Communication Service Providers (CSPs) are making AI deployments an immediate priority to improve service experience for customers and reduce operational costs, an Anodot survey reveals.
67% of survey respondents have already deployed AI in their networks and more than 50% of those who have not deployed AI plan to do so within the next 6-24 months. Furthermore, 53% of respondents stated that improving service experience was the primary driver for implementing AI-based network monitoring and detection.
These findings are based on an independent Heavy Reading global survey of nearly 100 senior networking and IT CSP decision makers in Q3 2021.
“Instead of waiting for next generation 5G network deployments to invest in AI, the majority of CSPs are already deploying AI on 4G networks now, the infrastructure most of their customers still use,” said Anodot CEO David Drai.
“With AI-based network monitoring, CSPs can detect network issues up to 80% faster and reduce incident costs by as much as 70%.”
CSPs using AI to better monitor their networks
At the same time, CSPs want to better use AI to monitor their networks as a whole and measure the quality of service they provide – rather than just monitoring network-related KPIs and minimizing network downtime, according to survey findings.
In fact, 46% of survey respondents stated that network performance troubleshooting, early warnings and visibility into service degradations are critical to improving customer experience and reducing customer churn. However, most CSPs lack the specific tools that integrate with AI, existing data, and tools to perform more effective, holistic network monitoring.
According to the survey, 42% of respondents cite integration with existing tools as the biggest barrier to deploying AI-based network monitoring and anomaly detection in networks.
Service providers are looking for solutions that deliver a short time to value with easy integration and open APIs. They are also seeking solutions that are easy for teams to use and support without requiring significant investment in data science talent and professional services.
CSPs seek to reduce operational time
The survey also found that more than half of respondents found that the cost of hours spent by their operations teams on monitoring service degradation is just as high as the cost of repairing the issues, which includes hardware fixes, third-party services, and truck roles. Therefore, CSPs seek to reduce the time their operations teams spend on detecting, analyzing, and understanding the root cause of network issues, with 49% of survey respondents stating that predictive impairment detection would deliver the largest cost savings for their organizations.