The growing impact of AI in telecommunications

growing impact of AI in telecom

AI is undoubtedly one of our generation’s most transformational technology advancements. Rising from the fantasies of science fiction, it has become an integral part of our lives and will play a more potent role in the years to come. In the telecom industry, AI is enabling smarter networks and infrastructure essential for meeting the exploding demand for speedier and more efficient data connectivity. Investments by operators across the globe point at AI’s growing impact in telecommunications. Here are some examples of how this industry is adopting AI.

AI in network optimization

Traditional networks, characterized by technological and industrial silos, are prone to security breaches, downtimes and are not suitable for applications that require multi-mode capabilities. AI is helping operators to overcome the limitations of siloed network planning, transforming traditional networks into more intelligent ones.

Based on the intelligence built upon humongous historical network data, telecom operators can detect patterns in network anomalies and take pre-emptive steps to prevent network failures and traffic congestion. Used in self-organizing networks (SON), AI allows mobile networks to continuously adapt – including adding needed capacity, making network configuration changes, and self-heal. Furthermore, predictive mechanisms are enabling telecom companies to take faster and more data-driven decisions.

The integration of SON in existing operational support systems help operators to significantly reduce their OPEX, shorten the time-to-market, increases automation, and deliver a better experience for customers.

Building 5G networks

5G deployment and maintenance is complex and challenging. There are varied infrastructural and computational requirements for different implementations across verticals. Leading operators such as AT&T, Orange, and Telefonica are using AI for better 5G planning and improving network capacity based on insights gathered from multi-mode coverage, cell distribution, and traffic-related data. Finally, AI is also allowing more complex nonlinear modeling of the network behavior, in case of design changes or subscriber and data growth.

AI will also equip CSPs with techniques for optimized 5G implementation and development of intelligent next-generation equipment to­­­ support the millimeter-wavelength radio frequency.

AI in cloud networking

The telecommunication industry is witnessing a shift towards cloud-native architectures in order to build digital telecom networks and eliminate traditional hardware dependencies. Using AI minimizes manual network management efforts, thus improving the cloud infrastructure’s operational and maintenance efficiency.

Cloud automation: Automation in the cloud environment will facilitate advanced autonomous networks, ensuring greater network agility and ubiquitous access to superior data services.

Network virtualization: Telecom vendors are working on cloud-native applications to virtualize legacy hardware architectures. ML techniques can be used for continuous delivery in the cloud-native architecture, leading to next-level innovation. Cloud-native also provides a multi-vendor environment and makes the network highly scalable and adaptable.

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AI in network security

Operators are applying AI-based solutions to identify network security risks before they can do damage. ML-based security techniques will help build networks that can identify patterns and forecast potential threat on network servers, cloud, and end-devices.

Additionally, hardware security is also going to be crucial due to heightened cyber-security risk in connected devices. AI-based cyber-security techniques offer encryption and key protection technologies for secure storage and sharing of encrypted data in hybrid environments. It ensures fully-managed, protected data transmission across the network and automates the detection of security threats.

Other AI implementations in telecom

Intelligent bots in conversational AI applications are helping telecom companies lower customer retention and round-the-clock customer service costs. Additionally, cross-industry AI connectivity will be critical for implementing applications like connected cars, remote surgeries, industrial automation, and robotics.

Digital twin technology incorporates AI to build virtual replicas of a physical entity. Telecom companies can use digital twins for better cell tower management, cell site planning, and designing network architecture. The combined capabilities of predictive analytics, geolocation tools, and other digital components can provide visibility into the inefficiencies in the overall network.   

AI investment by telecom-focused companies has been on the rise since 2014. According to Netscribes’ M&A analysis, transactions related to cloud-native, SON, and security have been gradually increasing over the years. Front-runners include AT&T, Verizon, Rakuten, Huawei, among others.

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