The telecommunications industry is acutely characterized by ever-growing competition, continually increasing customer demands, and quickly evolving technologies. This makes artificial intelligence (AI) and machine learning (ML) precisely the force multiplier operators need.
By harnessing the transformative capabilities of 5G AI analytics, operators are better positioned to meet the challenge with enhanced customer experiences, improved productivity, streamlined operations, and reduced costs.
Among the compelling AI/ML use cases are:
- Personalized customer support by providing the insights care agents need to help customers accelerate the resolution of their specific network issues.
- Predictive sales and marketing by identifying customer preferences and usage trends for optimized campaigns, targeted content, and diverting spend to the channels that will most likely return the investment.
- Operational efficiency by analyzing data about how network equipment and devices are used and how to optimize resources and plan for capacity.
As we can see, the value of AI/ML for telecom operators cannot be overstated. This technology enables them to save time and money, drive innovation, keep customers happy, and drive growth.
At the heart of it all
Sounds great, indeed. But before moving full speed towards capturing the opportunity, we first need to ensure we’re taking care of what’s at the heart of the operator’s business – the network.
If the network isn’t working like it should, none of the upside is possible. In our always-on digital world, seamless and reliable connectivity is at the very core of what the telecom operator is all about.
This is why, first and foremost, the mandate of AI/ML for operators is service assurance – to help them ensure that the network is performing as it should by:
- Finding service degradations and resolving them across the network from the RAN to the core.
- Identifying trends, patterns, and outliers that indicate issues and prioritizing them based on how they impact the subscriber experience.
- Detecting the source of an alarm and whether it came from the root cause of the issue or the result of a failure.
- Determining the required response and accelerating corrective actions for faults and irregularities.
- Optimizing network operations by learning trends and detecting anomalies immediately as they occur (for example, in the NOC as covered in a previous blog post).
How AI/ML is fueling service assurance
The need for such AI/ML-driven capabilities has never been greater. The advent of 5G with hybrid architectures and new technologies introduces a slew of new challenges. And the move to virtualized, cloud-based networks is accelerating even further the need for a real-time understanding of network performance.
The good news is that here, too, AL/ML can be very powerful for addressing even the most formidable of service assurance challenges.
| Anomaly Detection | Root Cause Analysis | Analytics |
| AI/ML can learn the normal behaviors of the network, analyze patterns in real time, and detect anomalies that indicate potential faults and failures. | AI/ML algorithms analyze massive quantities of data that no human can, identifying where and why network errors have occurred. | By analyzing historical data, AI/ML can help operators anticipate network anomalies and potential failures to minimize downtime. |
But it’s not just AI/ML-powered anomaly detection, root cause analysis, and analytics that will do. What telecom operators need for superlative service assurance is:
- Anomaly detection that’s KPI-based
- Root cause analysis that’s automated and comprehensive
- Analytics that’s predictive and drives closed-loop operations
How RADCOM can help
Anomaly detection that’s KPI-based.
With AI/ML-driven KPI-based anomaly detection, operators can receive a near real-time alert regarding a sudden degradation in network performance.
When an anomaly is detected, automated network actions can be triggered immediately. This frees engineers from monitoring every KPI independently and directing their focus to high-priority tasks.
Comprehensive, automated root cause analysis
RADCOM enables automated root cause analysis with AI/ML-powered insights for network, session, packet, and protocol-based analysis, intra and inter-protocol correlations, and more.
Operators can streamline the engineering resources required as they gain an end-to-end correlated view of subscriber and network sessions and drive proactive resolution of customer-impacting issues.
Predictive analytics for closed-loop operations
The RADCOM offering also enables operators to predict with unparalleled accuracy which specific device, cell site, or network function will fail within a certain time frame. This way, they can perform predictive maintenance, take preventative actions, and allocate the required resources for mitigating failures and correcting issues before they occur.
In conclusion
One of the telecom operator’s most critical imperatives is ensuring and optimizing network performance. This is why ensuring the network delivers on its performance promise is topping the list for telecom operators’ AI/ML investments worldwide.
With real-time AI/ML-powered service assurance, they can achieve their most strategic goals and drive the automated network to accelerate network issue resolution, grow engineering teams’ efficiency, and take service quality to unprecedented new heights.
This is what being proactive, preventative, and predictive is all about.
If you want to learn more about how RADCOM can help you meet your toughest service assurance demands, download our AI/ML-based solution brief
