Powering up smarter, intelligent telecom networks

AI-driven network optimisation ensures seamless connectivity and reliability amid rising mobile data demands, boosting customer satisfaction and efficiency.

The growing dependence on mobile data creates significant pressure on telecom networks. Imagine the disappointment of millions of users facing unexpected internet disruptions during crucial business video calls or while watching their favourite shows. Such downtime can significantly decrease customer satisfaction and loyalty, potentially prompting them to explore other service providers. This highlights the critical importance of network optimisation in guaranteeing seamless data transmission and catering to users’ increasing needs.

Using AI for predictive maintenance allows telecom operators to identify and avoid network failures, reducing downtime and improving user satisfaction.

Mobile data consumption is rapidly increasing. For example, the Nokia India report says that the average Indian consumed 24.1 GB of mobile data per month in 2023, a 24% increase from 2022. GSMA predicts that mobile data traffic in Europe will almost triple between 2023 and 2028. The industry rolled out 5G to address the increasing data demands and to support new use cases. This new standard brought about additional frequency bands, various radio types, increased base stations, and a broader range of connected devices, leading to more network complexity. Additionally, as the network becomes more open, vendor complexity is increasing.

Network Optimisation and

Predictive Maintenance

AI-driven network optimisation is vital for improved connectivity and network performance. Telecom firms use machine learning to analyse real-time data, identify congestion spots, and optimise systems efficiently. Unplanned network outages resulting from equipment malfunctions disrupt services, inconvenience customers, and result in significant revenue losses.

AI algorithms can predict potential network problems by analysing historical network data patterns and user behaviour. This leads to consistent network performance, minimal downtime, and the capability to address issues proactively through preventive maintenance. Using AI for predictive maintenance will allow telecom operators to predict and avoid network failures, reducing downtime and improving user satisfaction.

For example, AI can analyse traffic fluctuation patterns, signal strength variations, and equipment performance metrics. This enables the early detection of possible bottlenecks and equipment malfunctions before they develop into significant outages. Moreover, AI can enhance resource allocation by modifying network configurations according to real-time data. This ensures optimal bandwidth usage and effectively reduces congestion.

A global study of telecom and IT engineers about AI and the network by Ciena says that India boasts the highest proportion of respondents of any region that are very confident in CSPs’ ability to monetise AI traffic over networks, with 68%. In comparison, 69% of survey respondents believe AI will create more job roles within CSP businesses.

With its ability to optimise energy consumption, AI can play a crucial role in creating a more sustainable future for the telecommunications sector.

According to a report from Valuates, global AI in the telecommunications market is projected to reach a remarkable USD 19.17 billion by 2029. This exponential growth is fuelled by the increasing adoption of AI across various applications within the telecommunications landscape and the proliferation of AI-powered smartphones.

Generative AI in Telecom

In the face of high competition and cost-cutting in the telecommunications industry, initial signs indicate that generative AI might be the key to sparking growth following a decade of stagnation.

Generative AI enables telecommunication companies to handle large volumes of data, recognise patterns, and create innovative solutions. This technology has the potential to revolutionise traditional approaches and drive industry-wide advancements. Embracing generative AI allows telecom firms to address obstacles, discover new revenue sources, enhance operational effectiveness, and provide outstanding customer service. One McKinsey study found that software developers can complete coding tasks up to twice as fast with gen AI.

Delivering Intelligent Customer Service

Sustaining dependable and top-notch service within intricate networks involving various technologies poses a significant operational challenge, demanding ongoing monitoring and issue resolution. Conversely, poor service quality reduces customer satisfaction, attrition, and revenue decline. A report by Emplifi found that 63% of consumers would leave a brand because of poor customer experience.

AI-driven chatbots and virtual assistants are set to transform customer service within the mobile telecom sector. These AI agents will manage a broader range of customer inquiries, offering instant support around the clock. Generative AI can play a more prominent role in advancing bot-type automation. Through natural language processing algorithms, chatbots will enhance their ability to comprehend and address queries more precisely, simulating human-like interactions and enhancing the overall user experience.

Enabling Fraud Detection

A Communications Fraud Control Association report says that telecommunications fraud surged 12% in 2023 despite preventive measures, resulting in a substantial USD 38.95 billion loss, equivalent to 2.5% of total sector revenues. Fraudulent activities such as subscription fraud, SIM box fraud, and international revenue share fraud pose a multi-billion-dollar challenge for the telecommunications industry.

AI-powered solutions analyse vast datasets, including call detail records, subscriber data, and network traffic patterns.

Unusual behaviours, such as abnormal call lengths, a large number of international calls, or peculiar call ending patterns, are promptly identified and flagged in real time. AI-driven systems can analyse user behaviour patterns to develop individualised behavioural profiles for each user. This aids in identifying account takeovers or instances of identity theft.

AI is set to have a vital impact on boosting network security and protecting privacy within the mobile telecom ecosystem. Machine learning algorithms can constantly monitor network traffic patterns to identify and address security risks in real time. AI-driven behavioural analytics detect unusual user actions and possible security breaches, improving fraud detection and prevention abilities.

Driving Energy Conservation

The growing need for data results in a larger energy footprint for telecom networks, which leads to increased operational expenses and environmental challenges. A GSMA report highlighted that the telecom sector contributes to approximately 3% of global electricity consumption, highlighting the need for sustainable solutions.

AI-Powered Solution constantly examines network traffic patterns and user actions in real time, enabling them to adapt network configurations and enhance the efficiency of network equipment power consumption. Using a data-driven strategy greatly reduces energy usage while maintaining service quality. With its ability to optimise energy consumption, AI can play a crucial role in creating a more sustainable future for the telecommunications sector.

The potential for AI in the telecommunications industry is vast. Companies amid a digital transformation are achieving success by leveraging AI early on and developing suitable software. With AI’s ability to process massive amounts of data and human expertise, the possibilities are endless. 

By Dhirendra Pratap Singh

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