ai in telecommunications

AI in Telecommunications: Use Cases, Challenges & the Future

AI in telecommunications is soaring to new heights. Its market size, a substantial USD 841.85 million in 2023, is on track to reach an astounding USD 2808.96 million by 2028. This growth trajectory speaks volumes about AI’s impact on the telecom industry.  

By including AI in their business operations, telecom companies are improving on an unprecedented scale, slashing costs, and improving their service offerings. The benefits of AI in telecommunications vary from automating mundane processes to revolutionizing customer interactions. Yet, the transition to AI has its challenges.  

In this blog, you’ll learn about the benefits of AI in telecommunications and the industry’s challenges. 

AI in Telecommunications: Benefits 

Network Optimization and Efficiency 

Network optimization and efficiency are critical points for AI in telecommunications. Networks are the lifeblood of telecommunications companies, and those that use AI for optimization set a new standard for operational excellence and customer satisfaction.  

AI applications typically come as specialized software systems integrated into the telecom company’s infrastructure. The management and oversight of these systems require a collaborative effort from professionals with expertise in AI, machine learning, network engineering, IT, and cybersecurity. And as the telecom industry continues to evolve with AI, the demand for these skills will only grow.

Below are some of the most common use cases of AI for network optimization. 

1. Predictive Maintenance

Predictive maintenance uses AI to forecast equipment failures before they occur, reducing downtime. It usually involves software systems that continuously monitor and analyze data from network equipment. This data might include temperature readings, performance metrics, error messages, and historical failure patterns. 

These systems use machine learning models to identify signs that equipment is likely to fail. When the system predicts a potential failure, it alerts technicians to take preemptive action. 

Typically, a combination of network engineers and specialized AI or machine learning engineers would oversee the predictive maintenance system. They ensure the models are accurate and the system integrates well with the network’s monitoring tools. 

2. Traffic Flow Optimization

Traffic flow optimization intelligently manages massive data traversing networks to alleviate congestion and boost internet speeds. These software solutions analyze real-time network traffic patterns, understanding peak usage times, types of data being transmitted, and potential bottlenecks. This might mean rerouting traffic through less busy pathways or adjusting bandwidth allocation based on the type of data (e.g., streaming vs. web browsing).   

They’re typically managed by network operations teams, often with backgrounds in network engineering and computer science. 

3. Robotic Process Automation (RPA)

Robotic Process Automation (RPA) automates repetitive and labor-intensive tasks, freeing up human employees to focus on strategic initiatives. RPA involves “bots” or software agents that automate tasks such as data entry, billing, customer account updates, and even certain aspects of customer service. They work much faster and more consistently than humans. 

RPA is typically managed by IT professionals specializing in automation technologies. They identify tasks suitable for automation, configure the RPA software, and continuously monitor its performance. 

4. Anomaly Detection

Anomaly detection identifies unusual patterns that could indicate threats or system failures, enabling quick mitigation efforts. These systems monitor network traffic, performance data, and security logs for patterns or activities that deviate from the norm. These activities include unusual network traffic that indicates a cybersecurity threat to unexpected drops in performance.   

Machine learning models recognize “normal” patterns and flag anomalies as they occur, often in real time. Cybersecurity teams, network engineers, and data scientists may all play a role in managing anomaly detection systems. They aim to quickly identify and investigate anomalies to mitigate potential threats or issues. 

5. AI-driven Capacity Planning

AI-driven capacity planning enables networks to scale effectively by predicting future demands based on trends and usage patterns. This is often a cross-functional effort involving network planning teams, financial analysts, and data scientists. They collaborate to interpret the AI’s forecasts and integrate this information into the company’s strategic planning processes. 

These systems can predict when additional infrastructure, such as new cell towers or expanded bandwidth, will be necessary to meet demand. This helps companies plan capital expenditures and expansions strategically. 

Enhancing Customer Experience 

Aside from network infrastructures, AI in telecommunications is also changing how companies interact with their customers – much like we’re seeing across all industries.   

Automating customer service isn’t just about efficiency – it’s a major cost-saving move. AI’s ability to take over these tasks means resources can be allocated elsewhere, significantly reducing operational expenses.

AI Chatbots & Virtual Assistants

AI-driven chatbots and virtual assistants provide 24/7 customer support and can efficiently manage inquiries and complaints. With AI progressing, they are improving at processing natural language and consequently getting better results.   

Examples of AI chatbots in telecommunications include: 

  • Vodafone’s TOBi handles thousands of customer inquiries a month, alleviating the pressure from customer support teams.  
  • Elisa’s Annika is improving her language skills through machine learning and solution-finding capabilities. 

But AI doesn’t merely handle customer support tickets. McKinsey states that it can also prevent them with self-healing networks and systems. These systems automatically resolve issues before they impact the customer, reducing call volumes and improving customer satisfaction even more. This proactive maintenance goes a long way in building customer trust and loyalty, as it minimizes the inconvenience of service outages and technical issues. 

However, 90% of people still prefer customer service from a human rather than a chatbot. When customers have complex problems, interacting with chatbots can only cause frustration. 

Personalized Experiences for Customers

AI in telecommunications can also create more personalized and efficient customer interactions. For example, AI enables the ‘store-of-the-future’ experience, where customers benefit from highly personalized service when they enter a store.    

Machine learning algorithms take personalization even further. They analyze customer data in real-time to provide tailored product recommendations, streamline the checkout process, and even manage inventory so popular items are always in stock. These AI capabilities can significantly boost customer satisfaction by making every visit easy and personalized. 


AI in Telecommunications: Challenges 

Adopting AI in telecommunications brings its own set of hurdles. Learn more about them below. 

Scarcity of Technical Expertise

Finding an AI-skilled workforce can be tough, but it’s not an insurmountable obstacle. The key is in training and upskilling existing staff and be patient as AI has a steep learning curve. By taking this approach, telecommunications can reduce the expertise gap and boosts team morale by investing in their growth. 

Unstructured Data

Unlike structured data (databases and spreadsheets), unstructured data includes text, images, videos, and social media posts. It’s messy and doesn’t fit neatly into traditional databases, making it challenging for AI systems to interpret and analyze.   

Here are a few strategies to manage unstructured data: 

  • Data preprocessing tools are designed to organize and structure unstructured data, making it AI-ready. 
  • Natural Language Processing (NLP) can extract valuable insights from customer feedback, social media conversations, and other text-based sources. 
  • Upskilling your team in data science and AI capabilities can equip them to handle unstructured data challenges more efficiently. 

As telecommunications companies implement AI, they first need to set up processes for data management.  

Budget Constraints

A notable challenge within AI in telecommunications is the resource disparity among ISPs (Internet Service Providers). ISPs with larger budgets possess a distinct advantage in the “race” to harness AI’s full potential.  

These financial resources enable them to invest in cutting-edge AI technologies and talent, accelerating their innovation cycle and potentially widening the gap with less well-funded rivals. 

Competitive Market Pressures

The telecommunications industry is intensely competitive. The pace of technological advancement is relentless, with companies continually striving to innovate to capture and retain customer interest.  

For industry leaders, AI adoption is necessary to rise above the rest of the pack. Those who use AI to improve their networks and customer service gain a competitive edge.  

The Future of AI in Telecommunications 

The Ericsson blog highlights how GenAI (Generative AI) will redefine content creation and network management. Imagine AI not just analyzing data but creating new content, from text to images, and optimizing network operations based on learned data patterns. This capability pushes the boundaries of what AI can do, moving from predictive to innovative.   

Generative AI is part of a bigger picture that includes Large Language Models (LLMs). These models are lowering the barriers for voicebot implementations, allowing more natural interactions between consumers and chatbots.  

ai in telecommunications

Final Thoughts 

AI in telecommunications extends far beyond customer-facing chatbots. Its real power lies in anticipation, predicting future needs and challenges before they arise.   

This technological advancement promises to enhance the speed, reliability, and intelligence of telecommunications services for everyone involved, from the backbone of the industry—its employees—to the end-users and customers.   

Ready to explore the future of telecommunications and how it’s being shaped today? Download our eBook, “How 10G Changes Everything” to discover the technologies and innovations that will drive the next generation of connectivity – AI included.