AI’s omnipresence in today’s and future telecom industry explained

The telecom sector is enthusiastically embracing the generative AI wave to meet a wide range of business needs. According to data from the 2023 Nvidia report, 53% of respondents in the 2023 study agreed or strongly agreed that implementing AI gives telecom companies a competitive edge, up from 39% in 2022. This suggests that, despite challenges such as a lack of relevant skills or difficulty in quantifying ROI, telecom providers are more focused than ever on adopting AI. It’s clear: the telecom industry is no longer just tapping into AI; it’s all in, ready to harness the full potential of digital intelligence.

Read the article to explore how AI is being integrated into telecom operations and transforming the connectivity world as we know it.

Current use cases of AI in the telecom industry

Global AI in the telecommunications industry is expected to reach an astounding $19.17 billion by 2029. Multiple companies worldwide leverage AI, enabling telecom customers to experience a completely new level of personalized service, seamless connectivity, and faster issue resolution. For example, SK Telecom, the biggest service provider in South Korea, has invested $200 million in Smart Global Holdings (SGH), a provider of integrated data center solutions, to supply its clients with new services. More so, Anthropic and SK Telecom collaborated to create a large language model (LLM) tailored for telcos, with SK Telecom investing $100 million. The company aims to enhance its AI ecosystem, believing the potential of AI goes far beyond excellent customer experience.

Figure 1. Global AI In Telecommunication Market Research Report 2024

Interestingly, while telecommunications companies have scaled up their investments in deep learning technologies, they’ve pulled back slightly on machine learning funding compared to 2022.

Figure 2. State of AI in telecommunications: 2024 trends survey report

Now, why has the implementation of AI grown among telecommunications networks during the last few years? Well, AI plays a critical role in:

Network optimization & management

AI makes telecom industry systems wiser, leaner, and hyper-responsive, transforming network optimization and management. Here’s how it contributes to several key areas:

1. Predictive analysis. AI can leverage machine learning models to forecast mobile network traffic patterns, helping telecom operators predict when peak usage and other issues are likely to occur. This proactive strategy enables more efficient bandwidth allocation before congestion sets in. For example, by analyzing historical data, AI can predict surges in demand, allowing for timely resource scaling.

2. Real-time traffic shaping. Artificial intelligence can prioritize data streams in real time, reserving bandwidth for critical services like VoIP calls or video conferencing during periods of high traffic. . This adjustment will decrease latency and improve user experience. Moreover, with the detection of anomalies by AI algorithms, any strange pattern that might arise due to malfunctioning equipment, security threats, or data spikes will be quickly distinguished and put under control.

3. Load balancing and protocol optimization. AI systems help smooth network load by efficiently distributing traffic across multiple servers. This prevents congestion and ensures predictable performance. By analyzing real-time data, AI can automatically adjust load distribution as demand fluctuates, without the need for human intervention. Additionally, AI fine-tunes protocols, identifying the fastest and most reliable paths for data transmission, reducing latency and accelerating data transfers.

Predictive maintenance

AI-driven predictive maintenance has revolutionized the telecommunications market. The shift from reactive to proactive maintenance has transformed the entire approach. In the past, network operators relied on reactive maintenance, addressing issues only after they occurred, leading to significant costs and losses due to downtime. The next step was preventive maintenance, with scheduled checks, but it still wasn’t efficient enough.

Predictive maintenance applies AI solutions to the early detection of problems. For example, Vodafone uses its AI to monitor base stations for tiny changes that predict hardware failure, providing repairs in advance. Ericsson analyzes data from towers using predictive tools to find potential faults, reducing downtime. With prioritization, tasks are performed with no waste of resources. As a result, telecom businesses save costs and ensure the longevity of equipment.

AI capabilities also enable dynamic scheduling based on real-time conditions, avoiding unnecessary maintenance. For instance, Telefónica employs AI-driven tools to predict and resolve issues in their fiber networks, scheduling repairs during less busy periods to maintain high service quality. Overall, integrating AI leads to minimal downtime and improved customer satisfaction. Telefónica has already initiated the creation of more than 500 AI-based tools, reinforcing the robustness and seamless operation of their network.

Enhanced customer experience

Excellent customer experience is another important AI use case because it can be improved by leaps and bounds. According to the same NVIDIA survey, 35% of telecommunications sector professionals cited customer experience as their AI success story. Also, 48% stated that improving CX is their primary objective when deploying AI models. The data indicates that telecom companies are already reaping benefits from AI applications. Customers are happy and, more importantly, involved. Here’s how:

1. AI-powered automation of call centers. Routine customer inquiries can be handled by AI-powered systems without the need for human intervention. It helps with cutting down on wait times and freeing up support representatives to handle more complicated problems. With the use of automated solutions like IVRS, (Interactive Voice Response systems), telecom services can interpret customer intent and provide them with prompt solutions or route calls to the relevant departments.

2. Virtual assistants and chatbots. AI chatbots are used by telecom companies to provide 24/7 support for frequently asked questions, basic troubleshooting, and setup guidance. Examples include TOBi from Vodafone and Verizon’s Digital Assistant, both of which efficiently resolve customer inquiries, enhancing the user experience by offering immediate assistance.

3. Personalized sales experiences. If a telecom company knows that a user primarily browses for video streaming, it could recommend an upgrade to a better internet package or suggest adding a certain amount to their bill. This allows the company to tailor its sales approach and ensure the customer receives exactly what they need.

4. Service agent support. Another value of AI is in supporting human agents by providing real-time information, suggesting responses, and even showing them a customer’s interaction history. This reduces response times, enhances accuracy, and ensures a more coherent service experience. Companies like AT&T use AI to suggest quick solutions for agents to speed up issue resolution.

5. Personalized recommendations. AI, through analytics, offers service recommendations. For example, it might suggest a higher data usage plan to customers whose usage consistently exceeds the limit or propose family packages. This approach demonstrates consideration and lets the customer know their needs are valued.

6. Voice assistants. AI-powered voice assistants manage user accounts and can answer queries while also performing tasks like scheduling payments or enabling new features by simple voice commands. This provides hands-free and user-friendly experiences that improve accessibility and convenience.

7. Personalized communication channels. AI can determine a customer’s preferred method of communication (social media, email, SMS, or app notifications) and adjust interactions accordingly. This guarantees that users get help, promotions, and updates via the channel that suits them best.

8. Advanced fraud detection and prevention. AI strengthens security with added support against telecom fraud attempts. It warns the customer about security breaches, strengthening their trust in the service provider.

Future predictions for AI in telecommunications

The future of telecom is already here, but let’s explore how AI will change the industry as we know it even further.

Expansion of 5G and beyond

Ultra-fast connectivity is a key driver behind the rapid expansion of 5G technology in the telecom industry. This efficiency powers advanced, data-intensive technologies such as autonomous vehicles, remotely operated machinery, and connected drones. For instance, 5G enables remote operation of cranes and real-time monitoring of drones in challenging environments. Industries like construction and mining, where instant data transmission and precision are critical, greatly benefit from this capability.

Beyond industrial applications, 5G enables immersive customer experiences, including AR, VR, and mixed reality (XR).  While these experiences drive up the demand for data, AI-driven solutions have been very critical in managing network resources. They estimate data loads and assure seamless connectivity for telecom companies. AI also helps monitor 5G networks in real time and performs predictive analysis to act before any kind of service interruption, ensuring consistent service quality.

Further, 5G’s low latency empowers telecom providers to integrate the IoT across networks. For example, it can enable telecoms to support smart city initiatives that involve thousands of IoT sensors monitoring real-time data on traffic, utilities, and public safety. AI processes this information, allowing telecoms to prioritize network resources. Together, 5G and AI provide a basis for telecom providers to offer personalized experiences supported by reliable connectivity.

Autonomous networks

Future autonomous networks, powered by advanced AI, will elevate telecom systems to a new level of independence. Imagine a network automatically allocating extra bandwidth for major events, such as streaming the Olympics, by analyzing traffic in real time. When a network component encounters a fault, AI can detect the failure, reroute data, and notify maintenance, all without downtime. For example, telecoms can deploy autonomous networks in high-density urban areas, automatically adjusting tower capacity through self-optimizing towers in areas with higher demand, ensuring a seamless user experience. In turn, self-managing networks will enhance efficiency and reliability, offering telecoms greater flexibility and adaptability.

AI-driven customer solutions

AI-powered solutions are transforming the customer experience in telecom. In the future, the focus will be on proactive communication, driven by key demand factors. For example, the recognition of intent allows providers to reach out with helpful nudges before customers even ask for assistance. Omnichannel enablement facilitates self-service across all platforms, while conversational AI ensures seamless engagement at every touchpoint. This is demonstrated by Nokia’s AI Virtual Assistant, MIKA, which provides telecom engineers with quick tips for resolution and helps identify relevant resources for any issue. This approach significantly improves first-contact resolution rates. AI’s central ‘nerve center’ tracks performance, ensuring efficiency and personalization in every interaction.

Enhanced IoT connectivity

With the growth of IoT, telecom providers will be further evolving their connectivity strategies. Future IoT networks will be enabled with the following:

1. Advanced sensors. In industries ranging from sensors that track road conditions for autonomous vehicles to environmental sensors in smart cities, telecommunications companies will establish advanced sensors, updating data in real-time to provide an accurate and immediate response.

2. Expanded connectivity options. Future IoT networks will be enabled by options for tailored connectivity:

• 5G and 6G. They will support ultra-low latency, allowing for real-time applications such as autonomous vehicles and remote robotic surgeries.
• Wi-Fi 6 and beyond. Wi-Fi 6 and its successors will offer much higher speed and bandwidth within very dense environments, such as high-density airports or stadiums.
• Low Power Wide Area Networks (LPWAN). LPWAN is going to connect millions of low-power IoT devices, including sensors for agriculture or smart meters that need infrequent connectivity.

3. Advanced IoT Platforms. Cloud platforms, such as Microsoft Azure, IBM Watson IoT, and AWS IoT Core, will form the backbone of large-scale IoT network management. These platforms will support device management, data analysis, and system integration, enabling telecoms to optimize network performance and provide real-time insights to connected industries.

4. Predictive data processing and analytics. AI-driven analytics will process the upcoming surge of IoT data to generate predictive insights. Future analytics will forecast and manage congestion in smart city traffic by analyzing data from thousands of vehicle sensors.

By taking these steps, telecoms will lead the way for the next wave of IoT. This will establish a foundation for seamless automation, advanced prediction, and new levels of connectivity, from industrial automation to smart cities.

What the future of utilizing AI in telecom holds

The application of AI in the telecom industry is truly transforming the landscape, unlocking new possibilities. AI implementation brings a range of benefits, from enhancing customer experience to improving network optimization and management. Experts claim that AI-driven network optimization has increased overall network efficiency by up to 30% and reduced latency, indicating that AI adoption across telecom networks will continue to grow.

To learn more about AI adoption in the telecom sector and its role in the future of technology, contact Avenga and stay updated on the full potential of AI.