be able to support broader firm adoption.

What Overlooked Areas Can AI Tools Improve for RIAs?

AI can also support workflows that have historically been difficult to manage manually. One area where this shift is already visible is securities class action recovery. Firms historically had to match settlements to client holdings, file claims, and monitor payouts across large client bases.

Today, AI-powered platforms such as 11th.com automate this workflow and deposit recovered proceeds to client accounts. Its AI infrastructure covers recovery alpha across class actions, SEC Fair Funds, shareholder compensation, digital assets, and other recovery programs.

What Should RIAs Prioritize in 2026?

In 2026, RIAs should choose AI tools based on use-case fit, data security, compliance support, and measurable efficiency. The goal should be disciplined adoption rather than scattered experimentation.

FAQ

How Should RIAs Evaluate AI Tools?

RIAs should evaluate AI tools based on use-case fit, data security, compliance support, and measurable ROI.

What should RIAs consider before choosing AI tools?

RIAs should start with specific use cases and choose tools that solve clear workflow problems. This helps avoid scattered adoption and makes ROI easier to measure.

How should RIAs evaluate AI data security?

RIAs should review what data enters the tool, how that data is protected, and whether client information is used to train models.

How can RIAs measure the ROI of AI tools?

RIAs can measure ROI by looking at time saved, operational errors reduced, and whether the tool can scale across the firm.

What overlooked areas can AI tools improve for RIAs?

AI tools can improve workflows that were difficult to manage manually, including securities class action recovery and other opportunities.