It is hard to imagine a hotter topic than AI in telecommunication market. Over the past year the web got flooded with blogs, expert opinions, statistics and predictions on where AI can take us, what it can and can’t do, how to use it and implement it anywhere, from international telecom operators’ operations to basic customer support.
Even a brief google search would load you up on tons of information, most of which would claim AI to be the magic pill to cure all of your business’ diseases, from low sales to operational inconsistencies or failed strategic decisions.
Yet is it as powerful as people claim it to be? Yes and no. We at Intellias do not turn down the impressive potential of AI for telecommunications in creating more personalized services, more accurate billing or smarter network coverage. Still, we tend to believe AI is only an instrument. An instrument, that under the hand of the master, can turn into magic.
The added value of generative AI in the telecom industry
Even brief market research indicates that AI in the telecommunications market is rapidly expanding. Factors such as the growing need for efficient network management, improved customer experience, and the rising adoption of AI-driven technologies are fueling this growth. According to Precedence Research, the global AI in telecommunications market size is projected to reach around $14.99 billion by 2030, growing at a CAGR of approximately 40.2% from 2022 to 20301. This underscores the immense potential and the critical importance of AI for telecom companies aiming to stay competitive.
Why telecom businesses use AI
1. To enhance customer interactions with human-readable content
There’s a remarkable ability of generative AI for telecom to create and interpret text, images, audio, and video content. Why is it important for industry, you might ask. Well, how about automating the creation of service-level agreements, product documentation, and troubleshooting guides? AI can draft these documents in clear, understandable language, making complex information accessible to customers. Additionally, AI-driven chatbots and virtual assistants provide intuitive, dialogue-based support, mirroring actual human interaction.
2. To put big data to work
Telcos are among the world’s largest accumulators of data, collecting enormous volumes of network statistics, user behavior insights, logs, and more. AI-driven analytics tools help transform these raw, massive datasets into meaningful, actionable insights. By intelligently parsing through huge data streams, telcos can better understand usage patterns, forecast demand, enhance service quality, and drive strategic decisions that keep them ahead of market trends.
3. To optimize operations through machine-readable content
It’s no secret telecom networks generate huge amounts of data that are almost impossible to manage and interpret manually. But AI processes any machine-readable content within seconds, turning raw network data and logs into actionable insights. By analyzing network configurations and performance metrics, gen AI can create coverage maps, detect incidents in real-time, and recommend optimal configurations. How about that improving your overall service quality?
4. To streamline digital twin creation
Digital twins—virtual replicas of physical systems—are invaluable for testing, analysis, and optimization without affecting live networks. Traditionally, creating digital twins has been resource-intensive. Generative AI simplifies this process by learning from the behavior of physical network components and then efficiently creating accurate virtual models. What a playfield for telecom companies to experiment and refine network planning strategies in a risk-free virtual environment.
5. To uncover new revenue opportunities
AI’s analytical prowess enables telecom companies to delve deep into customer behaviors and market trends. By identifying patterns and preferences, AI helps in crafting personalized services and discovering untapped market segments. This strategic insight opens doors to new revenue streams, from customized service packages to innovative applications that meet emerging customer needs.
6. To Drive AI at the Edge
The future of telecom isn’t just about leveraging existing infrastructure for AI—it’s about placing those capabilities right where data is generated and consumed. Whether it’s enabling real-time network insights or powering conversational AI in telecom, integrating edge computing devices allows telcos to run AI workloads at the network’s periphery. This reduces the load on core systems, lowers latency, and supports immediate analytics and decision-making. Instead of pushing data across the network, operators can process it locally, deliver more responsive services, and ensure a superior user experience—all while improving operational efficiency and scalability.
Why AI is a natural fit for telecom
Artificial intelligence in telecommunications becomes more and more popular primarily because of its immense potential to transform core operations. A recent Frost & Sullivan industry report highlights that improving customer experience and optimizing network operations are the top benefits telcos expect from integrating AI, cited by 71% and 63% of surveyed companies respectively.
While AI integration presents challenges, telecom companies may be better equipped than they realize. For instance, network service providers that have deployed 5G networks manage vast infrastructures with numerous endpoints across multiple edge locations—similar to AI workloads. Their expertise in handling complex services and leveraging automation positions them well to embrace AI as a natural progression of their capabilities.
As a result, telecom companies are using AI to pursue use cases such as:
- Predictive maintenance
- Traffic flow optimization
- Network architecture enhancement
- Identifying new revenue opportunities
So integrating AI for telcos is not only about improving existing operations but also about new possibilities for growth and innovation in the industry.
Key elements for AI in telco
The integration of AI into the telecommunications industry is driving significant advancements in how networks are operated and managed. Three key elements are at the forefront of this transformation: zero-touch operations, trustworthy AI, and big data networks.
Zero-touch operations
AI holds a substantial role in network management providing a chance for a high level of autonomous operation. This shift leads to intelligent, self-managing networks that require minimal human intervention—often referred to as zero-touch operations. The real power of AI is realized within the network itself, enabling it to:
- Automate network functions: Routine tasks are automated, reducing the potential for human error and increasing efficiency.
- Implement intent-based networking: Networks can understand and act upon high-level business intentions, translating them into optimal network configurations.
- Adopt AI-native designs: Networks are built with AI capabilities at their core, ensuring that AI is not an add-on but an integral part of the network’s functionality.
Trustworthy AI
AI-enhanced telecom services have to be trustworthy. Meaning, businesses have to ensure that AI systems are transparent, secure, and operate with human oversight where necessary. Building trustworthy AI encompasses:
- Explainable AI: Developing AI models whose decisions and processes can be understood and interpreted by humans, fostering confidence in automated outcomes.
- User-centric design: Crafting user experiences that make interacting with AI intuitive and reassuring, which helps in gaining user trust.
- Robust security measures: Incorporating built-in safety mechanisms to protect against threats and ensure the integrity of AI systems.
Big data networks
The rollout of 5G telecommunications networks introduces new complexities due to the vast amounts of data and the need for real-time processing. AI-driven telecom strategies transform these challenges into opportunities by:
- Leveraging big data analytics: Combining large-scale data with network expertise to gain insights that enhance network performance and user experience.
- Optimizing network operations: Using AI to predict network demands, prevent outages, and efficiently manage resources.
- Personalizing services: Analyzing data to offer tailored services to customers, thereby increasing satisfaction and loyalty.
Challenges of implementing AI in the telecom
The telecom industry stands on the brink of a transformative AI revolution, yet the journey toward full integration is anything but straightforward. While the opportunities are vast, the challenges that telecom companies face in implementing AI solutions are equally significant.
One of the foremost issues is the need for comprehensive and high-quality data. Telecom operators must collect extensive datasets, often requiring collaboration and data sharing with external partners. This data must be transferred swiftly to the right locations, processed rapidly to yield timely and accurate insights, and then translated into actionable strategies that drive business value.
Source: Equinix
Compounding this complexity is the necessity to manage costs effectively while adhering to sustainability goals. Even after deploying AI models that deliver valuable results, companies must continuously retrain and update these models to maintain their accuracy over time—a process that demands ongoing resources and attention.
It’s a substantial undertaking, and it’s understandable why many network service providers feel they lack the necessary infrastructure and expertise. From an infrastructure standpoint, AI workflows are most effective when distributed appropriately across different environments. The AI process is inherently iterative, involving two main types of workloads with distinct requirements:
- Model inference workloads: These are highly sensitive to latency and require real-time data processing. Hosting them at the network’s edge ensures faster response times and more efficient operations.
- Model training workloads: This aspect is resource-intensive and benefits from the substantial computational power available in core data centers or cloud environments.
The challenge arises in orchestrating these workloads across various locations. Network service providers must manage the complexities of running different AI tasks in different environments, ensuring seamless integration and performance. This demands a flexible, scalable infrastructure capable of supporting distributed AI workflows without compromising speed or efficiency.
AI use cases in telecom
Having examined the key challenges in AI for telecommunications providers and potential solutions, let’s now explore specific technical domains where AI truly shines. For companies offering AI consulting services, grasping these vital AI-driven areas is essential to offer valuable insights.
Source: Appinventiv
Network planning and optimization
AI-enabled traffic analyzers do a great job of recognizing malfunctions and bottlenecks long before they become visible to network administrators. And when it’s time to act, AI-enabled systems can modify network configurations and reroute traffic to healthy nodes in response to local equipment failures and bottlenecked channels.
Sentiment analysis
Generative AI in telecom can be used to process and interpret customer feedback, helping CSPs uncover more insights and upcoming trends. Thus, companies can identify crucial issues that need to be fixed, respond proactively to clients’ needs and care for reputation management.
Fraud prevention
One of the things that AI in telecom can do exceptionally well is detect and prevent fraud. Processing call and data transfer logs in real-time, anti-fraud analytics systems can detect suspicious behavioral patterns and immediately block corresponding services or user accounts. The addition of machine learning enables such systems to be even faster and more accurate.
Revenue assurance
One of the things that AI in telecom can do exceptionally well is analyze vast volumes of transactional data to detect and prevent fraud, anomalies, or irregular billings and revenue collection processes. The use of AI helps telcos confidently safeguard revenue streams while maintaining regulatory compliance.
AI-assisted billing
The use of artificial intelligence in the back office helps streamline and automate various business-critical processes, analyze usage patterns, generate accurate invoices in real-time, spot errors and enhance overall billing transparency and accuracy.
AI-driven robotic process automation (RPA)
RPA has always been the number one choice for all digital transformation projects. If implemented correctly, it will deliver tangible value from day one by reducing document processing times and accelerating business flows. With AI applied to RPA, the performance-boosting effect is even more profound, allowing for anomaly detection and (semi-)automatic error correction.
Predictive maintenance
Predictive analytics, which identifies patterns in historical data, provides early warnings about potential hardware failure, so that telecom companies can address issues before they arise, minimizing customer support requests and enhancing the overall customer experience.
Intelligent virtual assistants
Virtual assistants and AI-driven chatbots are gradually replacing live operators at telcos for cost-saving purposes and in order to offer customers a faster, more convenient way of getting answers to their questions and resolving their issues.
AI for telecommunications: real-life examples
Having covered a number of challenges and applications of AI in telecommunications, let’s get a quick glimpse at some real industry cases.
Verizon Communications
Verizon is investing heavily in AI and ML technologies to improve network performance and customer service. A partnership with Cellwize led to an intelligent platform that streamlines the rollout of Verizon 5G sites and simplifies network application development. Other AI-related partnerships include one with IBM and another with Google, whose Cloud Contact Center AI service will offer Verizon customers a more intuitive and natural way of interacting with Verizon’s support service.
Content distribution platform for an online media company
A global online media company faced scalability challenges in one of its R&D centers due to a shortage of machine learning developers. In response, they sought a partner to enhance their engineering capacity for their digital content distribution platform. In 2017, Intellias provided an engineering team that integrated with the client’s Agile in-house teams, working on various software components. The partnership involved backend and frontend development, test automation, DevOps, and support.
Vodafone
The British telecom giant Vodafone Group launched an assistant app called TOBi, a highly intelligent text bot capable of supporting users in dealing with issues, managing subscriptions, and purchasing new equipment and services.
AWS Migration Services
Intellias collaborated with a major national telco, helping them transition to AWS for enhanced data processing and business intelligence. The telecom provider sought to optimize costs, improve scalability, and accelerate growth through AWS migration. Intellias has designed a custom cloud solution architecture, assessed resource requirements, and estimated infrastructure costs.
Deutsche Telekom
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, to intelligent business planning tools, Deutsche Telekom is actively embedding AI elements into its infrastructure and service portfolio.
Source: Digitalist Magazine
The journey of AI implementation in telecom
Successful integration of AI in telecommunications has become the key to the striving business – it helps enhance service operations, automate tedious routines, and create better customization for your customers. But before you start, make a plan and draw invaluable lessons from trailblazers who have already paved the way.
Here’s what we have found out to be the best practice at Intellias.
1. Assess your business needs
Before you jump in implementing AI in every possible process within your company, try evaluating its expediency. Not every part of your business has to be powered by AI, sometimes it might even hurt. See into your business operations and define which parts of network, customer service, billing, marketing, security, or else need AI.
2. Extract suitable data and get ready
A solid data foundation is crucial. Collect relevant data from your billing records, customer interactions, and network logs, and check market trends. You will use this data to train AI models, so make sure it is clean, organized and properly labeled.
3. Select AI technologies
Following up on point one, choose AI models that are tailored to your specific needs of running telecom operations. Train them using historical data you have previously collected and validate their performance through testing and evaluation.
4. Integrate it with existing systems
Merge AI models with your existing telecom system and infrastructure. Consider partnering with professional IT teams or software development vendors for a 100% compatibility and seamless operation.
5. Test and validate
Before trusting AI functionality, conduct a thorough testing to verify its accuracy and performance. Try out different scenarios and conditions to make sure your business is AI ready and would benefit from it.
6. Deploy and monitor
If AI models prove to be useful and valid for your business, deploy them into production environments. Still, continue monitoring their performance and gather feedback from users to identify flaws and areas of improvement.
7. Issue improvement
Make iterations of improvement based on the feedback and performance metrics you gather. Retrain the AI models you chose, if necessary, i.e. if you get updated data, or set new parameters, or add new features to the product.
8. Train your employees
AI models, no matter how impressive and effective, still need to be monitored by human professionals. Make sure your team is well-familiarized with AI technologies and tools you chose to implement.
Future of AI in the telecom industry
AI models and the possibilities of their implementation are already enormous and growing. Sometimes it seems nearly impossible to accurately predict the development of this technology even a year ahead, yet there are some tendencies that experts highlight.
First goes the rise of autonomous network management, where AI-driven systems would optimize resource allocation and performance preventing network failures and ensuring uninterrupted service for customers.
Second, there’s expected the rise of number and quality of AI-powered virtual assistants for personalized customer support, real-time service and service recommendations.
Also, AI-fueled predictive analytics is expected to create an easier flow for business problem-solving and identifying potential issues before they escalate.
Bottom line
Artificial intelligence in telecom is definitely a topic worthy of careful consideration. Apart from holding an incredible potential of automating and simplifying tedious routine tasks like billing or basic customer support, AI solutions promise to redefine the whole industry as we know it. They promise a world where every interaction is smarter, every operation more efficient, and every connection more meaningful. Don’t miss your chance to be a part of it!
Contact our experts to learn more about how to get a competitive advantage and maximize the efficiency of your business by embedding AI into your operations and customer service.
