Real-Time Generative AI Call Center Issue Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Call centers often face delays in connecting customers with agents due to long playbooks, agent inexperience, and lack of immediate information, leading to inefficient assistance.
Innovation Solution
Implementing a server computer system with a processor, communications module, and memory that uses trained machine learning models and generative AI to monitor calls, identify issues, provide real-time responses, and manage call queues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If call centers use traditional playbooks and manual agent assistance, then agents can provide help to callers, but the process becomes delayed and inefficient due to long playbooks, agent inexperience, and lack of immediate information
Solution Approach 1:
The patent introduces an intermediary system comprising a speech-to-text converter, machine learning model, and generative AI model that acts as a mediator between the caller and the agent. This intermediary automatically converts speech to text, identifies caller issues, and generates appropriate responses, eliminating the need for agents to manually search through long playbooks and reducing call delays.
Solution Approach 2:
The system enables self-service by allowing the generative AI model to automatically generate responses to caller issues without requiring agent intervention for every query. The AI model draws from the call center playbook and historical data to provide immediate, accurate responses, freeing agents to handle more complex situations and improving overall productivity.
2Loss of information
If call centers provide comprehensive playbooks with all necessary information, then agents can access complete information, but the playbooks become long and complicated, making it difficult for agents to find relevant information quickly
Solution Approach 1:
The patent extracts the essential information from comprehensive playbooks and historical call data, feeding it into the machine learning and generative AI models. This extraction process transforms the raw playbook content into structured knowledge that the AI model can quickly access and apply, maintaining information completeness while eliminating the need for agents to navigate complex, lengthy documents.
Solution Approach 2:
The patent replaces the mechanical system of manual playbook searching with an automated AI-based information retrieval system. The generative AI model automatically queries the playbook and historical data using natural language processing, substituting the manual mechanical process of flipping through pages with an intelligent, automated information retrieval mechanism that provides instant results.
3Reliability
If call centers rely on agent experience and knowledge, then quality assistance can be provided, but not all agents have prior experience with particular issues or immediate access to all requisite information
Solution Approach 1:
The patent creates a universal AI assistant that serves all agents regardless of their individual experience levels. The generative AI model is trained on diverse call center data and can adapt to various types of caller issues, providing consistent, high-quality assistance to all agents. This universal system compensates for individual agent knowledge gaps and ensures uniform service quality across the entire call center.
Solution Approach 2:
The system performs preliminary actions by pre-training the machine learning and generative AI models on extensive call center playbooks and historical call data before deployment. This preliminary training equips the AI with comprehensive knowledge and experience, enabling it to provide expert-level assistance immediately upon deployment, eliminating the need for agents to accumulate years of experience before handling complex issues.
4Productivity
If more agents are hired to handle increased caller volume, then more callers can be assisted, but the cost and complexity of managing larger agent teams increases
Solution Approach 1:
The AI system provides self-service capabilities that allow it to independently handle routine caller issues without requiring additional human agents. By automating the response generation and call management processes, the system increases caller handling capacity while avoiding the organizational complexity associated with hiring and managing larger teams of human agents.
Data Source
AI summary
The present disclosure relates to systems and methods for enhancing call center features using generative AI. There is provided a server computer system, comprising: a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores a playbook of a call center and instructions that, when executed, configure the processor to monitor a call in real-time during the call with a caller, identify, from the call, a caller issue in real-time using a trained machine learning model, obtain a response to the caller issue based on execution of a generative artificial intelligence (GenAI) model and the playbook stored in the memory, and implement the response during the call.