Human + AI triage model for customer operations

High-volume customer operations team supporting service incidents and billing enquiries across multiple inbound channels.

Experience Pattern

High-volume customer operations team supporting service incidents and billing enquiries across multiple inbound channels.

Two operations specialists reviewing an AI-assisted customer-service routing flow.

Challenge

Simple and complex cases were entering the same queue, supervisors were spending too much time reassigning work, and backlog growth was masking service risk.

Approach

  1. 01

    Designed an AI-assisted triage model to classify common enquiries and surface missing information before assignment.

  2. 02

    Separated standard work from exception paths with explicit confidence thresholds, escalation rules, and supervisor oversight.

  3. 03

    Added dashboards for queue mix, rework, backlog ageing, and AI-assisted routing performance.

Improvement focus

Lower backlog pressure by separating standard work from exception paths.
Better first-touch handling through clearer triage and missing-information checks.
Less supervisor effort spent manually reallocating work between queues.

Before and after pattern

Backlog

Before: Standard and complex work mixed in the same queue

After: Routine work and exception paths managed separately

First-touch resolution

Before: Incomplete information discovered after assignment

After: Missing information surfaced before work is routed

Supervisor reallocation effort

Before: Supervisors manually rebalanced queues

After: Queue analytics and routing rules reduced manual reassignment

This representative pattern shows how separating routine work from exceptions can reduce backlog pressure while keeping supervisors focused on higher-value review.

Tools and technologies

AI triageKnowledge orchestrationHuman-in-the-loop reviewQueue analytics