AI Agent Use Case: Personalized Investment Advice

This IDC Perspective presents a structured, step-by-step technical overview of the agentic AI process in personalized financial advice provision. Agentic AI represents a step change in the delivery of financial advice, moving beyond static, rules-based robo-advisors to systems capable of reasoning, learning, and adapting autonomously. By integrating structured financial data, unstructured market intelligence, and user-specific preferences, agentic AI can provide hyper-personalized and continuously updated recommendations that scale across client segments. The process framework outlined in this document illustrates how data ingestion, model training, compliance, and user interaction converge into an intelligent advisory cycle. For wealth managers, this promises operational efficiency, expanded client reach, and democratized access to sophisticated advice. At the same time, it introduces new challenges around trust, explainability, and regulatory oversight. "Agentic AI delivers economical, compliant personalization at scale," says Thomas Shuster, research director for IDC Financial Insights. "Advisory will move away from periodic check-ins toward real-time, adaptive, up-to-date personalization."