Gen AI IVR Workflow for Real-Time Call Tree Navigation
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Solution Overview
Problem
Existing IVR calling workflows are inefficient, with long hold times, high After Call Work (ACW) times, and redundant calls, leading to resource wastage and decreased efficiency due to manual interventions and complex IVR dialing trees.
Innovation Solution
A system and method utilizing Generative Artificial Intelligence (Gen AI) to optimize calling workflows by automatically generating IVR trees, processing user responses, and generating summary reports, reducing hold times and ACW through automated traversal and minimizing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual IVR dial out process is used, then call operators can handle customer queries, but hold time and After Call Work time increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically fetching user data from multiple sources before the call, generating a master data sheet, and pre-configuring the IVR tree with relevant information. This preparation eliminates the need for manual data gathering during the call, reducing both hold time and After Call Work time while maintaining high productivity.
Solution Approach 2:
The system enables self-service through automated IVR navigation that can independently traverse call trees, automatically generate summary reports, and complete administrative tasks without human intervention. This automation directly reduces hold time by eliminating manual IVR traversal and reduces ACW time by automating post-call documentation.
2Adaptability or versatility
If complex IVR dialing trees are used, then comprehensive service coverage is achieved, but navigation time and operational complexity increase
Solution Approach 1:
The system introduces an intermediary AI component that acts as a smart navigator through the complex IVR dialing trees. This intermediary automatically determines the optimal path through the IVR based on the master data sheet and user responses, maintaining comprehensive service coverage while eliminating the complexity burden from operators.
Solution Approach 2:
The system dynamically changes IVR navigation parameters in real-time based on user responses and call context. The automated IVR traversal adjusts the call tree navigation path, speed, and depth according to the specific call requirements, maintaining versatility while reducing operational complexity through adaptive parameter optimization.
3Reliability
If multiple data sources are queried manually, then comprehensive user information is gathered, but data collection time increases
Solution Approach 1:
The system performs preliminary data collection by automatically fetching information from multiple data sources before the call begins. The master data sheet is generated in advance with comprehensive user information, ensuring data completeness while eliminating time-consuming manual data gathering during the call itself.
Data Source
AI summary
A system and method for Generative Artificial Intelligence (Gen AI) based calling workflow optimization is provided. Historic data associated with user is fetched from multiple data sources and voice commands previously provided as voice prompts over an IVR call tree by the user are fetched for generating master data sheet. Outbound call is generated in the form of Interactive Voice Response (IVR) tree for user by processing master data sheet. Responses provided over outbound call are converted to text in the form of query, via first bot type. Prompt is generated based on text in the form of query and other queries in IVR tree and generated prompt are provided as input to large language model. The large language model identifies category from master data sheet which corresponds to query based on prompt to generate reply to query. Reply to query is inserted in IVR tree in real-time.