The telecommunications industry is leading the adoption of artificial intelligence-based technologies ‚confirmed by the launch of Siri, Alexa, Augmented Reality (AR), object recognition, and many similar features. Telecoms are not just communication service providers (CSPs) anymore, but digital service providers. With users doubling up as content creators, there is an ever-increasing appetite for faster, better, and more reliable connections. This unique aspect pitches the need for more efficient networks, better bandwidth handling, improved media content management, etc. All while, telecoms navigate the tricky waves of 5G, security, and connected things.

How can AI make a real impact?

AI-based solutions provide immediate feedback, enabling telecoms to make decisions in real-time. User consumption patterns hold the key to many deep insights that telecoms can gather using AI.

The five key areas where AI can make a difference in telecoms operations are:

1. Network Operations Monitoring

With the adoption of Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) along with AI, we can bring intelligent network operations and control to a different level. Analyzing traffic in real-time gives plenty of opportunities to plan and react for capacity demands, predict network congestion, and design better network capabilities, thereby enhancing the customer experience.

From a networking perspective, NFV and SDN can provide a common place for network configuration and a programmable interface for integrating AI. AI integration at this central place helps detect, predict, and take control. Network statistics can be collected, and the information then used for continuous network improvement and developing an AI-optimized network strategy.

AI and Machine Learning (ML) based automation tools can leverage NFV/SDN network infrastructure metrics to monitor unusual activity.These metrics can be processed to provide practical insights and solutions within the ambit of operator-defined policies. Network events can be predicted. Their impact on infrastructure can be constantly assessed and automatically responded to with preventive and/or corrective actions before the issue reaches the customer. Hereby, “closing the loop,” without any human intervention.

A network management platform with a built-in AI engine can perform:

Multi-layer fault correlation
Real-time network fault positioning
Real-time prediction
Fault diagnostics
Anomaly detection
Predict capacity demands
Predict network congestion
Root cause analysis

2. Predictive Maintenance

With the help of AI, we can monitor the condition of network equipment and infrastructure. By analyzing historical patterns, telecoms can accurately predict network outages and work out pre-emptive remedial action. This makes it much more cost and time-efficient in comparison to corrective maintenance.

AI-based solutions can monitor network health and infrastructure to predict network incidences and predict equipment failure and faults. Real-time and historical data from power lines, cell towers, data center services, and user consumption can be used for such insights.

3. Network Security and Fraud Mitigation

Telecom is the most vulnerable among other industries, suffering the largest financial losses due to breaches in their cybersecurity systems. Conventional telecom security systems only identify commonly occurring issues but fail in detecting or predicting potential future threats.

Telecom fraud happens in various forms such as subscription, voicemail fraud, identity theft, international revenue sharing fraud and vishing or voice phishing calls.

Telecommunications data includes highly sensitive information such as source number, destination number, call duration, call type, geography, region, and account billing information. AI can monitor this large volume of streaming data and instantly identify scams that appear in the garb of anomalies.

AI and ML can help in fraud prevention and detection by identifying transactions that are likely to be fraudulent, distinguishing them from legitimate ones, and detecting false positives. ML algorithms and neural networks can be used to identify fraud patterns by monitoring subscriber behavior. AI is used on data to learn patterns that best reflect legitimate behaviors or to discover outliers that show previously undetected forms of fraud.

4. Customer Service, Chatbots and Virtual Assistants (VAs)

Although AI cannot replace actual human interaction in contact centers, it can help telecoms identify issues sooner, resolve them faster, and prevent cases from recurring. It offers many opportunities for telecoms focusing on contact center optimization.

Chatbots and VAs aren’t new but backed by AI, they’re coming in a new avatar. They are much more intelligent now and go beyond responding to basic queries. They can take on tasks related to FAQs, know your customer (KYC) updates, grievance redressal and complaint registration, etc. This will effectively free up first line support and be a huge cost savings for telecoms.

A step further are chatbots with natural language processing (NLP) capabilities that can interpret customer sentiment. By applying sentiment analytics, these bots can offer an appropriate response based on the customer’s tone or choice of words.

With AI, network operation centers (NOCs) can turn into service operation centers (SOCs), branching out with not only support and administrative functions but with analytical capabilities to proactively advise rather than reactively resolve. For example, Telefonica using Huawei AI for three SOCs in Argentina, Chile, and Germany.

5. Intelligent Customer Relationship Management (CRM) Systems

AI in CRM is bringing a high level of analytics-driven insights and predictive accuracy to marketing strategies that were previously not present.

Some of the popular ones are Salesforce’s (AI tool is Einstein,) SugarCRM’s Hint, and Zoho’s Zia assistant. These solutions tackle:

Customized promotions
Sentiment analysis
Cross sell/upsell
Sale opportunity identification
Conversational AI assistants
Predict customer segmentation
Customer churn prevention

Other applications include calculating customer lifetime value, strategizing new product offerings, and statistics that help formulate customer service and retention policies.

These intelligent, integrated, and data-driven capabilities help make smart recommendations about a customer or a prospect based on user data collected over time. The customer experience leads to higher lifetime customer value.

Leveraging solutions to capture the power of AI

At Virtusa, we build solutions that allow clients to create the next generation AI solutions, leveraging our intuitive, end-to-end data science platform for industrializing AI/ML called Alcelerate. Some of our notable telco solution offerings include Smart Field Force Management (SFFM) Platform, powered by AI and AR, and Cognitive Network Decisioning Platform which assists with predictive network management.

AI in telecom isn’t a new phenomenon. In the coming days, adopting AI and ML won’t just drive differentiation, but will become essential for survival.

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