Thank you for Subscribing to Telecom Business Review Weekly Brief
Telecom Business Review | Saturday, October 29, 2022
In the telecom industry, AI uses advanced algorithms to look for patterns within the data, empowering telecoms to detect and predict network anomalies.
Fremont, CA: Considering people's disputes, forward-thinking CSPs have concentrated their AI investments on applications that support them manage these challenges. In these areas, AI has already started to deliver tangible business results.
Network Optimization
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
5G networks started to roll out in 2019 and will have more than 1.7 billion subscribers globally – 20% of global connections — by 2025. AI is important for enabling CSPs to build self-optimizing networks (SONs) to support this growth. These allow operators to optimize network quality about traffic information by region and time zone.
In the telecom industry, AI uses advanced algorithms to seek patterns within the data, enabling telecoms to detect and predict network anomalies. Because of using AI in Telecom, CSPs can actively fix troubles before customers are adversely impacted.
Predictive Maintenance
AI-driven predictive analytics support telecoms to offer better services using data, sophisticated algorithms, and ML techniques to forecast future results following historical data. This implies operators can use data-driven insights to oversee the state of equipment and expect failure according to patterns.
Implementing AI in telecoms also enables CSPs to vigorously fix issues with communications hardware, like cell towers, power lines, data center servers, and even set-top boxes in customers' homes. Shortly, network automation and intelligence will allow better root cause analysis and prediction of problems.
Long term, these technologies will support more strategic goals, like generating new customer experiences and dealing effectively with emerging business requirements.
Virtual Assistants for Customer Support
Another advantage of AI in Telecom is conversational AI platforms. Also called virtual assistants, they have learned to automate and scale one-on-one conversations efficiently. In addition, AI adoption in Telecom helps contend with massive support requests for installation, set-up, troubleshooting, and maintenance, which often overwhelm customer service centers. Through AI, operators can execute self-service capabilities that direct customers on how to install and operate their devices.
Robotic Process Automation (RPA) for Telecoms
CSPs have vast customers engaged in millions of everyday transactions, each sensitive to human error. RPA(Robotic Process Automation) is a form of AI-based business process automation technology.
RPA can improve telecom functions by enabling telcos to manage their back-office operations and large volumes of monotonous and rules-based actions. In addition, RPA frees up CSP staff for greater value-add work by streamlining the execution of complex, labor-intensive, and time-consuming processes, like billing, data entry, workforce management, and order fulfillment.
As a result, Telecom, media, and tech companies anticipate cognitive computing to "considerably transform" their companies within the next few years.
Fraud Prevention
Telecoms are utilizing AI's strong analytical capabilities to combat cases of fraud. AI and machine learning algorithms can discover anomalies in real-time, effectively decreasing telecom-related fraudulent activities, like unauthorized network access and fake profiles.
In addition, the system can automatically obstruct access to the fraudster as soon as the shady activity is detected, minimizing the damage. With industry estimates signifying that 90% of operators are aimed by scammers daily – amounting to billions in losses yearly – this AI application is particularly timely for CSPs.
Revenue Growth
AI can unify and make sense of a wide range of data, like devices, mobile applications, networks, geolocation data, detailed customer profiles, service usage, and billing data. Through AI-driven data analysis, telecoms can expand their subscriber growth rate and average revenue per user (ARPU) using smart upselling and cross-selling of their services. In addition, by expecting customer needs utilizing real-time context, telecoms can make the correct offer at the right time over the right channel.
More in News