Build an AI-Driven Intelligent Network
In the past, engineers would assure service quality by monitoring a few main KPIs, such as availability, quality, and retention. Today, networks generate too much data for engineers to track manually. By deploying AI, machines can do the hard work of analyzing the data, pinpoint issues, and free up engineers to deal with customer-affecting issues.
As an operator, to continuously deliver your customers with a quality experience, you must ensure an improved network performance in face of these new trends and inherent challenges. This requires a more intelligent and automated network. Artificial intelligence (AI) has a key role to play in helping you to increase network performance, proactively monitor its traffic, and rapidly troubleshoot issues in real-time, by learning from the actual data which is produced by the network to derive insights.
Using AI enables you to analyze the data in order to find patterns and relationships. This information will help set KPIs and thresholds for service delivery. When an anomaly occurs, or the service drops below the predefined threshold, an alarm will trigger, alerting of the issue. This will allow you to manage your networks more effectively, deal with the increasing complexities and enable the dynamic management of the network, leading to a more personalized customer experience for your subscribers.
Automated assurance with built-in AI/ML can analyze the data as it is collected for assurance, offering you several main benefits:
- Rapidly identify performance issues across all services.
- Analyze immense amounts of data generated by the network, which no human can do.
- Solve complex pattern recognition problems and identifies patterns over time and across different data sources.
- Receive alerts if a certain KPI breach occurs.
- Optimize the network in the most efficient way without the need for manual adaptation, accumulating experience over time.
AI and ML algorithms allow networks to operate in a way that far outweighs networks of the past, with updated AI and ML models seamlessly integrated and used for telecom-specific use cases, such as:
Optimizing network performance: use advanced AI tasks such as anomaly detection, to allow engineers to remain focused on handling only critical customer-affecting issues.
Smartly planning network capacity: create a long-term forecast for various performance and quality indicators and use it to plan future network capacity smartly.
Improving the customer experience: utilize AI/ML to gain insights into network encrypted traffic, allowing you to understand the customer’s Quality of Experience (QoE).