AI Ticket Routing Engine for Agent Matching Accuracy
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Solution Overview
Problem
Existing systems for routing work tickets to qualified agents in technical support organizations often fail to identify suitable agents or groups, leading to increased resolution times or unsatisfactory outcomes.
Innovation Solution
A ticketing engine with a ticket assignment module that employs artificial intelligence-based modeling, using group mapping and agent mapping models trained on historical ticket and agent performance data, to assess and route work tickets to qualified agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing routing procedures are used to assign tickets to agents, then the system attempts to route requests to qualified agents, but the system fails to identify a suitable agent or group, leading to increased resolution time
Solution Approach 1:
The system implements feedback loops where ticket assignment outcomes (successful resolution vs. reassignment) are fed back into the machine learning models. This allows the system to learn from past assignments and improve future routing decisions, progressively enhancing agent qualification matching accuracy while reducing resolution time through iterative optimization
Solution Approach 2:
The patent replaces traditional rule-based mechanical routing systems with machine learning models that can process and analyze multiple ticket attributes and agent characteristics simultaneously. This substitution enables more precise matching of ticket requirements to agent qualifications, resolving the contradiction between matching accuracy and resolution speed
2Productivity
If tickets are routed to unqualified agents, then assignment speed may increase, but the agent may route the ticket back to the dispatcher or another agent, creating delays
Solution Approach 1:
The system performs preliminary analysis of ticket attributes and agent qualifications before making assignments. By pre-processing and evaluating multiple potential assignments using machine learning models, the system ensures that tickets are routed to qualified agents on the first attempt, preventing reassignments and reducing total resolution time while maintaining high assignment speed
3Productivity
If agents are assigned to tasks beyond their expertise, then more tickets can be distributed, but agent morale decreases and service quality reduces
Solution Approach 1:
The system applies local quality by matching specific ticket requirements with corresponding agent expertise areas. Rather than distributing tickets uniformly, the machine learning models analyze ticket characteristics and assign them to agents with specialized skills in those domains, ensuring high service quality while maintaining efficient ticket distribution across the agent population
4Productivity
If agents are overwhelmed with ticket assignments, then more tickets can be handled overall, but the agent lacks availability to promptly address issues and may route tickets back
Solution Approach 1:
The system implements dynamic ticket assignment that adapts to real-time agent availability and workload conditions. Machine learning models continuously evaluate agent capacity and adjust assignments accordingly, distributing tickets to maintain optimal response speeds while maximizing overall system throughput. This dynamic approach prevents agent overload while preserving prompt issue resolution
Data Source
AI summary
Techniques for managing work tickets, including receiving a work ticket including a textual description of a technical issue to be resolved, determining, based on the textual description, ticket data including an issue description indicative of the technical issue to be resolved, determining, based on application of the issue description to a group mapping model, an agent group corresponding to the technical issue, determining, based on application of the agent group to an agent mapping model, an agent of the group for resolving the technical issue, and providing, in response to determining the agent of the group for resolving the technical issue, the work ticket to the agent.