AI in Telecom: Reshaping the Global Landscape

The AI in telecommunications market projected to be worth $38.8 billion by 2031, growing at a CAGR of 41.4% between 2022 and 2031. This rapid acceleration in AI adoption is going to be driven by the growing demand for improved customer experiences and the need to rationalize capital expenditures.

Global telecom companies that are more likely to emerge as the leaders in this scenario would be the operators who can drive value transformations from the top. This would require the active support of telecom CXOs for in enabling a strategic, AI-centric change management journey across the organization.

To understand the growing need for the adoption of AI, let us look at some of the most recent market instances. An UK-based telecommunications major recently announced that by 2030, AI will be able to replace 10,000 roles in its operations. A Japanese telecommunication service provider (TSP) announced that with AI, they have been able to reduce RAN energy consumption by half. And an American Telecommunications company was able to decrease their customer call abandonment rates by 62% with Ai, transforming the existing customer service experience in the process.

Instances such as these are indicative of how AI is reshaping the global telecom landscape. But a question remains.

Is AI the only hero?

AI and ML models are just 40% of the solution, and what turns out to be the pivotal aspect is the data. It is important to assess if the data is in right shape with effective architectures and governance in place. One of the major problems the that the telecom service providers face today is the integration and interpretation of the avalanche of data that is produced by the networks, connected devices, social media, call records, billing information, etc.

Uncovering correlation between these highly dimensional data space and creating actionable insights is a challenge that most excites the data engineering teams.

So how can TSPs leverage AI?

The growth in computing power, multi-layered data streams, and the advancement of algorithms that can capture much more sophisticated problems and signatures are driving the growth of AI in telecom. Customer service and network Maintenance are two key areas where AI is being leveraged.

Customer services related use cases that AI/ML is solving, includes:

• Anticipating possible service issues and resolving them before the customer notices;
• Optimizing of service operations, like in-store customer experience, customized marketing campaigns, and the deployment of employees in the fields, in the stores and the call centers;
• Streamlining of customer self-service with GenAI enabled human like interactions; and
• Detecting and preventing frauds in areas like subscriber management, billing, and undertaking proactive efforts to protect customer data and networks with of AI algorithms.

Network maintenance related use cases that AI/ML is helping address, includes:

• Detecting and preventing fraudulent activities on the networks and within the customer accounts,
• Reducing in the number of the field dispatches,
• Eradicating robocalls, and
• Enabling AI-powered systems to restart cell towers automatically during any network failures or performance issues or , and
• Optimizing network behavior on real-time weather data, wind speed, etc.

Charting the future

CSPs and TSPs worldwide are deploying 5G in preparation for driving next-gen network connectivity. Networks in the future are going to be more complex, with a large volume of data arising from increasingly connected and intelligent device. We would also need to prepare for zero-touch operations to be able to cope with the size, complexity and reduced lead time for decision making that will be required in this data-surplus scenario.

It is therefore important that the AI systems in the mobile networks are fair, accountable, reliable, secure, and transparent. These elements are crucial to ensure that humans can understand how and why the AI algorithms arrived at the specific decision and be able to establish trust in the AI systems.

LTTS is working to address all these challenges to ensure AI lives up to its full economic potential. Amongst several of our key focus areas for leveraging across industries, our global teams have developed an AI-enabled solution to predict RAN failures 24 hours in advance. This proactive maintenance solution leverages historical Fault Management (FM), Performance Management (PM), and equipment event logs, maintenance records, weather data etc., in predicting RAN failures with 90% accuracy, helping reduce network downtime by 5%-15%.

We foresee that AI will continue to unlock more such robust value streams across the global telecom landscape.