use of ai in telecommunication

AI phones, AI notebooks, AI routers – generative AI and automation will undoubtedly help shape future communication. But beyond this futuristic vision, there are already real applications for the telecommunications industry.

The telecom industry is all about data – more than any other industry. At the same time, it faces challenges from pressure to reduce costs and the need to modernize outdated IT systems. According to PwC, global data consumption over telecom networks is estimated to almost triple in the future – from 3.4 million petabytes in 2022 to 9.7 million petabytes in 2027. If used correctly, data can help modernize the industry, change the way people work, and enable higher-quality customer service.

Artificial intelligence and automation will undoubtedly shape this development. While automation is about getting a task done without any intervention, AI intelligently decides what should happen. Together, the technologies form the basis for the next stage of the digital transformation of the telecommunications industry. The possible applications range from customer experience to field service to sustainability issues.

Get answers faster

Societal and technological changes are impacting customer expectations, priorities and behavior. Recent research [PDF] found that 81 percent of customers expect faster service due to technological advances. In the future, generative AI tools and large language models (LLMs) will therefore play an increasingly important role in meeting these customer needs.

AI and automation in telecommunications can help improve the customer experience in general. Large language models offer a faster and more efficient way to access data than traditional technologies. For example, suppose a consumer has a question about installing their router and wants to contact their internet provider. In that case, they can use LLMs to quickly and easily find the information they need. LLMs can also make it easier for customer service employees to find information for their customers. This is because such information is often available but difficult to find, and therefore often leads to negative customer experiences.

Easy access to data

For business users, analyzing data has traditionally been extremely complex. Thanks to generative AI, even non-technical users can now access and analyze data. The ability to ask questions in natural language makes it easier for business users to extract the information they need from their data. For network engineers, for example, LLMs offer the ability to find and extract information about local patterns without having to deal with complicated and confusing data processing.

This way, the benefits of data analytics are available to employees across the organization, rather than limited to a department of trained, well-informed data scientists. The result: companies can become more data-driven operations in a way that is accessible to everyone, whether they’re sitting at a desk or working in the field.

Efficient technician deployment

AI technology is known to be very useful when it comes to gathering data for strategic network engineering decisions, but it can also be of great help in on-site incidents. For example, if a tree falls and damages a cell tower, it is often difficult for employees to make the right decisions remotely. Typically, telecom providers send their employees to the affected site to first get an idea of ​​the situation.

LLMs can be used to get the information needed faster. They allow teams to access geo-referenced data from location-based services, which, when combined with satellite imagery, gives emergency responders a complete picture of what is happening. This allows telecom providers to create more efficient operations and dispatch the right person at the right time.

Sustainability through autonomous networks

Efficiency is also becoming increasingly important when it comes to meeting sustainability targets in telecommunications. Companies in this sector are under growing pressure from consumers, investors and regulators to reduce their carbon footprint and achieve net-zero emissions. At the same time, telecommunications companies are facing increasing demand for their services, triggered by remote work, digitalization and cloud-based solutions, among others. Energy-efficient technologies such as autonomous networks will play a crucial role in global decarbonization efforts.

Just as an autonomous car saves fuel by intelligently maintaining the optimal speed without accelerating or decelerating unexpectedly, autonomous networks automatically find the optimal configuration for the network, thereby reducing energy consumption. AI and machine learning (ML) can automate network management tasks. Ideally, this allows for significant cost savings, faster responses to network problems, and an improved customer experience. In the future, autonomous networks will increasingly control their own energy consumption and operations – and thus be both more powerful and more sustainable.

Basis for other industries

The telecommunications industry is evolving beyond its traditional services and becoming the foundation of other industries. From transportation to healthcare, billions of consumers around the world rely on the seamless exchange of data every day. The smart use of automation and artificial intelligence promises to make customers happier, make operations greener, and encourage the development of innovative services. Telecommunications leaders should recognize how they can capitalize on accessible data, move beyond legacy technologies, and use AI paired with automation to create a smarter future. Given the telecommunications sector’s central role to other industries and society at large, seizing these opportunities is critical.


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