By Dhirendra Pratap Singh
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 favorite shows. Such downtime can lead to a significant drop in customer satisfaction and loyalty, potentially prompting them to explore other service providers. This highlights the critical importance of network optimization to guarantee seamless data transmission and cater to the increasing needs of users.
Mobile data consumption is rapidly increasing. For example, Nokia India report says that the average Indian consumed 24.1 GB of mobile data per month in 2023, which is 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 optimization and Predictive maintenance
AI-driven network optimization is vital for improved connectivity and network performance. Telecom firms use machine learning to analyze real-time data, identify congestion spots, and optimize systems efficiently. Unplanned network outages resulting from equipment malfunctions disrupt services, inconvenience customers, and result in significant revenue losses.
Through analyzing historical network data patterns and user behavior, AI algorithms can predict potential network problems ahead of time. 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 has the capability to analyze patterns in traffic fluctuations, variations in signal strength, and metrics related to equipment performance. This enables the early detection of possible bottlenecks and equipment malfunctions before they develop into significant outages. Moreover, AI has the capability to enhance resource allocation by modifying network configurations according to real-time data. This ensures optimal bandwidth usage and reduces congestion effectively.
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 the ability of CSPs in monetizing AI traffic over networks, with 68%. while 69% of survey respondents believe AI will create more job roles within CSP businesses.
As per report from Valuates, global AI in telecommunications market is projected to reach a remarkable $19.17 billion by 2029. This exponential growth is fueled by the increasing adoption of AI across various applications within the telecommunications landscape, coupled with 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, recognize patterns, and create innovative solutions. This technology has the potential to revolutionize 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.
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 results in reduced 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 wider range of customer inquiries, offering instant support round the clock. Generative AI can play a more prominent role in advancing bot-type automation. Through natural language processing (NLP) algorithms, chatbots will enhance their ability to comprehend and address queries with greater precision, simulating human-like interactions and enhancing the overall user experience.
Fraud Detection
A report by Communications Fraud Control Association says, despite preventive measures, telecommunications fraud surged by 12% in 2023, resulting in a substantial $38.95 billion loss, which is 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 Solution analyze vast datasets, including call detail records (CDRs), subscriber data, and network traffic patterns.
Unusual behaviors like 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 are capable of analyzing user behavior patterns to develop individualized behavioral 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. Through machine learning algorithms, network traffic patterns will be constantly monitored to identify and address security risks in real-time. AI-driven behavioral analytics will detect unusual user actions and possible security breaches, improving fraud detection and prevention abilities.
Energy Conservation
The growing need for data results in a larger energy footprint for telecom networks. This leads to increased operational expenses and environmental challenges. 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 it to adapt network configurations and enhance the efficiency of network equipment power consumption. By utilizing a data-driven strategy, energy usage is greatly reduced while maintaining service quality. AI plays a crucial role in creating a more sustainable future for the telecommunications sector by optimizing energy consumption.
The potential for AI in the telecommunications industry is vast. Companies in the midst of 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.
