AI in Telecom: Solutions for Network Management, Security, Customer Service and More

12 min read
AI in Telecom
AI in Telecom

Smartphones now know exactly when users need a stronger signal, Internet service adapts seamlessly to customers' usage patterns, and customer service inquiries are answered swiftly and accurately by virtual assistants. This is not science fiction. It's the reality made by artificial intelligence (AI) in telecommunications.

From optimizing network performance to predicting service disruptions, AI has become the driving force behind the telecom’s evolution, helping telcos meet growing needs of their clients.

AI's capabilities go far beyond the things we’ve mentioned, which is why it's in such high demand among telcos. In 2021, the AI market within the telecom industry was valued at $1.2 billion, and industry experts predict it will grow substantially to reach $38.8 billion by 2031, indicating an impressive annual growth rate of approximately 41.4% from 2022 to 2031.

At Flyaps, we are enthusiastic supporters of AI in telecommunications. We've been operating in the field for over a decade, witnessing the AI technology going from a novelty to an indispensable part of the industry. In this article, we'll leverage our expertise to highlight three critical areas within telecom operations that can be enhanced through AI solutions: network management, network security, and customer service. We will also provide real-life examples of telcos that have already achieved success thanks to betting on AI, so keep reading.

AI in telecom network management

The transition to 5G, IoT, and edge computing has resulted in the additional complexity of our mobile networks. They are growing in both size and functionality, which creates the need for increased reliability, security, and versatility to handle a wide range of tasks. The good news is that AI and machine learning (ML) have already proven to be invaluable for telecom network management. Let's explore it in more detail.

Usage of AI to improve network management
Usage of AI to improve network management

Network optimization

Thanks to AI in telecom, you can detect bottlenecks, optimize traffic routing, and predict potential problems. As a result, this approach lets you minimize downtime, enhance network reliability, and ensure seamless connectivity, even during periods of high usage.

For instance, Deutsche Telekom uses AI to optimize the part of their network that deals with radio signals – radio access network (RAN). The company is now testing a new AI-based solution to enable the radio network to monitor its own performance, identify issues, and take corrective actions automatically, without human intervention. For example, in the near future the system is promised to adjust settings to improve signal quality or network efficiency.

Predictive maintenance

AI-driven predictive maintenance is a game-changer in telecom. Take Vodafone as an example. In collaboration with Nokia, they have implemented an AI-driven system running on the public cloud, which uses advanced ML algorithms to detect anomalies and changing patterns within their network. These anomalies can range from congestion to interference, latency, and call setup problems. By promptly identifying and addressing these issues, Vodafone ensures the uninterrupted performance of its network, minimizing service disruptions and enhancing customer satisfaction.

Energy efficiency

For telcos, the rollout of 5G networks usually means an increase in traffic and, consequently, more energy consumption. The cost of electricity is on a steady rise, and environmental responsibility is a growing concern, so telcos face a critical dilemma: how can they meet the constant demand for high-speed connectivity while simultaneously minimizing their carbon footprint and reducing operational costs? This is precisely where AI in telecom plays a crucial role.

A compelling example comes from Telefonica Spain, which tested a feature called deep sleep mode. This energy-saving functionality was deployed in Madrid at a site with a 5G configuration. Supported by AI and machine learning algorithms, the company achieved remarkable savings of up to 8% in total consumption over a 24-hour period and up to 26% during low traffic hours. This not only reduces operational costs but also aligns with sustainability goals, making telecom networks more environmentally friendly.

But that’s not all. The telco is striving to make its networks more autonomous, implementing AI and ML-based platforms to automate various tasks, such as setting thresholds, deactivating cells during periods of low traffic, and consistently assessing quality – all without causing any disruptions to network performance or user experience.

This approach is crucial for the company's ambitious goals to achieve net zero emissions in their main markets by 2025 and surpass the objectives set by the Paris Agreement. Globally, Telefonica plans to reach net zero emissions in their entire value chain by 2040. Thanks to these efforts to make networks more autonomous, Telefonica has already reduced its worldwide energy consumption by 7.2%, even despite the fact that network traffic has surged by up to 6.7 times.

AI in telecom network security

The global IT and telecom cybersecurity market reached a value of approximately $30.18 billion in 2021. This market is anticipated to maintain a steady growth rate of around 12.1% annually from 2022 to 2030. The driving force behind this growth is the rapid adoption of cloud computing, the Internet of Things (IoT), and 5G networks. While these technologies have streamlined and automated many processes in the telecom industry, they have also exposed telecom companies to more cybersecurity risks. As an outcome, businesses have to protect themselves against cyber threats more. Let's explore how AI can help with that.

Usage of AI to improve network security
Usage of AI to improve network security

Fraud detection

AI algorithms can monitor network traffic 24/7, scrutinizing every data packet and user interaction, and also detect anomalous patterns or behaviors that human analysts might overlook. For instance, AI can identify unusual call routing, detect discrepancies in call duration, or pinpoint cases of SIM card cloning. By leveraging AI-driven fraud detection, telecom companies can not only safeguard their revenue but also protect customers from unauthorized charges and suspicious activities. The speed of AI detection and automated responses significantly reduces the window of opportunity for fraudsters, enhancing overall network security.

Threat detection

AI can be used to identify potential cyber threats such as malware, DDoS attacks, and intrusions. With AI solutions quickly detecting these threats and triggering automated responses to mitigate them, you can lower the risk of security breaches.

Security automation

Imagine your telecom company manages a large network of servers and data centers. Your security team has a tough job: they need to keep the network safe from threats, fix system vulnerabilities quickly, and control who has access to the network.

Before using AI, your security team had to manually find and update software on hundreds of servers. It was hard to keep up with all the updates, making the network vulnerable to cyberattacks in the meantime.

But then you've introduced AI to help. An AI-driven automation system constantly checks your network for problems and automatically updates software to keep it safe. It also handles other security tasks like updating firewall rules and managing who can access the network.

With AI in telecom, there's less chance of mistakes in managing updates, firewalls, and access control. Plus, AI can respond quickly to new threats, making your network much more secure.

User and entity behavior analytics (UEBA)

Suppose, one day, your security team noticed an anomaly. An employee, John, who typically logged in from the company's headquarters in New York, suddenly logged in from a remote location in another country at odd hours. This raised suspicions, but the security team couldn't be sure if it was a legitimate action or a potential security threat.

You decided to implement an entity behavior analytics (UEBA) - AI-driven solution, that uses machine learning algorithms to continuously analyze user and entity behaviors to identify suspicious activities.

Shortly after the integration, the UEBA detected a suspicious pattern: John's login behavior was inconsistent compared to his past activities. Not only was he logging in from a remote location, but he was also accessing customer data that he didn't typically need for his job role.

With this AI-generated alert, the security team quickly investigated the situation. It turned out that John's credentials had been compromised, and someone was attempting to steal sensitive customer data. Thanks to the rapid detection, the security team was able to block John's access, change compromised passwords, and prevent a potential data breach.

Case in point: British Telecom’s usage of AI and big data to improve cybersecurity

British Telecom (BT) is a well-known telecom giant with a leading position in the global telecom market. This prominent status makes them a prime target for cybercriminals.

In the past, cybersecurity efforts used to mainly focus on what happened within networks. But now, security teams have to take into account all the parts of the ecosystem, like clouds, software from different vendors, data from sensors, and data from IoT devices. Therefore, BT has implemented AI to manage the vast amount of data generated by its infrastructure and efficiently identify and respond to cyber threats.

BT created an AI-driven big data security platform, called Assure Cyber. Their security team took into account that cyber frauds these days are also well aware of AI's capabilities and principles of working. For instance, skilled hackers can make it think that it should keep attacking itself constantly. That's why it's crucial for them to have people double-check and balance things out. Assure Cyber simplifies and enhances the involvement of human personnel by visualizing all the processes. It makes people's participation both easy and efficient.

On average, BT's cyber security team deflects approximately 4,000 attacks daily, totaling around 125,000 each month. Given the immense scale of BT's network infrastructure and the constant threat of attacks, relying solely on human monitoring and response is unfeasible. However, the AI-driven system aids in early detection and swiftly identifying the source of an attack, enabling cybersecurity experts to respond promptly and protect the network.

AI for improving digital customer experience in telecom

Here's how you can transform customer service and enhance the overall customer experience with AI in the telecom industry.

Usage of AI to improve customer service in telecom
Usage of AI to improve customer service in telecom

AI-powered chatbots and virtual assistants

AI chatbots can handle routine customer queries, provide account information, and assist with common troubleshooting issues. They are available 24/7, so your clients can get assistance at any time, leading not only to faster response times, but also improved accessibility.

Moreover, AI-powered NLP technology enables chatbots and virtual assistants to understand and respond to customer inquiries in natural language. This makes interactions with AI systems more conversational and user-friendly, improving the overall customer experience.

Personalized recommendations

AI algorithms can analyze customer data, such as usage patterns and preferences, to provide personalized product and service recommendations. For example, AI in telecommunications can suggest tailored mobile plans or additional services based on a customer's usage history, helping customers find the most suitable options.

That is exactly what Flyaps did for NetSpark IP & Telecom. We created a tariff calculation tool within a custom CRM for the company. NetSpark's staff had to analyze and calculate customer data manually in order to find the optimal combination of tariff plans for each client. As a result, customers had to wait hours, if not days. After the tool was implemented, the process took just a few minutes.

Custom CRM designed for NetSpark IP & Telecom by Flyaps
Custom CRM designed for NetSpark IP & Telecom by Flyaps

Customer feedback analysis

AI can analyze customer feedback, including social media comments and surveys, to gain insights into customer sentiment and preferences. Telecom companies can use this information to make data-driven improvements to their services and offerings.

Automated billing and account management

AI-based telecom billing solutions automate billing processes and account management tasks, such as bill inquiries, payment processing, and plan upgrades. This reduces the administrative burden on customer support teams and ensures accurate and efficient handling of customer accounts.

Case in point: Verizon's use of AI-driven solutions revolutionized its customer experience strategy

Verizon, one of the largest telecom providers in the US, wanted to offer better experiences to its 90 million customers. They aimed to provide seamless interactions whether customers visited a store, made a call, or chatted online. However, their existing systems were disconnected and lacked consistency.

To address this, Verizon turned to AI technology. They implemented an AI-based management system, which allowed customers to begin their journey in one channel and continue in another without starting over.

They also integrated a customer decision solution into all channels, creating a single AI-powered system that suggests the best actions for customers. This led to improved attachment rates, higher customer satisfaction scores, and fewer digital contact redirects.

In the end, Verizon took care of 90 different customer journeys, handled a billion transactions per month, and significantly improved customer experiences by using AI to streamline and personalize interactions across channels.

Challenges to overcome when embracing AI in telecommunications

Using AI in the telecom industry offers immense potential, but it also comes with its fair share of challenges:

Challenges with AI in telecommunications
Challenges with AI in telecommunications

Regulatory compliance

Telecom has always been a heavily regulated industry, with laws governing crucial aspects such as data privacy, security, and customer rights. However, the integration of AI solutions, which now organize and process vast amounts of data, including customers' personal information, has added a new layer of complexity to regulatory compliance.

AI applications often depend on extensive customer data for analysis. Therefore, ensuring compliance with stringent data protection laws like GDPR or HIPAA has become paramount. Telecom companies must establish robust data handling and consent management processes to effectively navigate these evolving legal requirements.

Integration with existing infrastructure

Many telecom companies have legacy systems and infrastructure that were not originally designed with AI integration in mind. Integrating AI solutions seamlessly with these existing systems can be challenging, requiring substantial reengineering and investment.

Talent acquisition for AI

According to a McKinsey report, only 10% of the world's data scientists have the skills required for AI-related work. This means that there is a global shortage of AI talent. Finding and hiring qualified AI professionals who understand both the telecom industry and the intricacies of artificial intelligence can be highly competitive and costly.

Additionally, telecom companies may need to invest in training and upskilling their existing workforce to harness the full potential of AI. This includes training network engineers, data scientists, and IT professionals in AI-related skills.

Ways to achieve a smooth AI implementation

A successful implementation of AI in telecommunications requires careful planning and attention to several critical factors we'll explore in this paragraph. For better understanding, imagine a telecom company that was eager to enhance its services and operational efficiency with AI. To navigate this transformative journey, they partnered with an experienced software development company. Their AI implementation journey would look like this:

AI in telecom implementation strategy
AI in telecom implementation strategy

Strategic alignment

To align AI initiatives with their strategic goals, the company and their tech partner conducted in-depth consultations. They identified key areas where AI could drive improvements, such as optimizing network performance and enhancing customer support. This clear alignment ensured that AI investments had a direct impact on the telco's business objectives.

Ensuring high-quality data

Our imaginary telco recognized that the success of their AI adoption depended on the quality of the data they could harness. With their IT partner's assistance, they created a comprehensive data strategy. They collected data from various sources, including customer call records, network performance metrics, and operational logs. The IT team helped clean and structure this data, ensuring it was of the highest quality for AI analysis.

Establishing scalable infrastructure

Realizing the need for scalable infrastructure, the telco and the IT team opted for cloud computing solutions. They set up a cloud-based architecture that could accommodate the growing volume of data and AI workloads as the company expanded its services.

Regulatory compliance

Being in a highly regulated industry, the company needed to adhere to strict data privacy laws and standards like GDPR. Their IT partner implemented robust data handling and consent management practices. This ensured that the telco could use customer data for AI without violating any regulations, gaining the trust of their customers.

Proof of concept (PoC) and pilot programs

Before rolling out AI solutions across the entire network, the telco and their IT partner conducted PoCs and pilot programs. They tested AI-driven network optimization algorithms in select regions and implemented AI-powered chatbots for customer service in specific markets. These small-scale initiatives allowed them to fine-tune the AI systems, gather valuable insights, and identify any potential challenges.

How Flyaps can help you to embrace AI in telecommunications

Here at Flyaps, we've gained the expertise in developing AI-based solutions in various fields, telecommunications included. Let's explore how we can apply our experience to benefit your telecom business.

AI consulting for telecom

We can assess the specific needs and challenges of your business, helping you identify areas where AI can bring the most value. Our experts can create a roadmap for AI integration, including selecting the right AI technologies.

Custom development of AI-based solutions

We can design, develop, and deploy a custom AI solution that will perfectly address various use cases, such as network optimization, customer service automation, fraud detection, and predictive maintenance. Custom development ensures that AI applications are precisely aligned with your company's objectives and infrastructure.

Data management and integration

Our developers can assist in collecting, cleaning, and managing your data from various sources. They can design data pipelines and integration solutions to ensure seamless data flow to AI systems. Data integration is crucial for AI algorithms to have access to the right information for analysis and decision-making.

Integration with existing systems

Integrating AI solutions with existing telecom systems and infrastructure can be complex. We can ensure seamless integration, allowing AI applications to work in harmony with legacy systems and other software tools used by your telco.

Ready to empower your telco with AI? Drop us a line to start!