The application of artificial intelligence in the telecommunications industry and the transformation of the global landscape

It is expected that the value of artificial intelligence in the telecommunications market will reach US$38.8 billion by 2031, with a compound annual growth rate of 41.4% between 2022 and 2031. With the growing demand for customer experience improvements and the need to rationalize capital expenditures, the adoption of artificial intelligence will accelerate rapidly

In this case, operators who can drive value transformation from the top are more likely to Become a global leader in telecommunications. To achieve an AI-centric strategic change management journey, active support from telecom CXOs is required, spanning the entire organization

To understand the growing demand for AI, let’s look at some latest market examples. Recently, a British telecommunications giant announced that artificial intelligence will be able to replace 10,000 jobs in its operations by 2030. Japanese telecom service providers have announced that they have successfully reduced the energy consumption of their radio access networks (RAN) by half through the use of artificial intelligence. A U.S. telecommunications company used artificial intelligence to successfully reduce customer call abandonment rates by 62%, thereby changing the existing customer service experience

Although there are examples such as this that show that artificial intelligence is How to reshape the global telecommunications landscape, but a question remains

Is AI the only hero?

AI and ML models are only 40% of the solution, and data is the key. It is important to assess whether data is in the right state and effective architecture and governance are in place. One of the major problems currently faced by telecom service providers is integrating and interpreting large amounts of data from networks, connected devices, social media, call records, billing information, etc.

Revealing correlations between high-dimensional data spaces and creating Actionable Insights The Biggest Challenge Exciting Data Engineering Teams With the advancement of signature algorithms, artificial intelligence applications in the telecommunications field are being promoted. Customer service and network maintenance have emerged as two key areas in this development

Customer service related use cases currently being addressed by AI/ML include:

Predicting possible service issues and resolve them before customers even notice

Optimize service operations such as in-store customer experience, customized marketing campaigns, and employee deployment in the field, stores, and call centers

• Streamline customer self-service with GenAI, enable Human Interaction
• Detect and prevent fraud in areas such as user management and billing, and leverage artificial intelligence algorithms to proactively protect customer data and networks.
• Use cases that AI/ML can address in network maintenance include:

Detect and prevent fraudulent activity on the network and within customer accounts

Reduce Number of field dispatches

• Eliminate robocalls
• Enables AI-driven systems to automatically reboot cell towers during any network outage, or performance issues
• Optimizes real-time weather data, Network behaviors such as wind speed
• Describe the future
• CSPs and TSPs are promoting 5G deployment globally to cope with future network connectivity needs. Future networks will become more complex, and as connections and smart devices increase, large amounts of data will be generated. To adapt to this data glut, we need to be prepared for zero-touch operations to cope with the scale, complexity and shrinking time to prepare for decisions

In mobile networks, AI systems must be fair, responsible , reliability, security and transparency. These elements are critical to ensuring that humans can understand how and why AI algorithms make certain decisions, and to be able to build trust in AI systems

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