GenAI Call Script Generation for Wealth Advisor Meeting Preparation
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Solution Overview
Problem
Wealth advisors face challenges in leveraging redundant content from meetings with multiple clients, as current technologies do not provide a way to utilize this overlapping information effectively.
Innovation Solution
An apparatus and method that utilize generative artificial intelligence (GenAI) to identify goals from user conversations, associate these goals with other users, generate call scripts based on the identified goals, and integrate these scripts into digital calendars, thereby optimizing the discussion of redundant content across multiple client meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wealth advisors manually review and prepare content for each client meeting, then the quality and personalization of client discussions can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically analyzing past meeting content, identifying key topics and assets discussed, and pre-organizing this information into reusable templates before meetings occur. This allows advisors to quickly access and adapt pre-prepared content rather than creating everything from scratch during each meeting preparation phase.
Solution Approach 2:
The system creates copies of effective content from previous meetings and adapts them for current client interactions. By copying proven discussion points, asset presentations, and strategic recommendations from past successful meetings, advisors can efficiently reuse high-quality content while customizing it for each specific client context.
2Reliability
If wealth advisors discuss the same assets and strategies with multiple clients, then consistent advice can be provided, but the redundancy of content increases meeting preparation time
Solution Approach 1:
The system creates universal content templates that can serve multiple clients simultaneously. By identifying common assets, strategies, and discussion points that appear across multiple client meetings, the system generates reusable content modules that maintain consistency in advice while reducing the need to recreate the same information for each client.
Solution Approach 2:
The system merges redundant content from multiple client meetings into consolidated, optimized discussion templates. By combining overlapping information about similar assets and strategies discussed across different client interactions, the system creates comprehensive yet concise content that maintains consistency while eliminating unnecessary repetition.
3Productivity
If wealth advisors leverage redundant content from previous meetings, then productivity increases, but the risk of providing generic rather than personalized advice increases
Solution Approach 1:
The system applies local quality by taking standardized, reusable content templates and customizing them with client-specific information, preferences, and contextual details. Each generic template is locally adapted to match the individual client's portfolio, goals, and communication style, ensuring personalization while maintaining the efficiency of template-based content generation.
Solution Approach 2:
The system dynamically changes parameters such as client name, portfolio values, risk tolerance levels, and specific financial goals when adapting reusable templates. By automatically adjusting these parameters based on each client's unique profile, the system transforms generic content into personalized advice while maintaining the structural efficiency of template-based generation.
Data Source
AI summary
An example operation may include one or more of displaying a report on a user interface of a software application on a user device, listening to a call between a user on the user device and a different user on a second user device that is connected to the user device via a network, executing a generative artificial intelligence (GenAI) model based on content that is heard during the call and content within the report displayed on the user interface to identify content within the displayed report that is discussed during the call, and modifying the displayed report to emphasize the identified content within the displayed report on the user interface.