New US research tracking SEC filings finds wealth management firms adopting AI are growing headcount and adviser capacity, not reducing them
Wealth management firms that have disclosed meaningful use of artificial intelligence are hiring more staff and outperforming peers on advisor productivity.
That’s a key finding from new American research released on September 3, 2026. The 2026 RIA Market Monitor produced by Astraeus, an AI-native infrastructure company for wealth management based in New York City, in collaboration with Pirker Partners, a strategic advisory firm specialising in business and technology innovation, analyzed Form ADV Part 2A filings submitted by 6,384 independent registered investment advisors (RIAs) in the United States as of March 2026.
Form ADV is the primary disclosure document through which US wealth management firms describe their operations and material business risks to the Securities and Exchange Commission (SEC) under a legal obligation to be accurate.
The study found that only 6% of independent US private wealth RIAs disclosed any use of AI, machine learning, or algorithmic tools in those March 2026 filings. However, those firms collectively managed approximately 11% of industry assets under management; a concentration that reveals where meaningful adoption is actually occurring.
Contrary to the view that AI will reduce headcount, the research found the opposite is happening at US advisory firms. Total headcount grew 15% at firms disclosing AI use between April 2025 and April 2026, compared with 8% at firms without AI disclosures, according to the Astraeus report.
Among enterprise and large RIAs (those managing more than $5 billion in client assets) non-advisory, or operational, staff increased by a median of 14.2%, more than double the 6.7% growth rate recorded for advisory employees at the same firms.
"The firms moving fastest with AI are hiring people, investing in operational infrastructure, and building the capabilities necessary to support more sophisticated businesses," said Jon Stevenson, co-founder and president of Astraeus, based in New York City. "At this stage, AI appears to be creating capacity rather than replacing it."
Adding operations staff
The pattern is relevant to Canadian wealth professionals, who face many of the same questions about AI's effect on practice staffing and advisor productivity as the industry evolves.
Rather than automating away positions, firms that have adopted AI are adding operations staff to implement, supervise, and maintain new systems; a dynamic the report's authors describe as the current technology being in its "infrastructure phase."
Firm size is the defining factor in adoption. Among US RIAs managing between $5 billion and $25 billion in assets, 16% disclosed AI use, compared with 7% of mid-sized firms and 5% of smaller RIAs, per the Astraeus data.
Hybrid RIAs, which offer both fee-based and brokerage services and tend to have larger, more operationally complex middle offices, were more likely to disclose AI adoption (7.4% compared with 5.5% of fee-only firms).
This concentration at the top of the market reflects a well-established pattern: firms with greater financial resources are better positioned to fund technology investments, and the growing scale of AI deployment across wealth management is accelerating that divide.
The productivity findings are notable. Among enterprise and large RIA adopters, assets under management per advisor grew by a median of 22% between April 2025 and April 2026, compared with 12% among comparable firms without AI disclosures, according to the Astraeus report.
The authors cautioned against attributing this gap entirely to AI, noting that adopting firms were already outgrowing peers before widespread deployment began; suggesting that firms with the means to invest in AI are also those with the momentum to perform.
"Meaningful adoption is occurring primarily among firms with the scale, resources, and operational complexity to invest in enterprise initiatives," said Alois Pirker, founder and chief executive of Pirker Partners. "This study provides a baseline for understanding where adoption actually stands and how it evolves over time."
What are advisors actually doing with the technology?
Use cases remain firmly operational at this stage. Nearly half of disclosing firms cited AI for administrative efficiency (note-taking, summarising client meetings, updating CRM systems, and drafting documents) according to the Astraeus report.
Investment research was the second most common category, referenced by just over one-third of disclosing firms. Fewer than 5% reported using AI as a direct input into asset allocation or security selection. Over half of enterprise, large, and mid-sized RIAs explicitly stated in their disclosures that AI does not make investment decisions.
That restraint also shapes how firms communicate with regulators. Of the firms that disclosed AI use, 42% discussed risks only, 55% balanced risks and benefits, and just 4% led with benefits. The report's authors argue this reflects legal counsel's influence on disclosure language — and means the public record on AI use almost certainly understates actual adoption across the industry.
"The firms that disclose are communicating under a legal standard of accuracy," said Andrew Lasky, head of GTM and strategy at Astraeus. "That makes the data especially valuable for understanding how firms are thinking about AI, even though actual usage almost certainly extends beyond what appears in public filings."
The investment profile of AI-adopting firms adds further context. Firms disclosing AI use were nearly twice as likely to offer private equity investments as non-disclosers (47% against 24%) and more than three times as likely to offer credit alternatives, per the Astraeus data.
Over half also offered access to hedge funds, compared with 38% of non-disclosers. The researchers attribute this overlap to AI's strength in extracting structured information from complex, unstructured documents; the kind of material that dominates alternative investment workflows.
For Canadian advisors assessing their own AI strategies, the US data offers a sobering baseline: the gap between early AI adopters and those yet to invest meaningfully in the technology is already visible in growth and productivity metrics, even before attributing that gap directly to AI.
