Morgan Stanley Wealth Management deploys OpenAI-powered AI Assistant to drive advisor efficiency at scale

Morgan Stanley Wealth Management's Financial Advisors serve clients using the firm's extensive intellectual capital, which covers individual companies, sectors, asset classes, capital markets, and global regions. The firm wanted to help advisors and their teams work with this content and serve clients more efficiently.

Morgan Stanley developed the AI @ Morgan Stanley Assistant, a proprietary internal tool built on OpenAI's large language model technology. The system was developed specifically for the firm's Financial Advisors and uses OpenAI's technology to access, analyze, and synthesize Morgan Stanley's extensive intellectual capital. Advisors and their teams interact with the tool using natural language questions, receiving consolidated responses that include resource links. The assistant uses LLM and natural language processing capabilities to deliver real-time insights, answer process-driven questions, and surface relevant information. The tool has been deployed across the firm's financial advisor base.

The AI @ Morgan Stanley Assistant has helped drive efficiency and scale across Morgan Stanley's Financial Advisor base, and Morgan Stanley was the first wealth manager to adopt and deploy OpenAI's technology. Morgan Stanley won the 2024 Celent Model Wealth Manager Award for Essential and Emerging Technologies, awarded in March 2024 by Celent. The tool aims to:

• Streamline advisor workflows through consolidated responses
• Improve efficiency
• Enhance the overall client-servicing experience

• Proprietary grounding matters: grounding a general-purpose LLM in firm-specific intellectual capital — rather than public data — is what makes AI output trustworthy and advisor-ready.
• First-mover status can draw recognition: being the first wealth manager to adopt and deploy OpenAI's technology was followed by third-party recognition.
• Design for augmentation, not replacement: the tool enhances advisor capacity for client servicing rather than automating the advisory relationship itself.
• Advisor-specific design matters: natural language interaction lets advisors and their teams ask questions and receive a consolidated response with resource links.