Customer Support AI Triage System
Problem Statement
Customer support response time averages 48 hours for enterprise clients, resulting in a 23% churn rate among accounts worth $85K+ annually. The current manual triage process routes 60% of tickets to the wrong team on the first attempt. Over the past 12 months, delayed responses have contributed to the loss of 12 enterprise accounts ($1.02M in annual recurring revenue).
Success Criteria
- 1.Reduce average first-response time from 48 hours to under 4 hours within 90 days of launch
- 2.Increase first-contact resolution rate from 38% to 65% by end of quarter 2
- 3.Reduce enterprise client churn from 23% to under 12% within 6 months of deployment
- 4.Achieve 85% ticket routing accuracy (up from 40%) within 60 days of launch
Scope Boundaries
In Scope
- +Build AI-assisted ticket classification and routing for email and portal channels
- +Integrate with Zendesk API for ticket ingestion and response delivery
- +Train classification model on 24 months of historical ticket data (42,000 tickets)
- +Deploy to production with staged rollout (10% > 50% > 100% over 3 weeks)
Out of Scope
- -Do not build phone or live chat integration (Phase 2, pending Phase 1 results)
- -Do not replace existing Zendesk platform (integration only, no migration)
- -Do not automate response generation (classification and routing only)
- -Do not process tickets from the free-tier customer segment
Risk Register
| Risk | Prob | Impact | Severity |
|---|---|---|---|
| Historical ticket data quality insufficient for model training | Medium | High | High |
| Zendesk API rate limits restrict real-time routing during peak hours | Low | Medium | Low |
| Enterprise clients reject AI-routed tickets as impersonal | Medium | Medium | Moderate |
Key Milestones
Week 2
Data audit and model requirements
Week 8
Model training and validation
Week 12
Staged production rollout
Week 16
Full deployment and monitoring