The Challenge: A €600k Problem Hidden in the Order Queue
A premier European telecom operator faced a persistent 10% fallout rate in FTTH orders. The most costly pattern—Network Capacity Mismatch—appeared whenever live network inventory deviated from sales feasibility checks, leaving orders stuck for hours or days. Manual investigation across systems was slow, expensive, and inconsistent.
Solution: AI-Powered Fallout Triage Inside ServiceNow
We embedded an AI Recommendation Engine directly in the client’s existing ServiceNow workflows. It ingests historical resolutions, network inventory, feasibility rules, and policy constraints to produce ranked next-best-actions (NBA) with confidence scores and audit trails.
- Auto-classification of fallout types from ticket context and metadata
- Contextual recommendations (e.g., alternate port, area reassignment, or order re-route)
- One-click action via ServiceNow tasks with full logging and rollback
- Guardrails: PII redaction, policy checks, SOC2-friendly observability
Implementation Highlights
- Data: Past fallout tickets, closure notes, inventory snapshots, topology diffs
- Models: Retrieval-augmented generation for reasoning + lightweight rules for safety
- Integration: ServiceNow UI actions, background jobs, and notifications
- Ops: Feature flags, evaluation harness, and live dashboards
Results
- 90% reduction in investigation time — from ~8 hours to under 30 minutes.
- €500,000+ annual operational savings through faster triage and fewer escalations.
- 75% faster end-to-end resolution for complex fallouts (48h → 12h).
- 25% reduction in order cancellations for impacted orders.
- +4 point CSAT recovery for affected journeys.
Why It Works
The engine pairs actionable intelligence with the operator’s real-world constraints. Recommendations are explainable, reversible, and auditable—so teams trust the system and scale it safely.