JPMorgan Chase

Wealth and asset management advisors at JPMorgan Chase faced significant time burdens from manual research, document summarization, and report drafting. Generating investment ideas, market insights, and personalized client reports often took hours or days, limiting time for client interactions and strategic advising. Private Bank advisors specifically struggled with preparing for client meetings and creating tailored recommendations amid regulatory scrutiny and data silos.

JPMorgan developed LLM Suite, an internal platform of seven fine-tuned large language models integrated with secure proprietary data infrastructure, enabling advisors to draft reports, generate investment ideas, and summarize documents rapidly. A specialized tool called Connect Coach was built for Private Bank advisors to assist with client preparation, idea generation, and research synthesis. Rollout was progressive with governance frameworks, AI hackathons, and 'learn-by-doing' training programs.

LLM Suite was deployed to 140,000 employees firm-wide, scaling from 60,000 users in mid-2024 to 140,000 by late 2024. Over 450 proofs-of-concept were developed across operations, with wealth management as a priority vertical. The initiative is projected to deliver up to $2 billion in AI-driven productivity upside, with advisors reporting faster document summarization and report drafting that frees time for high-value client interactions.

• Secure, internally governed AI infrastructure is essential when deploying LLMs at a regulated financial institution — data privacy and compliance guardrails must be built in from the start.
• Progressive rollout with rigorous ROI measurement across 450+ use cases allowed JPMorgan to validate value before scaling firm-wide.
• 'Learn-by-doing' programs and AI competitions drove organic adoption and surfaced high-impact use cases across business units.