Traditional customer routing uses menus and categories chosen by the customer. Customers frequently choose the wrong category, resulting in transfers, repeated explanations, and longer resolution times. The content of the message tells you more about where it should go than the category the customer selected.

The Problem

IVR menus and web form dropdowns force customers to self-diagnose their issue category. A customer with a billing error on a technical product might choose “technical support” or “billing” depending on how they frame the problem. Either choice leads to at least one transfer.

The AI Approach

An LLM can read the customer’s message, understand the actual issue regardless of how it is framed, and route it to the agent or team best equipped to resolve it on the first contact. This content-based routing reduces transfers and improves first-contact resolution.

Three-Step Build

Step 1 - Team mapping. Define your routing targets with their expertise areas and the types of issues each team handles best. Include edge cases and overlap areas.

Step 2 - Content analysis. When an inquiry arrives, send the message text to an LLM with the team mapping. The model identifies the issue type, complexity level, and any specialized knowledge required, then recommends the best routing target.

Step 3 - Skill-based assignment. Within the target team, assign to the agent with the best skill match and current availability. Include a brief AI-generated summary of the issue so the agent can start helping immediately without re-reading the full message.

Where It Breaks

Messages that combine multiple issues need a primary routing decision that may not fully address secondary issues. Very brief messages lack enough context for accurate routing. Agent skill profiles must be maintained as team capabilities change.

The Production Path

Measure transfer rates and first-contact resolution before and after AI routing to quantify improvement. Build a feedback mechanism where agents flag misroutes to improve classification. Start with email and chat where full message text is available; expand to voice after integrating transcription.