Why AI matters for investment advisers

Common AI use cases in the advisory business

AI use across investment management is likely to develop unevenly, with lower-risk internal productivity applications likely to emerge before more sensitive investor-facing or investment-decision applications. Potential uses include:

• Investment research summaries and market analysis support.
• Portfolio analytics and risk monitoring.
• Compliance surveillance, exception reporting and policy testing.
• Drafting and summarizing internal communications.
• Client service support and investor education.
• Operational automation, workflow management and document review.

The important question is whether the firm can validate, supervise, document and defend what AI produces on their behalf. The benefits can be substantial, but AI outputs may depend on opaque assumptions, incomplete data, changing model behavior or prompts that are not preserved in an auditable manner. Human review and intervention remains key to all AI lifecycles.

Large language models and investor communications

One of the most promising uses of AI involves helping investors understand complex information. Oftentimes, offering lengthy documents, prospectuses, shareholder reports, Form ADV disclosures and other regulatory materials can be difficult to navigate. By leveraging large language models, investors may be able to ask plain-language questions about fees, objectives, conflicts of interest, redemption provisions, benchmarks or general investment strategies.

In turn, these tools may improve investor understanding and engagement—however, that same functionality can create liability exposure. Human review of investor communications is essential to determine if AI-generated answers are inaccurate, incomplete, inconsistent with filed materials or reasonably understood as personalized investment advice or marketing.

A practical control framework should require that investor-facing AI tools be grounded in approved source materials, tested against known scenarios, limited by clear disclaimers, monitored for accuracy and reviewed for consistency with regulatory disclosures. Firms should also distinguish among operational support, investor education, marketing content and individualized advisory recommendations. As AI tools move from summarizing approved content toward generating individualized recommendations, potential regulatory and liability implications increase.

Professional liability implications

From a liability perspective, AI-related claims against advisers may arise when:

• An adviser allegedly overstates AI capabilities in marketing or client communications.
• A client claims that AI-assisted research, analysis or communications were inaccurate or inadequately supervised.
• AI-generated investor communications conflict with Form ADV, offering documents or other approved disclosures.
• A vendor-provided AI tool creates data privacy, cybersecurity, recordkeeping or operational failures.
• A firm cannot produce documentation showing who approved an AI use case or reviewed an AI output.

These scenarios are not fundamentally different from traditional adviser liability theories, but AI changes the speed, scale and evidentiary complexity of those issues. A firm that cannot explain how an AI output was generated, reviewed, approved or corrected may face a more difficult defense posture after a client complaint, examination finding or investor loss.

Regulatory and litigation developments to watch

The most directly relevant adviser matters to date are SEC enforcement actions rather than private civil lawsuits. In March 2024, the SEC announced settled charges against investment advisers Delphia (USA) Inc. and Global Predictions Inc. for false and misleading statements about their purported use of AI. The SEC stated that the firms agreed to pay $400,000 in total civil penalties.¹

According to the SEC, Delphia made false and misleading statements about its use of AI and machine learning in its investment process from 2019 to 2023 in SEC filings, a press release and on its website. According to the SEC, Delphia did not have the AI and machine learning capabilities it claimed; they also charged Delphia with violating the Marketing Rule.¹

Similarly, the SEC stated that Global Predictions made false and misleading claims in 2023 on its website and social media about AI use, including the claim that it was the first regulated AI financial advisor and that its platform provided expert AI-driven forecasts. The SEC said Global Predictions violated the Marketing Rule, falsely claimed it offered tax-loss harvesting services,\ and included an impermissible liability hedge clause in its advisory contract, among other securities law violations.¹

These actions illustrate a practical risk point for advisers: liability may also arise from how a firm describes, markets, supervises and documents its AI capabilities. If a firm claims to use AI, those representations should be accurate, supportable and consistent with actual practice.

Broader AI-related securities litigation is also increasing. According to Cornerstone Research and the Stanford Law School Securities Class Action Clearinghouse, securities class action filings increased 30% to 121 in the first half of 2026 compared with the prior six months, coinciding with a surge in litigation involving AI-related claims and technology companies. Their report also stated that investors filed 15 AI-related securities class action lawsuits in the first half of 2026—on pace to nearly double the 2025 total.^{2, 3}

That broader trend does not mean the same volume of claims has already emerged against investment advisers for AI use. It does, however, show that AI-related disclosures and representations are attracting closer scrutiny from regulators, investors and plaintiffs. For advisers, the lesson is to treat AI-related representations as compliance-controlled statements rather than informal descriptions of innovation.

Conclusion

AI should not be viewed solely as a technology deployment; it should be treated as a governed business process and subjected to the same disciplines that apply to investment decision making, client communications, compliance supervision, fiduciary oversight and books-and-records obligations.

The firms best positioned to benefit from AI will be those that can clearly explain how and why the technology is used—including how outputs are validated, how risks are monitored, how investor interests are protected and how AI-related statements are kept accurate. Advisers looking to integrate AI technologies should also prepare to demonstrate that AI-assisted activities are subject to prudence, supervision, transparency and accountability.