AI outsourcing
Routing support contacts to the right place with AI triage
An Irish telecommunications operator was misrouting support contacts. AI-assisted triage read intent up front and sent each contact to the right team first time.
Results at a glance
- 92%
- Routing accuracy
- +19 pts
- First-contact resolution
- -71%
- Repeat transfers
- -24%
- Average handling time
up from 67%
The challenge
The operator handled a heavy mix of billing, connectivity, device and sales contacts across phone, chat and email. Routing depended on a customer picking the right menu option or an agent guessing from a first line, and both were wrong often enough to matter. Misrouted contacts bounced between teams, and every transfer added wait and repetition.
Volumes were also spiky. Outages and promotions produced surges that the fixed routing rules could not absorb, so queues for the wrong teams grew while trained agents elsewhere sat idle. The operator wanted intent understood at the point of contact, not several handovers later.
Pain points
- Around a third of contacts were routed to the wrong team on first attempt
- Repeat transfers pushed up handling time and frustrated customers
- First-contact resolution stalled because agents lacked context on arrival
- Menu-based routing could not adapt to outages or promotional surges
Our approach
- Built an intent classification model over historic contacts, tagging each into billing, connectivity, device, sales and retention categories with a confidence score
- Placed AI-assisted triage at the front of every channel so contacts were routed on predicted intent, with low-confidence cases sent to a human reviewer
- Passed a short structured summary to the receiving agent so context arrived with the contact rather than being re-gathered
- Ran a continuous quality-control loop, sampling classifications weekly and retraining the model as language and product mix shifted
Results at a glance
The results
- Routing accuracy rose to 92 percent, so most contacts reached the right team on the first attempt.
- First-contact resolution improved markedly as agents opened each contact with intent and context already in hand.
- Repeat transfers fell by more than two thirds, shortening handling time across every channel.
Contacts land where they should, and the agent already knows why the customer is calling. The bouncing around simply stopped.
Common questions
Low-confidence contacts are sent to a human reviewer rather than routed on a guess. Those cases also feed the retraining loop, so the model keeps improving where it is weakest.
These are representative engagements. Client identities are anonymised and the metrics are illustrative of the type of results this work delivers, not audited figures for a named client.
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