Reducing Customer Support Friction Through AI + System Integration
Context:
A specialized customer support team at a large consumer technology company handled all inquiries for a unique product line using standalone tooling. Common issues were being routed through general support before reaching the specialized team, increasing time to resolution by more than 50%. The specialized tools were not integrated with the company’s broader support systems, creating unnecessary transfers and fragmented knowledge.
Problem:
The core issue was structural inefficiency: the support model and tooling were misaligned with the actual patterns of customer inquiries, creating avoidable handoffs and slow resolutions.
What I Did:
Conducted a deep dive on all inbound inquiry types and mapped the most frequent, repetitive issues
Identified which use cases could be resolved instantly through an AI-powered support agent
Designed a path to integrate the specialized tooling directly into the main customer support system
Created a support model enabling any trained agent to handle common issues without requiring escalation
Outcome:
Time to resolution dropped significantly as high-frequency issues shifted to automated resolution and trained general agents. The specialized team focused only on true edge cases, reducing cost-to-serve while improving customer satisfaction.
What this shows:
AI and system integration can eliminate structural inefficiencies when teams align on the pattern of work—not the legacy org chart.