The team grounded the solution in real advisor workflows, closely examining how AI chat was being used and where inefficiencies surfaced. Discovery and workflow analysis identified key friction points, particularly around meeting preparation and repeated prompt creation.

These insights informed the design of a prompt and conversation lifecycle management system, introducing core capabilities such as:

• Visibility into recent and saved AI prompts

• Searchable AI conversation history

• The ability to save, edit, duplicate, and delete prompts and interactions

The experience was designed for speed and usability, enabling advisors to quickly retrieve prior work and adapt it to new contexts. This reduced the need to start from scratch while enabling more consistent, higher‑quality AI outputs across client engagements.

From a technical standpoint, the solution required integrating structured data and conversation management within the existing AI chat framework while maintaining performance and simplicity. The system was designed to scale alongside growing volumes of interactions without adding cognitive overhead. Iteration focused on ensuring new capabilities enhanced and not disrupted the existing workflows.

The final experience balanced flexibility and clarity, making AI a more practical and repeatable part of daily advisor productivity.