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Implementation of Artificial Intelligence in telecom organizations can assist in managing operations efficiently and improve CX. TechSee looks into 4 regions where AI is successfully used. 

The leveraging of AI in telecom is more and more popular, and it’s simple to see why. In this article, we’ll talk about the four primary applications of AI in the telecom market. 

Telecoms and AI 

“Alexa, launch Netflix!” 

No more restricted to furnishing basic phone and online service, the telecom domain is at the epicentre of technological growth, spearheaded by mobile and broadband services in the Internet-of-things (IoT) age. This expansion is expected to continue, with Fortune Business Insights forecasting that the international telecom IoT market will post an impressive CAGR of 25.4% in the 2021-28 period. 

The key driver of this expansion? 

Artificial Intelligence (AI) 

The Additional Value of AI Adoption in Telecom Organizations 

Present day’s communication service providers (CSPs) encounter increasing demands for increased quality services and improved customer experience (CX). Telecoms are capitalizing on these avenue by harnessing the massive amounts of data gathered over the years from their massive client bases. This information is culled from an array of channels, like: 

  • Devices 
  • Networks 
  • Mobile application
  • Geolocation 
  • Detailed Customer Profiles 
  • Service Usage, and 
  • Billing Data 

Telecoms are additionally leveraging the potency of Artificial Intelligence to process and undertake analysis of these massive volumes of big data. Harnessing Artificial Intelligence in Telecom enterprises, facilitates them to extract actionable insights and furnish improved client experience, enhance operations, and enhance revenue via new products and services. 

With Statista forecasting that 30.9 billion connected devices will leveraged globally by 2025, more and more CSPs are getting with the program, identifying the advantages of AI in the telecom industry. 

Four Ways to leverage AI in the Telecom Sector 

Forward-thinking CSPs have concentrated their AI investments on four primary areas: 

  • Network optimization
  • Preventive maintenance
  • Virtual assistants
  • Robotic process automation (RPA) 

In these regions, AI has already started to provide tangible business outcomes. 

AI for Network Optimization 

AI is critical for assisting CSPs develop self-optimizing networks (SONs) These provide operators the capacity to automatically optimize network quality on the basis of traffic data by region and time zone. AI within the telecom industry leverages sophisticated algorithms to seek patterns within the data, facilitating telecoms to both identify and forecast network anomalies. As an outcome of leveraging AI in telecom, CSPs can proactively rectify problems prior to clients being negatively affected. 

Telecom AI Use Cases 

The number of operators making investments in AI systems to enhance their infrastructure is predicted to expand 70% in 2025. Some widespread telecom AI use cases consist of:  

  • ZeroStack’s ZBrain Cloud Management, which undertakes analysis of private cloud telemetry storage and leveraging for enhanced capacity planning, upgrades, and general management. 
  • Aria Networks, an AI-based network optimization solution that counts an increasing number of Tier 1 telecom organizations as clients. 
  • Sedona Systems’ NetFusion, which undertakes optimization of the routing of traffic and speed delivery of 5G-enabled services such as AR/VR
  • Nokia initiated its own machine-learning-based AVA platform, a cloud-based network management solution to better handle capacity planning. It additionally forecasts service degradations on cell sites up to seven days prior. 

AI for Predictive Maintenance 

AI-based predictive analytics are assisting telecoms furnish improved services by harnessing data, advanced algorithms and machine learning strategies to forecast future outcomes on the basis of historical data. This implies operators can leverage data-driven insights to monitor the state of equipment and anticipate failure on the basis of patterns. Implementation of AI within telecoms also facilitates CSPs to proactively rectify problems with communications hardware, like: 

  • Cell Towers 
  • Power Lines
  • Data Centre Servers, and even
  • Set-top boxes in client’s homes 

During the short term, network automation and intelligence will facilitate improved root cause analysis and forecasting of issues. In the longer-term, these technologies will underpin more strategic objectives, like developing new client experiences and managing emergent business requirements efficiently. 

An innovative telecom AI use case is AT&T, who are harnessing AI to support their maintenance protocol. The organization has successfully employed a drone to grow its LTE network coverage during natural disasters. Extra advantages of artificial intelligence with telecom consist of the capacity to undertake analysis of video data gathered by drones for tech support and maintenance of its cell towers. 

Utilizing preventive maintenance to assist clients: a telecom AI use case 

Preventive maintenance is efficient not just on the network side, but additionally on the client’s side. Dutch Telecom KPN undertook analysis of the notes produced by its contact centre agents, and leverages the insights produced through implementation of AI in telecom to make alterations to its interactive voice response (IVR) system. 

KPN also tracks and undertakes analysis of client’s at-home behaviour, with their authorization, like switching channels on their modem, which may demonstrate a Wi-Fi issue. Once detected, KPN proactively follows up on these issues, driving bigger successes for technical teams. 

Virtual Assistants for Customer Support 

Another application of artificial intelligence within telecom is conversational AI platforms. Also referred to as virtual assistants, they have become aware how to automate and scale one-on-one conversations so effectively that they are predicted to reduce business costs by $8 billion annually in 2022, going by research put out by Juniper Research. 

AI adoption within telecom assists to content with the massive number of support requests for installation, setup, troubleshoot, and upkeep, which usually overwhelms client service centres. Leveraging AI, operators can implement self-service capabilities that demonstrate to clients how to install and operate their own devices. 

Successful Telecom AI use cases 

Vodafone undertook implementation of TechSee’s AI in Telecom and observed a 68% enhancement in client satisfaction. They put forth their new chatbot TOBi to manage an array of client service questions. The chatbot scales responses to simple client queries, delivering the speed that subscribers demand. 

Nokia’s virtual assistant MIKA indicates solutions to network issues, causing a 20% to 40% enhancement to its first-time resolution rate. 

Voice assistants, like Telefonica’s Aura, are developed to minimize client service costs produced by phone enquiries. Comcast has also put into the market a voice remote that facilitates clients to interact with the Comcast system via natural speech. 

Further instances of Artificial Intelligence within the telecom market consists of DISH Network’s partnership with Amazon’s Alexa. This facilitates clients to look or buy media content through spoken word instead of remote control. Integration of visual support within IVR facilitates more time-effective interactions, minimizing average handling times (AHT) and client hold times, and eventually driving improved CX.  

The times when the leveraging of artificial intelligence within network operations was in essence, cold-hard proof of thrift, are in the past. We are at the cusp of a brand-new era, where the intensification of activities within the provisioning and upkeep of telecommunications services will force the hand of enterprises to harness more AI-based advanced tools to assist operations and satisfy client requirements.

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