AI shifts mass-affluent clients to automated wealth tools

Wealth managers are directing clients with $100,000 to $1,000,000 in liquid assets toward AI-driven tools, while reserving human advisors for ultra‑high‑net‑worth households that need direct handling of family and emotional issues.

Debasish Patnaik, a senior partner at McKinsey, described the mass‑affluent client as “no longer worth spending human hours on,” saying AI can now deliver near‑private‑banking quality advice for accounts in that range. He argued that advisors who wish to remain relevant must develop skills for high‑touch situations such as inheritance disputes, liquidity events and market volatility, where reading family dynamics and managing relationships are required.

Citigroup plans to add staff even as it deploys automation. The bank intends to hire about 400 wealth advisors in its U.S. retail bank and roughly 100 employees for its private bank as part of efforts to expand its wealth business. Citi is building AI-backed software to produce near‑instant portfolio reviews that currently take several hours. Joe Bonanno, head of wealth intelligence at Citi, described a workflow where bankers can “press a button” to have the system draft a chief investment officer email and explain the implications for a client.

Citi is also introducing conversational AI features for affluent customers, including an avatar designed to help with practical decisions such as managing a child’s college fund. Bonanno expects the technology to increase contact and engagement with clients and to support retention.

Other wealth firms are applying AI across advisory teams. UBS reported that about 90% of its U.S. advisory teams use an internal platform to boost productivity, and the firm provides personalized client insights through an in‑house tool. Commercially available models from several providers are being used to model portfolios, optimize tax strategies and explore philanthropic options.

The technology is creating demand for new specialist roles. Patnaik identified needs for behavioral data scientists, personalization architects and human‑in‑the‑loop oversight professionals to manage model outputs, enforce governance and adapt automated advice. He described these hybrid profiles-combining domain knowledge with technical fluency-as among the fastest‑growing and hardest‑to‑fill positions in financial services.

Firms are altering hiring, training and product development to reflect where automated systems handle routine portfolio work and where human judgment is applied for complex family, succession and emotional matters.