In 2026, 87% of organizations report at least partially integrating AI into their operations, while 41% have fully embedded AI into core business processes. Generative AI adoption has also accelerated, with 69% of financial organizations now using the technology.
For RIAs, these trends coincide with growing research demands, increasing market complexity, and SEC examination priorities that include AI governance, procedures, and disclosures.
Why Are RIAs Increasingly Using AI for Investment Research?
Investment research requires analyzing growing volumes of SEC filings, earnings transcripts, macroeconomic data, market news, and alternative datasets. As research coverage expands, AI is increasingly being adopted to organize information and identify investment opportunities more efficiently.
Recent workflow analyses show that "universe coverage" queries now account for approximately one in five financial research interactions. Rather than replacing analysts, AI is increasingly being used to expand research coverage beyond traditional watchlists and surface themes that warrant further investigation. Industry trends also point to growing adoption of agentic AI systems capable of generating research hypotheses and variant perceptions for human review.
Forecasts for 2027–2028 suggest AI will become more deeply integrated into research workflows as firms seek broader market coverage without proportionally increasing research staff. At the same time, industry expectations increasingly emphasize documented source verification and governance frameworks to reduce hallucination risk and improve transparency.
How Is AI Changing Investment Research Workflows?
Recent industry research indicates that AI adoption is influencing several stages of the research process.
AI-powered systems are increasingly used to summarize SEC filings, identify material disclosure changes, and organize lengthy documents into structured formats. In 2026, firms using AI for information filtering reported improved separation between disclosed facts and interpretation, while industry benchmarks associated AI-assisted filtering with higher signal quality and reduced time spent reviewing routine disclosures.
Research efficiency has also improved significantly. Workflow studies indicate that integrated AI platforms can reduce a traditional 40-hour weekly research process to approximately 8–12 hours, while expanding analyst coverage from dozens of companies to more than 150 securities. In some implementations, real-time monitoring has reduced response times to material corporate events from hours to less than 30 minutes, allowing analysts to dedicate more time to fundamental analysis rather than information gathering.
Recent guidance, including the CFA Institute's AI Transition Framework released in mid-2026, continues to emphasize that AI should function as a decision-support tool rather than a replacement for investment professionals. Structured briefings, scenario comparisons, and risk summaries can improve research workflows, but documented human oversight remains central to responsible implementation.
How Can RIAs Improve Operational Efficiency Beyond Investment Research?
While AI adoption is often associated with investment research, firms are increasingly applying automation to operational workflows that require significant manual effort.
Securities class action recovery is one example. Securities class action settlements totaled approximately $8 billion in 2025, yet many eligible recoveries continue to go unclaimed because filing claims has historically required substantial administrative work.
AI-powered platforms such as 11th.com automate full process of recovery through native integrations with major custodians and TAMPs, including Fidelity, Schwab, BNY, Pershing, Goldman Sachs, Merrill, Axos, Addepar, Orion, SS&C Black Diamond, and Advyzon, covering more than 85% of the market. As firms continue investing in AI across research and operations, automation is increasingly being viewed as a way to reduce administrative workloads while allowing internal teams to focus on higher-value analytical and client-facing activities.
How Should RIAs Prepare for the Next Stage of AI Adoption?
Current industry trends suggest AI adoption will continue expanding across investment research over the next several years. As organizations process larger volumes of market data and regulatory expectations around governance continue to evolve, firms are expected to place greater emphasis on documented oversight, source verification, and transparent AI workflows. Research indicates that combining AI-enabled efficiency with human judgment will remain the prevailing approach as investment teams seek broader coverage, faster analysis, and improved research capacity while maintaining fiduciary standards.
FAQ
Why are RIAs adopting AI for investment research?
RIAs are adopting AI to analyze larger volumes of financial data, improve research efficiency, and expand investment coverage while maintaining human oversight.
How can AI improve investment research workflows?
AI can summarize SEC filings, monitor market developments, identify investment opportunities, and reduce the time required to complete research tasks.
Can AI replace investment professionals?
Current industry guidance emphasizes that AI should support investment decisions, while human analysts remain responsible for oversight and final judgment.
What should RIAs consider before implementing AI?
RIAs should establish governance policies for AI, including source verification, documentation, model oversight, cybersecurity, and regulatory compliance.
How can AI improve operational efficiency beyond investment research?
AI can automate processes such as securities class action recovery, allowing internal teams to focus on higher-value analytical and client-facing activities.
