The work of a 34-person service team, absorbed by one AI agent
The Situation
A European automotive group served more than 4,500 dealerships across multiple markets. Every service request from a dealer routed through a central support team of 34 people.
The team was capable. The volume was not survivable. Requests queued for days, dealers escalated through account managers, and the official response SLA sat at five days. Every one of those days was a dealer waiting to serve a customer.
Why it was hard
The obvious fix, hiring more people, scales cost linearly with volume and solves nothing structurally.
The harder problem: the answers lived across fragmented dealer systems and a 700TB data estate that was mid-migration to the cloud. Any solution had to work during the migration, not after it. And it had to answer in the languages of every market it served.
What Was Built
An AI service agent integrated directly with the dealer network systems, built and shipped by a team of 23 engineers on Azure.
The agent reads the request, retrieves the answer from the systems the human team used, and responds in the dealer language. Requests it cannot resolve route to a human with full context attached, so escalation starts warm instead of cold.
The 700TB migration ran in parallel and completed without breaking service.
The Results
What This Means For You
If your team is buried in repetitive requests and the answers are locked in your systems, you are sitting on the same opportunity. The only question is whether your data is ready for an agent to use it. That is a two-week answer, and it is exactly what the audit gives you.
Not sure where you stand? Take the free AI Readiness Calculator →