Provide the service customers expect

In addition, cross-domain correlation is currently unachievable by most monitoring solutions utilized by CSPs, since these solutions work in silos

Providing the service that customers expect requires CSPs to create a holistic view across multi-vendor, multi-domain environments for real time detection and correlation of service-impacting incidents. Anodot serves as the brain on top of the OSS, giving CSPs the cross-domain and service experience view they require. Anodot's patented correlation engine correlates anomalies across the network for holistic root cause analysis and the fastest time to resolution, leading to enhanced network monitoring capabilities, improved network availability and customer experience. These capabilities are critical for reducing the typically long time to the detection and resolution of customer impactful incidents, continuous alert storms, and, finally, revenue loss and damaged brand reputation.

AI monitoring technologies have the potential to introduce significant cost savings for CSPs. Based on machine learning and fully autonomous, these monitoring solutions provide high ROI by reducing Time to Detection (TTR), Time to Resolution (TTR), the total number of alerts, and the number of false positives and negatives. Forward thinking CSPs who rely on AI-based monitoring drive operational efficiency, deliver a better customer experience, and prevent critical performance and quality of service issues across the network.

However, for most CSPs, successful adoption and implementation rates of AI monitoring are still low. The main hurdles faced by CSPs are the complexity of the network, limited resources and internal knowledge, and an overwhelming number of potential use cases. In most cases, AI monitoring solutions require heavy investment in setup, data integration, use case development, operation and maintenance — as well as specialized skills typically provided by pricey professional services firms. This results in significantly higher TCO, longer time to value, and slower use case implementation when compared to out-of-the-box solutions.

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