THE TELCO INTERVIEW
For the end-user, these behind-thescenes optimisations translate to a demonstrably superior experience: an AN continuously monitors itself and makes automated adjustments to ensure optimal performance.
It translates to“ faster speeds, lower latency and improved reliability.” Furthermore, by identifying and resolving network issues before they escalate, the system“ minimises service disruptions and improves satisfaction.” The ultimate goal is a seamless, uninterrupted and high-quality service that builds customer loyalty. enabling pre-emptive maintenance and preventing costly outages. It not only minimises downtime but reduces the need for expensive, reactive and often overtime-heavy repair efforts.
“ Reducing operational and maintenance( O & M) costs are among the top priorities for CSPs across all regions, as they aim to improve operational effectiveness,” Erez confirms. ANs contribute by optimising resource allocation – such as bandwidth and power – in real-time, improving efficiency and directly lowering operational costs, particularly energy consumption.
The power of prediction: from reactive to proactive management The shift from a reactive to a predictive operational model is one of the most transformative promises of AI. Instead of waiting for an alarm to signal a failure, CSPs can now anticipate and prevent issues from happening in the first place.
Erez provides concrete examples of how European CSPs are leveraging these technologies. One powerful application is in predictive maintenance. By analysing vast amounts of network data, AI algorithms can“ predict equipment failures, for example, base station transceiver replacements scheduled during off-peak hours, enabling proactive maintenance and preventing outages.” The simple change in timing minimises service impact and improves efficiency.
When issues do arise, real-time anomaly detection and accelerated root cause analysis dramatically reduce the Mean Time To Repair( MTTR), improving
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