While a 2023 University of Chicago survey placed financial advisors at the bottom of AI adoption across eleven professions, the landscape has shifted dramatically by early 2026. Cerulli research now indicates that 70% of billion‑dollar registered investment advisors use AI for routine tasks such as note‑taking and call documentation.
Rapid market response
That change follows a wave of compliant AI tools built specifically for the financial services sector. In September, Anthropic and OpenAI each announced new products and integrations aimed at advisors, signaling that the AI titans view wealth management as a growing market.
Public interest in the Model Context Protocol (MCP)—the standard that lets software connect with AI tools—spiked in the spring of 2026 as fintech firms rolled out LLM‑enabled features. The Jump report highlights how this expanding capability is unlocking fresh use cases and measurable business impact.
AI maturity framework
The AI Maturity Model for Enterprise Wealth Management outlines four stages: Experimental, Operational, Strategic, and Transformational. Maturity is assessed across six dimensions, including AI capability, specialization, compliance, governance, tech‑stack integration, adoption, and business outcomes. An advisor using generic chatbots or public LLMs sits in the “experimental” stage, while firms that have woven purpose‑built AI into their client‑service workflow move toward “strategic” or “transformational” status.
“I’d call myself AI‑aware, but nowhere close to AI‑native,” says Drew Boyer, CFP, describing his experience with a purpose‑built AI system that syncs directly to his CRM. Danielle Darling, CDFA, feels further ahead, noting that AI now helps turn client conversations into actionable steps, freeing time for proactive outreach and business development.
Benefits and remaining gaps
Advisors who have integrated AI report higher productivity and an enhanced high‑touch client experience. By automating note‑taking and CRM updates, they reclaim hours for deeper client conversations and growth activities. However, both Boyer and Darling acknowledge that true end‑to‑end automation remains elusive. “I still review outputs and move information between systems,” Darling explains, emphasizing the need for fully autonomous agents.
Sarah Cicero, CFP and CFA, echoes this sentiment from a firm‑wide perspective, noting that connecting workflows across systems and reducing manual handoffs is the next frontier.
Impact on firm performance
According to the Jump analysis, firms that reach the “transformational” stage can leverage years of client relationships to make better business decisions, gaining a competitive edge. Early AI wins—meeting summaries, updated CRM notes, and saved hours—lay the groundwork for deeper process automation, which in turn drives organic AUM growth, net new assets, and higher revenue per advisor.
Even advisors early in the journey see tangible benefits. “Growth isn’t always AI directly handing you a referral,” Darling says. “Sometimes AI creates the capacity that allows you to take on more relationships while maintaining a high‑touch experience.”
Looking ahead
The pressure to advance remains strong. Boyer admits he has traditionally been a late adopter but now wants to avoid “holding a BlackBerry when everyone else has switched to the iPhone.” The industry’s rapid evolution suggests that advisors who lag may soon find themselves at a competitive disadvantage.
This story was produced by Jump and reviewed and distributed by Stacker.
Original reporting: El Paso News (HLL/CB) — read the source article.
