Dynamic Contact Center Routing with AI-Driven Context
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Solution Overview
Problem
Traditional contact center routing systems rely on static, pre-configured models that fail to adapt efficiently to changing customer needs and do not fully utilize the expertise of knowledge workers, leading to suboptimal routing of communications.
Innovation Solution
An augmented routing system that uses AI and ML to dynamically categorize work items based on context information, allowing knowledge workers to customize their views and actions, and continuously improve routing processes by learning from their interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static/pre-configured routing models are used, then routing decisions are consistent and predictable, but the system cannot adapt efficiently to changing customer needs and knowledge worker expertise
Solution Approach 1:
The patent implements dynamic routing by allowing knowledge workers to create and modify work item categories and routing rules in real-time based on their expertise and changing customer needs. The system transitions from static pre-configured routing to dynamic adaptive routing where categories and rules can be created, modified, and retired without system reconfiguration, enabling the routing system to adapt efficiently to new scenarios while maintaining operational simplicity.
Solution Approach 2:
Knowledge workers are empowered to self-serve by creating their own work item categories, defining routing rules, and managing their preferred routing methods without requiring system administrator intervention. This self-service capability allows the system to adapt to changing needs through the expertise of front-line workers while keeping the overall system architecture simple and manageable.
2Ease of operation
If static routing models are used, then the system is easy to manage, but modifications must be made retroactively based on historical data and are difficult to implement quickly
Solution Approach 1:
The system performs preliminary actions by having knowledge workers define their preferred routing methods and work item categories in advance through an intuitive interface. When routing decisions are needed, the system already has pre-defined categories and rules ready to apply, eliminating the need for retroactive modifications and historical data analysis. This allows rapid adaptation to new routing scenarios without time-consuming reconfiguration.
Solution Approach 2:
Knowledge workers can independently create and modify work item categories and routing rules without requiring system administrator intervention or complex configuration processes. This self-service capability dramatically reduces the time needed to implement routing changes while maintaining ease of management through a user-friendly interface that guides workers through category creation and rule definition.
3Productivity
If traditional routing systems are used, then routing decisions are automated, but the system does not fully utilize the expertise of knowledge workers
Solution Approach 1:
The system incorporates feedback loops where knowledge workers review automated routing decisions, add context information to work items, and modify categories and rules based on their expertise. This feedback mechanism ensures that human knowledge continuously improves and refines the automated routing process, maximizing productivity by combining algorithmic efficiency with human expertise in category management and rule definition.
Solution Approach 2:
The system acts as an intermediary between automated routing algorithms and human knowledge workers. It captures and structures the expertise of knowledge workers into work item categories and routing rules, then applies these human-defined frameworks to automate routing decisions. This intermediary role allows the system to fully utilize human expertise while maintaining high levels of automation in the actual routing execution.
Data Source
AI summary
Dynamically routing and re-evaluating a work item based on actions taken on the work item (e.g., adding context information). The augmented routing system categories a work item into one or more dynamic work categories and identifies active knowledge workers and/or knowledge articles based on the work categories. The work item is displayed in a dynamic knowledge worker view, which allows the knowledge worker to take action on the work item. The actions a knowledge worker may take are based on permissions of the knowledge worker, one of the actions that a knowledge worker may take on a work item is to add context information to the work item. After an action is taken on a work item, the system re-evaluates the work item, which may result in the work item being added/removed from one or more work categories; and added/removed from one or more dynamic customized knowledge worker views.