By: Brian Coombs

To date, most industry use cases for AI have been limited to extracting insights from customer and network-generated data using big data and machine learning. Now though, GenAI solutions such as ChatGPT and Claude are making it easier than ever for telcos to automate many different processes and offer new services to customers.

A recent survey by AWS found that adoption of AI in telecoms will hit 48 percent in the next two years, but already, many of the world’s largest telcos are well on their way to integrating emerging technologies and dedicated language models into their business processes.

For example, South Korea’s SK Telecom was well ahead of the curve when it announced mere weeks after ChatGPT’s initial release that it was putting AI at the core of its business, partnering with Deutsche Telekom to develop their own telecom-focused Large Language Model (LLM) for CSPs to deploy GenAI models efficiently and quickly. Trained on billions of specialized industry parameters, these models can handle complex tasks, while requiring less power and equipment.

How can communications services providers incorporate AI and LLMs into their everyday processes? Here are three key areas that I think could be radically transformed:

Network Optimization

In the all-AI telco, real-time analytics will continuously monitor every area of the network, which will then adapt to changing conditions seamlessly, self-optimizing and self-healing from faults.

Network quality of service will be dynamically adjusted, easing strain and energy usage on physical networks and reducing capacity at times of lower demand. When needed, autonomous drones carrying picocells will supplement networks to provide additional capacity when traffic increases or is anticipated to.

As vast quantities of data are created by users, AI can be used to optimize complex operations, improve service assurance and reduce operating costs, using real-time reports to monitor traffic and resolve potential issues before they occur. Analysis of this behavioural data can provide further improvements to services and predict usage trends.

A new model developed by the University of Surrey gives us a look at what such an autonomous network could be, utilizing “constrained combinatorial optimization with deep reinforcement learning” to save 76 percent in resources and decrease energy use, with only a 23 percent increase in compute costs.

For future network planning, digital twins will provide real-time insights into system performance, predict potential failures, and simulate various scenarios to enhance decision-making processes and improve overall efficiency.

Business Operations

Thanks to AI, day-to-day business processes will be largely automated, helping telcos reduce their operational costs, optimise supply chains, improve inventory management, and make suggestions for new business opportunities.

AI agents will oversee billing and revenue management operations, identifying errors and preventing revenue leakage, whilst ensuring that customers are charged correctly. With most processes running automatically, any human interventions would be targeted where they can deliver the most value.

By automating product creation through natural language text or voice commands, users will build new products and packages in seconds. Furthermore, image recognition will auto-create from sketches and drawings, transforming how products are conceived and deployed. Products will also be created dynamically at sale time, tailored to individual customers based on their history, business goals, and margins to maximize profits.

On the network management side, augmented reality will enable technicians to visualize and solve complex issues remotely, leading to faster resolution times and reduced downtime.

Customer Experience

Beginning in the back-end of the AI telco, customer profiles will be generated automatically based on patterns of behavior, with chatbots capable of understanding and resolving inquiries providing highly efficient and responsive customer service,reducing wait times and improving overall satisfaction. All marketing efforts