How AI can improve your business: Successful AI Use Cases in Telecoms In the Area of Assurance

There is no doubt that the application of AI in the telecommunication industry is a must. As artificial intelligence becomes increasingly accessible, telecoms are reaching for it as a way of automating technological and business processes to gain market advantage. But what advantages can CSPs gain from adapting AI-driven technology?

Telecom AI Use Cases in Assurance

Automated Baseline Generation and Anomaly Detection (ABGAD)
ABGAD monitors key performance indicators, quality performance, and customer satisfaction indices for violations. Clustering violations into anomalies, root-cause analysis of these anomalies, and long-term forecasting optimize the work of telco experts.

Operators' problem: Using previous technologies meant analyzing multiple sources and monitoring hundreds of thousands of parameters.

Comarch solution: Machine learning (ML), a subset of AI, is employed to detect performance indicators that deviate from expected patterns and uncover new patterns consisting of multiple baseline violations. Anomalies can then be prioritized for faster analysis.

Value for the operator: Automating this process increases variable flexibility and helps identify and neutralize disruptions before they impact customer experience.

Automated Situation Detection (ASD)
Unlike typical automated alarms, ASD performs root-cause analysis by clustering alarms into situations and enabling classification for better understanding.

Operators' problem: ML can only remove known causes and events, leaving significant manual clustering work for technicians.

Comarch solution: ASD identifies unexpected events and uncovers correlations automatically. Situations can be prioritized, expediting issue resolution and system capacity.

Value for the operator: ASD boosts productivity and asset utilization by reducing event noise and categorizing work by root causes, simplifying repair and maintenance efforts.

Automated Problem Detection (APD)
APD clusters trouble tickets into problems and performs root-cause analysis, identifying true causes rather than just symptoms.

Operators' problem: Analyzing past events and correlating incidents from multiple sources is tedious and time-consuming.

Comarch solution: ML techniques automate the detection of event sources, minimizing time and effort while improving precision.

Value for the operator: APD proactively detects network issues, linking customer trouble tickets with network faults, reducing team workload without service disruption.

Knowledge Accumulation (KA)
KA creates automated recommendations for anomalies, situations, and problems to streamline processes and maintain consistent quality of service.

Operators' problem: Existing static and outdated knowledge bases require time-consuming updates and maintenance.

Comarch solution: ML-powered systems analyze expert decisions to build a knowledge base that can recommend or automate solutions after expert validation.

Value for the operator: This reduces reliance on specific experts, shortens training time, and facilitates knowledge transfer, ensuring continuity and efficiency.

The Future of AI in Telecommunications

These AI use cases in telecom are just the tip of the iceberg. With ongoing advancements, AI-based services will continue to help telecom operators deliver better service quality while optimizing resource use, paving the way for innovative applications and improved customer experiences.