From reactive to predictive: AI-powered common cause analysis stops problems from spreading.
Over the past year, major telecom outages—from BT in the UK to Optus in Australia and AT&T in the US—have disrupted emergency services, businesses, and millions of users. Common causes? Configuration errors, cascading failures, and slow root cause analysis.
As OPT/NET’s report on significant telecom outages shows, the cost of these failures is rising sharply, with some incidents exceeding $150 million in penalties, lost revenue, and reputational damage.
To prevent such disasters, telecoms must shift from reactive troubleshooting to predictive, AI-powered common cause analysis. Here’s how:
Detect anomalies in seconds, not hours.
In the BT outage, a misconfigured media server caused cascading failures that disrupted emergency call handling for over 10 hours. OptOSS AI could have detected the anomaly within seconds, identifying the “patient zero” device, the chain of events, and the affected interfaces. This level of visibility allows operators to act before customers are impacted.
Correlate across domains to find the real root cause
Modern networks are multi-layered and dynamic. Configuration changes, third-party dependencies, and software-defined infrastructure make it hard to pinpoint the source of a problem. AI-driven platforms like OptOSS MANAGER correlate telemetry, maintenance records, and service impact data to uncover the true common cause, not just the symptoms. This enables faster, more accurate resolution.
Automate response and recovery workflows
Whether it’s generating incident tickets with full context, reverting to stable configurations, or guiding engineers with GenAI-powered assistants, automation is key. In semi-autonomous setups, AI can suggest fixes and validate service restoration. In fully autonomous networks, it can even block risky changes before they’re deployed—stopping outages before they start.
Numo Data perspective:
The telecom industry can no longer afford to rely on outdated, rule-based tools to manage 21st-century networks. AI-powered common cause analysis transforms incident response from reactive firefighting to proactive prevention, reducing Mean Time to Resolve (MTTR), protecting critical services, and building resilient networks that adapt in real time.
