Almost every piece of modern technology today has, in one way or the other, been optimized, discovered, or created with the help and involvement of Artificial Intelligence & Machine Learning technologies. The telecom sector has also evolved in the past decade. With the increased usage of smartphones and the internet, telecom has become an industry where operations have become intricate in its complexities.
According to research conducted by the International Data Corporation (IDC), the worldwide telecom industry was last valued at $1.53 trillion, and this number is expected to grow steadily with time. This is a clear indicator of how vast and still expanding the telecom market is in the world. AI & ML technology provides much-needed optimization, customer relations management, feasible marketing options, predictive maintenance, autonomous operations, etc.
Applications of AI & ML in the Telecom Industry
Enhanced Customer Service
AI-enabled chatbots, virtual call assistants, and Customer Relationship Management (CRM) software help ensure that the customers’ queries are easily satisfied. Employing this software goes beyond just customer satisfaction.
According to a recent Juniper Research employing cognitive assistants will bring down operations & service costs by Us$8 billion. ToBi, the AI chatbot of Vodafone, has shown a 68 per cent improvement in their customer satisfaction. MIKA, Nokia’s assistant, has a proven track record of producing up to 40 per cent improvement in first-time user experience.
Fraud Detection
There is no shortage of spammers, trolls, and known fraudsters in any telecom network. The telecom space is so vast, and a medium of easy access to the populace also happens to be the ideal petri-dish for cases of identity cloning, phishing scams, thefts, etc. Any occurrence of fraudulent incidents erodes the trust customers place on the telecom network.
Implementing unsupervised machine learning models backed by data science allows security specialists to detect any suspicious behavior. The efficacy of an ML model is that it can produce results in real-time. So, with a combination of data science, data visualisations, and ML models, the operators and the customer can both be alerted of a potential fraudulent caller.
Network Optimisations
Using AI algorithms and techniques, telecom companies can produce solutions to root cause problems. More importantly, AI can be applied to preemptively identifying and flagging anomalies. AI is vital for creating self-optimisation networks.
These networks leverage information gathered on network traffic, region, and timesone to produce optimised solutions that operators can implement for better performance. According to IDC, 63.5 per cent of telecom operators are actively investing in this technology. For example, NetFusion optimises 5G networks’ speed and network traffic to ensure seamless AR/VR functions.
Predictive Maintenance
The ability to prevent an incoming problem or meet a problem with an efficient preemptive solution is perhaps one of the most significant advantages of applying AI & ML techniques. AI & ML solutions are generated by leveraging the information acquired on hard historical data. This data has been generated from the consistent and constant analysis of many well-denied parameters like cell towers, data servers, mobile phone usage & devices, power lines, etc.
From monitoring network towers to customer set-up boxes, any problem/threat can be detected and neutralised before any damage occurs using the right AI tools & ML model. This particular technique is of immense aid to operators for maintenance purposes, as done by AT&T in real-time. The company successfully employed AI techniques during the test of a drone that monitors its LTE network towers.
Robotic Process Automation
Regardless of its scale, any given industry employs several processes, all directed towards delivering a final product. Now for a service provider industry like telecom, the number of operations involved is countless, and each is severely complex. Employing a virtual workforce is expensive and creates an operating model that is highly susceptible to human error. On top of this, there are several redundant tasks involved in telecom operations.
The use of automation processes supported by AI technologies can significantly aid in ensuring that these processes are carried out precisely & effectively. For example, Kryon Systems Ltd employs a full-cycle automation suite to automate critical processes in telecom operations.
In closing
Per a survey conducted by Deloitte, it became apparent that 40 per cent of Telecom, Media, and Technology executives have gotten unparalleled benefits from AI technology. More importantly, more than 75 per cent of these executives have agreed that almost all telecom companies are constantly transitioning to operations that complement AI & ML operations.
There is no doubt that AI & ML drive the technology today to ensure a better tomorrow. These technologies go beyond telecom performance enhancement and customer satisfaction. They dictate the next stage of evolution in the telecom industry. At present, it is evident that where the telecom industry is concerned, the implementation of AI & ML has stopped becoming an option and has started to become an essential tool for its sustainability in the era of digital transformation.
