Define an AI project to triage tickets with useful categories, route, review, and evaluate errors before scaling up automation.
Index
Triage only helps when it takes the call to the person capable of resolving it. Sorting into too many categories can seem sophisticated and still increase transfers. The project must start from decisions that change service, priority or responsible team, using examples from the operation.
Decision this guide helps you make: Route requests with context without automating fragile classifications.
Review taxonomy before template
Overlapping categories and ambiguous names make both human and AI work difficult. Observe recent calls and identify which distinctions really change the treatment. Allow a question or review category. Forcing the model to choose between inappropriate options produces an apparently complete but unreliable classification.
Define errors with different impacts
Forwarding a simple question to another queue does not have the same effect as hiding a critical failure. Create assessment criteria by class and examples of sensitive cases. Priority should not depend solely on alarming words written by the applicant. Rules and review need to reflect known operational impact.
Preserve context for the agent
Deliver summary, reason for classification and source information, without inventing missing data. The person must be able to correct the fate and record the result. This allows you to identify bad categories and changes in demand. Don't automatically use corrections as training without governance over data and purpose.
Implant first as recommended
Compare the suggestion with the team's decision before enabling automatic routing. Measure additional transfers, time to first responder, and relevant errors. If one category works well and another doesn't, only release the validated clipping. An average hit rate can hide a problematic queue.
- Categories associated with real decisions.
- Assessment by type and impact of error.
- Simple, traceable human fix.
A scenario to check out in the demo
Hypothetical example: a message mentions charging, but the real problem is lack of access after payment. Forwarding only by the most frequent word creates additional transfer. The assessment set must contain close topics and enough context to distinguish the responsible team. When information is insufficient, the revision suggestion needs to be an accepted result, not a hidden error.
Briefing to request a proposal
- Categories that change staff, priority or procedure.
- Anonymized sample with correct routing checked.
- Error costs that require review before routing.
Design a pilot with Quantum9
Take anonymized tickets and the team map. Assessment can begin without changing the production flow, producing recommendations in parallel. The objective is to improve referral, not to artificially reduce triage time by shifting errors to care.
Discover the scope of AI and process automation and deepen the context in related guide.