When a fibre cut or a downed server took out service across a region, the telecom's call centre was hit by thousands of queries in the same hour. Agents drowned, customers who only wanted to know when it would be fixed waited hours or got an email after the fault was already resolved, and routine requests jammed in the same queue.
The failures that hurt most were the big ones. A fibre cut or a downed server would take out service across a whole area, and within the hour thousands of customers called, messaged and posted at once. The centre had no way to absorb a spike that size.
Everyone was asking the same thing, and no one was getting an answer. A customer who only wanted to know when the line would be back waited on hold behind everyone else, and often got a reply by email after the fault had already been fixed. The one easy answer, "we know, here is the status," was the hardest to deliver at scale.
And the routine work stalled with it. A simple request to change a billing address or query a charge sat in the same jam as the outage calls, so the storm did not just slow the outage response, it froze everything else the centre did.
Put AI in front of the storm: detect the disruption, target the people living it, and handle the routine work so it stops landing on agents.
The system watches the flow of incoming queries in real time and recognises a disruption spike as it forms, a sudden surge of contacts about connectivity from one area, before a person has even worked out what is happening.
It reads which area each customer belongs to from the CRM, matches it to the fault, and knows exactly who is affected, so the update goes to the people living the outage and not to everyone on the list.
The moment a disruption is confirmed, the system pushes an accurate status, what is down, where, and the expected time to fix, by email, SMS or chat, so most affected customers get their answer without ever reaching a queue.
Everyday requests, a billing-address change, a payment, a subscription freeze, are handled directly by the AI against the billing and operational systems, so they no longer jam behind the outage and no longer need an agent at all.
Nothing risky runs unsupervised, and anything the system cannot resolve is escalated to a person, so the agents who used to read out the same outage status all day are free for the customers with a genuine equipment problem that needs a human.
The assistant is connected end to end to the billing and operational systems, so it does not just describe the outage, it updates records, completes requests and reflects the live repair status, which is what lets it carry the load instead of adding to it.
Peak load absorbed, affected customers informed, agents back on the work that needs them.
A regional outage no longer swamps the contact centre. The system absorbs the surge of queries and keeps affected customers informed in real time, so the company handles sudden jumps in traffic, an outage, a storm, a major sporting event, without adding people, and at a fraction of the cost of handling them by hand.
With the status updates and routine requests handled automatically, technical agents stopped reading out the same message all day and went back to the customers with real equipment problems, and the complaints and churn that used to follow every outage eased.
A contact centre falls over in a disruption because a spike of identical questions all lands on humans at once. The value here is an AI that sees the spike forming, works out exactly who is affected, answers them in real time, and handles the routine requests itself, with a person on anything that carries risk, so peak load is absorbed instead of passed on. It is the same agentic discipline behind our agentic automation work, aimed at the outage storm rather than everyday self-service.
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Reef TRH