AI Applications in the Telecommunications Industry: Challenging Telecoms With Machine Learning Solutions

AI Applications in the Telecommunications Industry: Challenging Telecoms With Machine Learning Solutions

Konrad Fulawka - December 15, 2021

Current Applications of AI in Telecoms

The telecom sector has experienced several automation phases. While previous connections were still made manually by switching cables, hardware later automated this work. These features no longer need specific hardware but are virtually defined through software.


• AI has been applied in the telecom sector for over a decade, with applications focused on optimizing radio signals, power management, and transmission estimation.
• AI in telecom can provide faster and better decision-making, utilize expert knowledge more efficiently, and handle repetitive tasks effectively.
• AI applications can operate autonomously through autonomous learning and action, with scenarios ranging from closed-loop systems to human-in-the-loop interactions.
• Challenges in implementing AI in telecom include technical integration, lack of technical expertise, and dealing with unstructured data. Contact nexocode data engineers for overcome these problems and implement AI in the telecom industry.

Artificial intelligence (AI) has been clearly advancing since around 2010 and has applied to the telecommunication market for more than a decade. Besides the fact that telecom infrastructures are vital to society, more and more applications depend on well-functioning, reliable, and always available telecom services.

Most of the current AI applications focus on improving specific parameters. These are strictly defined applications such as:

1. Optimizing the Parameters of a Radio Signal

Currently, Machine Learning (ML) is used to optimize data flow to and from a Base Station (BTS) in a mobile network. The distance to users, connected users, and certain environmental factors determine the radio parameters. They, in turn, determine the maximum amount of data that can be transmitted per quantity of spectrum per unit of time. Interference also plays a role: radio resources can be coordinated between micro and macro cells. To maximize efficiency, algorithms are being used to dynamically determine what part of the spectrum should be used for which user and with which parameters. The parameters of these algorithms can be “tuned” with AI.

2. Power Management

Machine learning techniques are used to achieve power savings in live mobile networks. Based on meteorological data, the number of users, and their position, antennas actively adjust their radiation pattern, direction, and strength to demand. This results in energy saving, for example, during the night when data demand is relatively low and in more efficient use of the base stations because a larger surface area can be operated at set-up points where the need for capacity is not uniform.

3. Quality of Transmission Estimation

With optical connections, the signal can be disturbed or interrupted and can cause permanent equipment failure. Machine learning is applied to estimate how well the transmission will work over a connection in advance. It calculates the best path based on things like the cable length, other signals within the cable, and the equipment’s age. The traffic is routed based on this assessment. It is also possible that such algorithms are used in wireless networks, for example, determining how much error correction or redundancy (e.g., retransmission) is used. Expert systems and Machine learning algorithms are the two AI techniques that have been widely used in the telecommunication sector, while ML and distributed Artificial Intelligence are the two AI techniques that are most promising for the future.

AI and Machine Learning

AI and ML are disrupting and transforming businesses. Telecommunications companies can leverage these technologies to improve customer retention, enable self-service, improve equipment maintenance, and reduce operational costs at the same time.

Network management

The telecommunication industry is riding the waves of the tech revolution and digital transformation to offer a wider variety of services to its consumers. However, consumers in today’s digital world will not be happy with run-of-the-mill products and services – they also demand a better quality of services and more responsive service providers. Data-driven insights relied on solutions powered by AI and ML can help telecom providers fulfill these expectations.

Digital transformation

Autonomous Learning and Action

AI applications often take over tasks generally performed by people or support people. This means that these systems have a degree of autonomy. There are two forms of independence: autonomous learning and autonomous action.

Autonomous Learning

Today’s AI model has been developed based on large amounts of (historical) data. An algorithm ’learns’ the desired results from this data with a particular input. There are several ways to shape this learning or ’training':

  • Offline Learning - model is trained once or every so often based on a static dataset. Both the model and the data used can be tested and validated before the model goes into production;
  • Online Learning - model is trained as with offline learning and then retrained periodically based on new data;
  • Continuous Learning - model is continuously updated using incoming data. Unlike with online learning, there are no longer different ‘versions’ of the model: each inference request potentially directly affects the following AI decision.

Autonomous Action

AI applications in the telecommunication industry can be applied in various ways. The most used ones include:

Closed-loop scenario - in this scenario, the AI system performs actions directly. The only action people can achieve is switching off the system, for example - speech recognition software.

Closed-loop scenario
Closed-loop scenario

Open-loop scenario - AI system’s role is to provide support. AI presents a person with an outcome, and based on this outcome, the person can act. In this scenario, people can deviate from the advice and/or check this advice based on other information. Example - expert systems that help doctors form a diagnosis.

Open-loop scenario
Open-loop scenario

In a rule-constrained closed-loop scenario, an AI system can perform direct actions but is restricted by specific “hard” rules. Breaking the rules results in the system being disabled or failing to act. Autonomous vehicles are an example of this strategy. They are often equipped with various ‘fail safe’ rules that ensure a car makes an emergency stop in unsafe situations;

Rule-constrained scenario
Rule-constrained scenario

In human in the loop scenario - AI can perform actions directly, but humans can stop or adjust these actions if necessary. Example: autonomous vehicles where people have to keep their hands on the steering wheel;

Multiple AI systems in the loop - one or more other AI systems monitor an AI system that performs actions. The controlling AI model can view the original inputs and the AI’s decision and assess whether this decision is correct.

Added-Value of AI in the Telecommunications Industry

The use of Artificial Intelligence in telecommunications can help solve several complex and sometimes long problematic issues and at the same time yield tons of added value to both consumers and operators alike. The latter has always been collecting substantial amounts of telemetry and service usage statistics, but most of it never got used in a meaningful way due to the lack of the right software.

With AI, this massive array of previously unused data can be turned into fertile soil for growing new services, improving the quality of existing ones, taking customer experience to a new level, and optimizing business operations. According to relatively recent studies, AI in telecom companies will be generating nearly 11 billion dollars by 2025 — a staggering amount that is likely to keep growing as the scope of AI applications expands.

The global market of AI in telecoms - projected growth. Based on Valuased Reports - Global AI In Telecommunication Market Size, Status and Forecast 2021-2027
The global market of AI in telecoms - projected growth. Based on Valuased Reports - Global AI In Telecommunication Market Size, Status and Forecast 2021-2027

AI generally provides several advantages and opportunities:

AI Can Make Faster and Sometimes Better Decisions Than People

Whereas a person sometimes needs a few seconds to minutes to make a decision, a Machine learning model can often process thousands of data items in a fraction of a second.

For example, fraud detection AI can monitor thousands of credit card transactions in real-time and block potentially fraudulent transactions.

With AI, Rare Expert Knowledge Can Be Used More Efficiently

People often undergo training for several years before entering the labor market. A person will not yet be at the top of their game, even a few years later. With ML, an expert’s knowledge can be distilled into a model, and thus, this knowledge can be applied more widely.

For example, Google has developed an AI system that can identify tumors from CT scans just as well as an experienced radiation oncologist. Such application of AI can provide insufficient expert knowledge in specific scenarios and thereby free up the human experts to do other work more efficiently.

Artificial Intelligence Is Good at Repetitive Tasks

People often perceive repetitive tasks as boring and unsatisfying. However, if these tasks are well framed, the AI framework is ideally suited to take them over. AI does not have to sleep, rest, or take breaks because it will not get bored or tired. Repetitive tasks also lend themselves well to AI because (if humans currently perform the job) there is probably sufficient Big Data available to train the AI.

Challenges of Using AI and ML for Telecom Companies

Even though the global AI in the telecommunication market is proliferating, implementing it can still be tricky for many businesses. Besides being unable to recognize the need for AI or identify appropriate business use cases, the most common challenges of implementing AI in telecoms are:

  1. Technical integration - although old legacy systems are one of the most common reasons why many AI integration fails, several things could be prepared before starting any AI project:

Set up a unified database where all the data required by the system will be stored;

Use data lakes, as well as edge or cloud computing to eliminate any issues that can occur when storing large amounts of data;

Do not hesitate to give your data entry and storage process a complete overhaul if you notice that collected data is disparate or unstructured;

Ensure that you have the required hardware and software to handle the new system.

  1. Lack of technical expertise - AI is a relatively new technology. With limited local talent, building an in-house team can take a significant amount of time and yield little result. A better option is looking for a technical partner to implement AI in telecommunications. However, finding a vendor that has both enough competence and experience to build an AI system successfully can be a challenge in itself. Moreover, implementing AI can be quite pricey, so starting your project with the right partner is crucial.
  2. Unstructured data - Implementing an AI system without access to relevant data is a fruitless endeavor. Many organizations struggle with data collection because of several common issues:

Fragmented data - information is collected and stored by different systems without a single unified database from where it can be accessed;Unstructured data - an enormous mass of uncategorized data without any context or explanation of what it is related to is not very useful to any AI algorithm;Incomplete data - using data with missing components can lead to inconsistent or faulty learning by the AI system.

Since AI algorithms require clean, well-structured data, around 80% of the time of any ML project is dedicated to ETL (extracting, transforming, loading) and data clean-up. Therefore, it is essential to put an appropriate Big Data engineering ecosystem to collect, integrate, store, and process data from numerous siloed data sources.

AI for Telco - Most Common Use Cases

AI has helped the telecom sector redefine customer satisfaction, bringing new opportunities and complicating business models. Here are some of the ways that AI has contributed to the telecom industry already:

1. Predictive Maintenance

AI and ML have enabled the telecommunication industry to extract valuable business insights. Since telecoms have a massive amount of Big Data, AI can use it to make efficient and effective decisions through customer segmentation, predicting the lifetime value of a consumer, and making purchase recommendations.

Predictive analytics, by finding patterns in the historical data, can accurately anticipate and warn about possible hardware failures. Furthermore, created algorithms and data science models can identify the reason behind each failure, making it possible to fight the problem at its root. Preventive maintenance allows telecom companies to be very proactive at maintaining their equipment, fixing issues before they occur, and minimizing support requests. This proactive approach brings an overall boost to the customer experience.

Predictive analytics

2. Improved Network Optimization

Artificial Intelligence applications in the telecommunications industry help Communication Service Providers (CSPs) build self-optimizing networks (SONs) to improve customer satisfaction, lower customer service costs, prevent outages and keep certain network quality. Such networks are automatically monitored by AI algorithms that detect and accurately predict network anomalies. Furthermore, they can proactively optimize and reconfigure the network to ensure higher quality services. You can read more on data management and network optimization in our past article - Machine Learning to the Rescue: Facilitate Data Management Needs of Telecommunications Industry.

3. Network Anomalies

AI-based anomaly detection effectively augments and automates early detection, predictions, and decision-making in operations and business processes where humans can’t deal with data volume or velocity. Improving the overall time to detect invariably leads to quicker resolution of incidents. It thus results in reduced costs associated with outages and aids in the prevention of lost revenue and brand impact.

Furthermore, AI/ML-based anomaly detection solutions can analyze multiple dimensions of data sources, looking at the cell, subscriber, and device-level KPIs, monitoring for faults in network equipment, and correlating alerts across domains for noise reduction and root cause analysis. This gives engineers a transparent view of both network & service performance and subscriber experience — at any given time and enables them to leverage data and predict network anomalies in time. You can read more on network anomaly detection and network automation in our recent article.

4. Robotic Process Automation (RPA)

Robotic process automation is a form of digital transformation that relies on implementing AI. The Telecom sector can use RPA and natural language processing (NLP) to automate data entry, order processing, billing, and other back-office processes that require lots of time and manual work. This frees up employees’ time, lets them focus on more critical tasks, and reduces the number of errors that manual labor is prone to. Thanks to robotic process automation solutions, the office runs smoother, employees are more productive, and customers enjoy error-free service.

You can read more about specific use cases of applying RPA and NLP solutions in the telecom sector in our recent article.

5. Fraud Prevention

One of the things that AI in telecom can do exceptionally well is fraud detection and prevention. Anti-fraud analytical systems can detect suspicious behavioral patterns and immediately block complementary services or user accounts by processing call and data transfer logs in real-time. The addition of ML enables such systems to be even more accurate and fast.

Real-Life Case Studies

1. Nokia

Nokia released AVA Telco AI Ecosystem, which provides Artificial Intelligence solutions that can be delivered through the cloud with Microsoft Azure or other public options. The solutions enable Communications Service Providers to automate network management, capacity planning, and service assurance, cutting operational costs, increasing agility, and boosting subscriber experience.

Nokia AVA across analytics, insights, and AI portfolio. Based on Nokia AVA marketing materials.
Nokia AVA across analytics, insights, and AI portfolio. Based on Nokia AVA marketing materials.

2. Vodafone

The British telecom giant Vodafone Group launched an assistant app called TOBi for customer care management, an intelligent virtual assistant capable of supporting users in dealing with issues, subscription management, and purchasing new equipment and services.

3. Deutsche Telecom

Deutsche Telekom has been making considerable investments in AI at various levels. From an AI-powered chatbot called Tinka, capable of providing over 1500 answers to customers’ questions through interactive voice response, to intelligent business planning tools, this CSP is actively embedding AI and data science elements into its infrastructure and service portfolio.

The Future of AI for Telecom Companies

Cloud, 5G, and AI, cognitive computing technologies engagement with consumer insights have made it possible to answer a wide variety of questions, all in the customer’s language. However, in the future, as businesses get comfortable turning customer insights over to machines, human customer-service agents might become a thing of the past, allowing customers to engage with virtual assistants and boots.

AI is also predicted to leap from dealing with insights to predicting consumer behavior and impacting business decisions. This should lower costs and enhance customer experience, increasing their lifetime value.

The range of potential Artificial Intelligence applications in telecommunications and AI telecom use cases is surprisingly broad. There is no doubt that key market players will see increasingly intelligent automation systems being rolled out to streamline day-to-day operations and deliver more value to customers.

The success of telecommunication companies embarking on a digital transformation journey will rely on their ability to put AI to good use as early as possible and develop corresponding software. With cognitive technologies-powered data collected, reliable insights, and manual expertise, there may be no limit to what AI can help us achieve.

About the author

Konrad Fulawka

Konrad Fulawka

Strategic Advisor and Telco Expert

Linkedin profile

Konrad Fulawka graduated from the University of Technology in Wroclaw and has almost 20 years of experience in the Telecommunications Industry.
For the last 11 years, he works for Nokia. Over the time, Konrad was responsible for leading international and multicultural teams working on many complex telecommunication projects, delivering high-quality software worldwide. During the last few years, he is heading the Nokia Garage - Innovation Hub, which helps Nokia drive cutting-edge innovative projects.
At nexocode, Konrad acts as a strategic advisor and Telco Expert with unparalleled insight into global business trends and best practices across all verticals. He loves DIY (Do It Yourself) activities besides Political Economy and Financial Services Markets.

Would you like to discuss AI opportunities in your business?

Let us know and Dorota will arrange a call with our experts.

Dorota Owczarek
Dorota Owczarek
AI Product Lead

Thanks for the message!

We'll do our best to get back to you
as soon as possible.

This article is a part of

AI in Telecommunications
7 articles

AI in Telecommunications

Artificial Intelligence and ML are disrupting and transforming telecom businesses. Telecommunications companies can leverage these technologies to improve customer retention, enable self-service, improve equipment maintenance, and allow for an undisrupted flow of the evergrowing amounts of telecom data.

These advancements will also reduce operational costs, which means you're likely going see more savings than ever before! Click here for our article series about how AI revolutionizes the Telco industry across all areas.

check it out


Insights on practical AI applications just one click away

Sign up for our newsletter and don't miss out on the latest insights, trends and innovations from this sector.


Thanks for joining the newsletter

Check your inbox for the confirmation email & enjoy the read!

This site uses cookies for analytical purposes.

Accept Privacy Policy

In the interests of your safety and to implement the principle of lawful, reliable and transparent processing of your personal data when using our services, we developed this document called the Privacy Policy. This document regulates the processing and protection of Users’ personal data in connection with their use of the Website and has been prepared by Nexocode.

To ensure the protection of Users' personal data, Nexocode applies appropriate organizational and technical solutions to prevent privacy breaches. Nexocode implements measures to ensure security at the level which ensures compliance with applicable Polish and European laws such as:

  1. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (published in the Official Journal of the European Union L 119, p 1); Act of 10 May 2018 on personal data protection (published in the Journal of Laws of 2018, item 1000);
  2. Act of 18 July 2002 on providing services by electronic means;
  3. Telecommunications Law of 16 July 2004.

The Website is secured by the SSL protocol, which provides secure data transmission on the Internet.

1. Definitions

  1. User – a person that uses the Website, i.e. a natural person with full legal capacity, a legal person, or an organizational unit which is not a legal person to which specific provisions grant legal capacity.
  2. Nexocode – NEXOCODE sp. z o.o. with its registered office in Kraków, ul. Wadowicka 7, 30-347 Kraków, entered into the Register of Entrepreneurs of the National Court Register kept by the District Court for Kraków-Śródmieście in Kraków, 11th Commercial Department of the National Court Register, under the KRS number: 0000686992, NIP: 6762533324.
  3. Website – website run by Nexocode, at the URL: whose content is available to authorized persons.
  4. Cookies – small files saved by the server on the User's computer, which the server can read when when the website is accessed from the computer.
  5. SSL protocol – a special standard for transmitting data on the Internet which unlike ordinary methods of data transmission encrypts data transmission.
  6. System log – the information that the User's computer transmits to the server which may contain various data (e.g. the user’s IP number), allowing to determine the approximate location where the connection came from.
  7. IP address – individual number which is usually assigned to every computer connected to the Internet. The IP number can be permanently associated with the computer (static) or assigned to a given connection (dynamic).
  8. GDPR – Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals regarding the processing of personal data and onthe free transmission of such data, repealing Directive 95/46 / EC (General Data Protection Regulation).
  9. Personal data – information about an identified or identifiable natural person ("data subject"). An identifiable natural person is a person who can be directly or indirectly identified, in particular on the basis of identifiers such as name, identification number, location data, online identifiers or one or more specific factors determining the physical, physiological, genetic, mental, economic, cultural or social identity of a natural person.
  10. Processing – any operations performed on personal data, such as collecting, recording, storing, developing, modifying, sharing, and deleting, especially when performed in IT systems.

2. Cookies

The Website is secured by the SSL protocol, which provides secure data transmission on the Internet. The Website, in accordance with art. 173 of the Telecommunications Act of 16 July 2004 of the Republic of Poland, uses Cookies, i.e. data, in particular text files, stored on the User's end device.
Cookies are used to:

  1. improve user experience and facilitate navigation on the site;
  2. help to identify returning Users who access the website using the device on which Cookies were saved;
  3. creating statistics which help to understand how the Users use websites, which allows to improve their structure and content;
  4. adjusting the content of the Website pages to specific User’s preferences and optimizing the websites website experience to the each User's individual needs.

Cookies usually contain the name of the website from which they originate, their storage time on the end device and a unique number. On our Website, we use the following types of Cookies:

  • "Session" – cookie files stored on the User's end device until the Uses logs out, leaves the website or turns off the web browser;
  • "Persistent" – cookie files stored on the User's end device for the time specified in the Cookie file parameters or until they are deleted by the User;
  • "Performance" – cookies used specifically for gathering data on how visitors use a website to measure the performance of a website;
  • "Strictly necessary" – essential for browsing the website and using its features, such as accessing secure areas of the site;
  • "Functional" – cookies enabling remembering the settings selected by the User and personalizing the User interface;
  • "First-party" – cookies stored by the Website;
  • "Third-party" – cookies derived from a website other than the Website;
  • "Facebook cookies" – You should read Facebook cookies policy:
  • "Other Google cookies" – Refer to Google cookie policy:

3. How System Logs work on the Website

User's activity on the Website, including the User’s Personal Data, is recorded in System Logs. The information collected in the Logs is processed primarily for purposes related to the provision of services, i.e. for the purposes of:

  • analytics – to improve the quality of services provided by us as part of the Website and adapt its functionalities to the needs of the Users. The legal basis for processing in this case is the legitimate interest of Nexocode consisting in analyzing Users' activities and their preferences;
  • fraud detection, identification and countering threats to stability and correct operation of the Website.

4. Cookie mechanism on the Website

Our site uses basic cookies that facilitate the use of its resources. Cookies contain useful information and are stored on the User's computer – our server can read them when connecting to this computer again. Most web browsers allow cookies to be stored on the User's end device by default. Each User can change their Cookie settings in the web browser settings menu: Google ChromeOpen the menu (click the three-dot icon in the upper right corner), Settings > Advanced. In the "Privacy and security" section, click the Content Settings button. In the "Cookies and site date" section you can change the following Cookie settings:

  • Deleting cookies,
  • Blocking cookies by default,
  • Default permission for cookies,
  • Saving Cookies and website data by default and clearing them when the browser is closed,
  • Specifying exceptions for Cookies for specific websites or domains

Internet Explorer 6.0 and 7.0
From the browser menu (upper right corner): Tools > Internet Options > Privacy, click the Sites button. Use the slider to set the desired level, confirm the change with the OK button.

Mozilla Firefox
browser menu: Tools > Options > Privacy and security. Activate the “Custom” field. From there, you can check a relevant field to decide whether or not to accept cookies.

Open the browser’s settings menu: Go to the Advanced section > Site Settings > Cookies and site data. From there, adjust the setting: Allow sites to save and read cookie data

In the Safari drop-down menu, select Preferences and click the Security icon.From there, select the desired security level in the "Accept cookies" area.

Disabling Cookies in your browser does not deprive you of access to the resources of the Website. Web browsers, by default, allow storing Cookies on the User's end device. Website Users can freely adjust cookie settings. The web browser allows you to delete cookies. It is also possible to automatically block cookies. Detailed information on this subject is provided in the help or documentation of the specific web browser used by the User. The User can decide not to receive Cookies by changing browser settings. However, disabling Cookies necessary for authentication, security or remembering User preferences may impact user experience, or even make the Website unusable.

5. Additional information

External links may be placed on the Website enabling Users to directly reach other website. Also, while using the Website, cookies may also be placed on the User’s device from other entities, in particular from third parties such as Google, in order to enable the use the functionalities of the Website integrated with these third parties. Each of such providers sets out the rules for the use of cookies in their privacy policy, so for security reasons we recommend that you read the privacy policy document before using these pages. We reserve the right to change this privacy policy at any time by publishing an updated version on our Website. After making the change, the privacy policy will be published on the page with a new date. For more information on the conditions of providing services, in particular the rules of using the Website, contracting, as well as the conditions of accessing content and using the Website, please refer to the the Website’s Terms and Conditions.

Nexocode Team