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No longer limited to providing basic phone and internet service, the telecom industry is at the epicenter of pushing the wide adoption of cutting edge technologies, led by mobile and 5G broadband services in the Internet of Things (IoT) era. This growth is expected to continue due to the rapid adoption of AI in telecommunications to support networks and customers at scale. Valuates projects that the global AI In telecommunication market size will reach $14.99B by 2027, from $11.89B in 2020, at a CAGR of 42.6% during 2021-2027.
What is driving the widespread adoption of AI by telecoms? AI has proven itself essential to the telecoms’ digital transformation strategy as it addresses the key challenges telecoms face today.
The Challenges that AI in Telecommunications Can Address in 2023
Poor Network Management
Global traffic and the need for more network equipment are growing dramatically, resulting in more complex and costly network management.
Lack of Data Analysis
Telecoms struggle to leverage the vast amounts of data collected from their massive customer bases over the years. Data may be fragmented or stored across different systems, unstructured and uncategorized, or simply incomplete and not very useful.
High Costs
Following massive investments in infrastructure and digitalization, industry analysts expect telecoms’ global operating expenditures to increase by billions of dollars. Many telecoms face a financial crunch and must find ways to improve their bottom lines.
Crowded Marketplace
Telecom customers are demanding higher quality services and better customer experience (CX) and are known to be especially susceptible to churn when their needs are not met.
2023 Common Applications of AI the Telecommunications Sector in 2023
The telecom industry is at the forefront of technological innovation, and artificial intelligence (AI) is playing a major role in this transformation. AI is being used to improve network performance, automate customer service tasks, and develop new products and services.
One of the most important ways that AI is being used in the telecom industry is to improve network performance. AI can be used to analyze data from network sensors to identify potential problems before they occur. This allows telecom providers to take proactive steps to fix problems and prevent outages.
Here are some specific examples of how AI is being used in the telecom industry in 2023:
Network optimization
AI is being used to analyze data from network sensors to identify potential problems before they occur. This allows telecom providers to take proactive steps to fix problems and prevent outages. For example, companies are using AI to predict network congestion and proactively reroute traffic to avoid outages. 5G networks began to roll out in 2019 and are predicted to have more than 1.7 billion subscribers worldwide – 20% of global connections — by 2025. AI is essential for helping CSPs build self-optimizing networks (SONs) to support this growth. These allow operators to automatically optimize network quality based on traffic information by region and time zone. AI in the telecom industry uses advanced algorithms to look for patterns within the data, enabling telecoms to both detect and predict network anomalies. As a result of using AI in telecom, CSPs can proactively fix problems before customers are negatively impacted.
Customer service automation and Virtual Assistants
AI-powered chatbots can answer customer questions and resolve issues without the need for human intervention. This can free up customer service representatives to focus on more complex issues. For example, Verizon is using AI to power its Virtual Assistant, which can answer customer questions about billing, service plans, and technical support.
Predictive Maintenance
AI-driven predictive analytics are helping telecoms provide better services by utilizing data, sophisticated algorithms, and machine learning techniques to predict future results based on historical data. This means operators can use data-driven insights to monitor the state of equipment and anticipate failure based on patterns. Implementing AI in telecoms also allows CSPs to proactively fix problems with communications hardware, such as cell towers, power lines, data center servers, and even set-top boxes in customers’ homes. In the short term, network automation and intelligence will enable better root cause analysis and prediction of issues. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with emerging business needs.
Robotic Process Automation (RPA) for Telecoms
CSPs have vast numbers of customers engaged in millions of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI. RPA can bring greater efficiency to telecom functions by allowing telcos to more easily manage their back-office operations and large volumes of repetitive and rules-based actions. RPA frees up CSP staff for higher value-add work by streamlining the execution of complex, labor-intensive, and time-consuming processes, such as billing, data entry, workforce management, and order fulfillment. According to Statista, the RPA market is forecast to grow to 13 billion USD by 2030, with RPA achieving almost universal adoption within the next five years. Telecom, media, and tech companies expect cognitive computing to “substantially transform” their companies within the next few years.
Fraud Prevention
Telecoms are harnessing AI’s powerful analytical capabilities to combat instances of fraud. AI and machine learning algorithms can detect anomalies in real-time, effectively reducing telecom-related fraudulent activities, such as unauthorized network access and fake profiles. The system can automatically block access to the fraudster as soon as suspicious activity is detected, minimizing the damage. With industry estimates indicating that 90% of operators are targeted by scammers on a daily basis – amounting to billions in losses every year – this AI application is especially timely for CSPs.
Revenue Growth
AI in telecommunications has a powerful ability to unify and make sense out of a wide range of data, such as devices, networks, mobile applications, geolocation data, detailed customer profiles, service usage, and billing data. Using AI-driven data analysis, telecoms can increase their rate of subscriber growth and average revenue per user (ARPU) through smart upselling and cross-selling of their services. By anticipating customer needs using real-time context, telecoms can make the right offer at the right time over the right channel.
AI in Telecommunications: Real Customer Success Stories
| Challenge | Name of Company | Impact |
|---|---|---|
| Accelerate digital transformation and improve CX | Vodafone | Increase in NPS |
| Business continuity in a crisis | WCTel | Reduced technician dispatch rate |
| High call volume and rising operational costs | Orange Spain | Reduced FCR |
| Continue operations during pandemic | Verizon | Business continuity |
The Future of AI in Telecommunications
AI in the telecom market is increasingly helping CSPs manage, optimize and maintain infrastructure and customer support operations. Network optimization, predictive maintenance, virtual assistants, RPA, fraud prevention, and new revenue streams are all examples of telecom AI use cases where the technology has helped deliver added value for enterprises.
As big data tools and applications become more available and sophisticated, the future of AI in the telecom industry will continue to develop. Employing AI, telecoms can expect to continue accelerating growth in this highly competitive space.
