Data-driven Connectivity: The Rise of AI and Machine Learning in Telecommunications

Rajeev Gandhi, Head of Technology, Telco Network Engineering

Healthcare organizations have long relied on legacy technology, resulting in disjointed data collection and a healthcare experience that can be difficult for patients and providers to navigate.

Rajeev Gandhi, Head of Technology, Telco Network Engineering

Artificial intelligence (AI) and machine learning (ML) play a multifold role in the telecommunications industry. While the emergence of 5G in the telecommunications industry has opened the gates to a new universe of opportunities and possibilities AI/ML acts as an enabler and offers the opportunity for myriad new use cases.

Enterprises use data, automation, and digitalization to differentiate themselves and gain a competitive edge. 5G, driven by AI/ML and edge computing, creates a private wireless infrastructure with multiple new applications in the Internet of Things (IoT) environment and the global digital transformation.

Another important aspect is that AI/ML will significantly reduce costs in the mobile network infrastructure by automating functions that typically require human interaction and speed up the deployment of new, revenue-generating service offerings. This is becoming increasingly important as edge, open radio access networks (OpenRAN), and cloud-native 5G cores become more prevalent. 5G is the newest wireless technology, with higher speeds, lower latency, and the capacity to link many sensors.

Since AI/ML help telcos achieve improved short- and long-term performance, they have become a vital part of a digital transformation and service integration journey. Communication service providers (CSPs) are under growing pressure to provide higher-quality services and deliver a better client experience. One way to achieve this is by combining large volumes of data gathered over time from their massive customer base.

Additionally, AI represents a modern computing paradigm in which algorithms learn from data to effectively manage the ever-increasing volume of data generated by sensors, including the ability to identify patterns and trends in real time.


AI/ML as growth engines

AI/ML will be pivotal in improving network performance, reliability, and many more network challenges. AI in the telecommunications market is expected to grow from USD 773 million and a CAGR of 49.8% in 2019 to USD 1.3 billion by 2026.

The rising deployment of AI for various applications in the telecommunication sector and the use of AI-enabled smartphones are significant drivers for the expansion of AI in the telecommunication market.

Furthermore, the proliferation of over-the-top (OTT) services, such as video streaming, has changed how audio and video content is distributed and consumed. Consumer demand for bandwidth has risen dramatically as more people rely on OTT services. The increase in traffic on OTT services is connected to the telecom industry's high operating expenses. Businesses will accelerate client enrollment and new service introductions by reducing the amount of human interaction necessary to configure and maintain networks. Automation is key to these critical advancements.


Benefits of AI/ML in telecommunications

AI/ML will improve network performance, reliability, and challenges.

  1. Network overload prevention
    With AI in place, the network will leverage algorithms to respond instantly to any substantial infrastructure overload. The network will be able to detect an overload, automatically build the number of virtual computers needed to manage the incoming traffic and divert the extra traffic via these virtual machines-all without the need for human intervention.
  2. Personalized upselling and better customer targeting
    AI and data analytics may be coupled to assist you in obtaining a better understanding of your subscribers' tastes and offer them better-tailored service packages when they are most inclined to order. Machine learning algorithms, in particular, natural language understanding, image recognition, and video processing, can discover the different types of information your subscribers consume over time, understand their interests, and find related content that you can serve.
  3. Attracting new clients/new business models
    Industry 4.0 application cases will be realized because of 5G's fast speed, low latency, and dense deployment of endpoints like sensors, robotics, and video cameras. This creates a substantial new revenue potential for telecom operators, allowing them to provide revolutionary process automation services powered by AI at the edge, in addition to providing outsourced IT services to businesses. Examples of services include intelligent video analytics for object recognition and automated inference, sensor data analytics, and industrial equipment control for preventative maintenance.
  4. Keeping malicious acts at bay
    Machine learning can effectively protect your network from dangerous behaviors such as DDoS attacks. Your network can be trained to recognize a huge number of identical requests that are overwhelming resources simultaneously and decide whether to refuse these requests outright or divert them to a less busy data center to be handled manually by your personnel or automatically by machines.
  5. Customer service enhancement
    Intelligent virtual assistants can help you interact with your customers more effectively. As a core technology, AI can help telecom companies reimagine customer connections at scale by enabling tailored, intelligent, and persistent two-way dialogues.

Use cases

  1. Anomaly detection and automated baseline creation
    Anomaly detection and automated baseline creation checks for breaches of key performance metrics, quality performance, and customer satisfaction indicators. The job of telecom experts is optimized by grouping infractions into anomalies, root-cause research of these anomalies, and long-term predictions.

    Anomaly detection based on machine learning may find performance indicators in a data collection that do not comply with an anticipated pattern and expand the breadth of detection by uncovering new patterns multiple baseline breaches. Then, once an anomaly is discovered, it can be prioritized, and the instances with the greatest priority may be evaluated first, considerably speeding up the process.

  2. Automated situation detection
    Users can utilize automated situation detection to find occurrences that don't follow the anticipated pattern and discover relationships between them. This is far more efficient than performing these tasks manually. A circumstance can be prioritized depending on its identification once it has been discovered. Cases of the greatest priority can be assessed first, speeding up the process and ensuring that the system can run at maximum capacity.

  3. Network optimization
    AI is critical in assisting CSPs in developing self-optimizing networks, which allow operators to autonomously adjust network quality based on traffic data by area and time zone. In the telecommunications business, artificial intelligence applications utilize powerful algorithms to seek patterns in data, allowing telcos to discover and forecast network abnormalities and address problems before consumers are harmed proactively.

  4. Predictive maintenance
    Predictive maintenance will help telecommunications deliver better services by proactively repairing faults with communications technology, monitoring the equipment status, forecasting failure based on trends, and more.

  5. Virtual assistants
    The large volume of support requests for installation, setup, troubleshooting, and maintenance, which often overload customer care centers, has led telcos to turn to virtual assistants for assistance. Self-service features that show clients how to install and manage their own devices can be implemented using AI.


The future of AI/ML in telecommunications

AI will improve the performance of a telecommunications network significantly. The telecom network can run autonomously and make competent decisions using artificial intelligence and machine learning to shrink the network. Furthermore, the growing requirement to track material on telecommunications networks and to encourage the elimination of human mistakes from telecommunications networks is the key reason driving AI's rise in the telecoms business.

The industry has seen a definite change, with Telcos partnering with system integrators and forming their software team to leverage independent AI technologies.

Partnerships and alliances in a multi-edge/multi-cloud environment, service-based architecture, open platform with API layer, and edge applications will encourage independent AI-based software services to drive volume business opportunities, with system integrators acting as enabler in the 5G era. This can be accomplished by integrating a service layer architecture and leveraging software tools like AI/ML.

At UST, we have been leveraging AI/ML to help telcos optimize network planning and lower costs. As a leader in 5G network deployment services and tools, we are excited to talk to you about your specific AI/ML use cases and network automation strategies. Please visit us at