The use of AI in telecom is increasingly popular – and it’s easy to see why. In this article, we’ll discuss the four main applications of AI in telecommunications.
AI and Telecoms
“Alexa, launch Netflix!”
No longer limited to providing basic phone and internet service, the telecom industry is at the epicenter of technological growth, led by mobile and broadband services in the Internet of Things (IoT) era. This growth is expected to continue, with Technavio predicting that the global telecom IoT market will post an impressive CAGR of more than 42% by 2020. The driver of this growth? Artificial intelligence (AI).
The Added Value that AI Gives Telecom Companies
Today’s communications service providers (CSPs) face increasing demands for higher quality services and better customer experience (CX). Telcos are capitalizing on these opportunities by leveraging the vast amounts of data collected over the years from their massive customer bases. This data is culled from devices, networks, mobile applications, geolocation, detailed customer profiles, service usage and billing data.
Telcos are also harnessing the power of AI to process and analyze these huge volumes of Big Data in order to extract actionable insights and provide better customer experience, improve operations, and increase revenue through new products and services.
With Gartner forecasting that 20.4 billion connected devices will be in use worldwide by 2020, more and more CSPs are getting with the program, recognizing the value of artificial intelligence in the telecommunications industry.
Four AI Use Cases in the Telecommunications Industry
Forward-thinking CSPs have focused their AI investments on four main areas:
Network optimization
Preventive maintenance
Virtual Assistants
Robotic process automation (RPA)
In these areas, AI has already begun to deliver tangible business results.
AI for Network Optimization
AI is essential for helping CSPs build self-optimizing networks (SONs), which give operators the ability to automatically optimize network quality based on traffic information by region and time zone. Artificial Intelligence applications in the telecommunications industry use advanced algorithms to look for patterns within the data, enabling telcos to both detect and predict network anomalies, and allowing them to proactively fix problems before customers are negatively impacted.
IDC indicates that 63.5% of operators are investing in AI systems to improve their infrastructure. Some popular AI use cases in telecom include:
ZeroStack’s ZBrain Cloud Management, which analyzes private cloud telemetry storage and use for improved capacity planning, upgrades and general management
Aria Networks, an AI-based network optimization solution that counts a growing number of Tier 1 telecom companies as customers
Sedona Systems’ NetFusion, which optimizes the routing of traffic and speed delivery of 5G-enabled services like AR/VR
Nokia launched its own machine learning-based AVA platform, a cloud-based network management solution to better manage capacity planning, and to predict service degradations on cell sites up to seven days in advance.
AI for Predictive Maintenance
AI-driven predictive analytics are helping telcos provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. This means operators can use data-driven insights to can monitor the state of equipment, anticipate failure based on patterns, and proactively fix problems with communications hardware, such as cell towers, power lines, data center servers, and even set-top boxes in customers’ homes.
In the short term, network automation and intelligence will enable better root cause analysis and prediction of issues. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with emerging business needs. An innovative solution by AT&T is using AI to support its maintenance procedures: the company is testing a drone to expand its LTE network coverage and to utilize the analysis of video data captured by drones for tech support and maintenance of its cell towers.
Using Preventive maintenance to help customers
Preventive maintenance is effective not only on the network side, but also on the customer’s side. Dutch telco KPN analyzes the notes produced by its contact center agents, and uses the insights generated to make changes to its interactive voice response (IVR) system. KPN also tracks and analyzes customers’ at-home behavior – with their permission – such as switching channels on their modem, which may signify a Wi-Fi issue. Once identified, KPN proactively follows up on these problems, driving greater successes for technical teams.
Virtual Assistants for Customer Support
Another application of AI in telecommunications is conversational AI platforms. Also known as virtual assistants, they have learned to automate and scale one-on-one conversations so efficiently that they are projected to cut business expenses by as much as $8 billion annually in 2022, according to Juniper Research. Telcos have turned to virtual assistants to help contend with the massive number of support requests for installation, set up, troubleshooting and maintenance, which often overwhelm customer service centers. Using AI, operators can implement self-service capabilities that show customers how to install and operate their own devices.
Vodafone – which has implemented TechSee’s technology and saw a 68% improvement in customer satisfaction – introduced its new chatbot TOBi to handle a range of customer service questions. The chatbot scales responses to simple customer queries, delivering the speed that subscribers demand. Nokia’s virtual assistant MIKA suggests solutions to network issues, leading to a 20% to 40% improvement to its first-time resolution rate.
Voice assistants, such as Telefónica’s Aura, are designed to reduce customer service costs generated by phone enquiries. Comcast has also introduced a voice remote that allows customers to interact with their Comcast system through natural speech. Similarly, DISH Network’s partnership with Amazon’s Alexa allows customers to search or buy media content by spoken word rather than remote control. Integrating visual support within IVR enables more time-efficient interactions – reducing average handling times (AHT) and customer hold times, and ultimately driving better CX.
Want to take it further? Check out these five strategies for improving CX for telecoms customers.
Robotic process automation (RPA) for Telecoms
CSPs have vast numbers of customers engaged in millions of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI. RPA can bring greater efficiency to telecommunications functions by allowing telcos to more easily manage their back office operations and large volumes of repetitive and rules-based actions. By streamlining the execution of complex, labor-intensive and time-consuming processes such as billing, data entry, workforce management and order fulfillment, RPA frees up CSP staff for higher value-add work.
According to a survey by Deloitte, 40% of Telecom, Media and Tech executives say they have garnered “substantial” benefits from cognitive technologies, with 25% having invested $10 million or more. More than three-quarters expect cognitive computing to “substantially transform” their companies within the next three years.
Celaton helps telecoms streamline inbound data, such as emails, web forms and posts, extracting and validating key data from each correspondence, and presenting suggested responses to service reps, who then amend messages before responding to customers. Kryon, meanwhile, assists operators with identifying key processes to automate in support of both digital and human workforces for optimal efficiency.
The Future of AI in the Telecom Industry
Artificial Intelligence applications in the telecommunications industry are increasingly helping CSPs manage, optimize and maintain not only infrastructure, but also customer support operations. Network optimization, predictive maintenance, virtual assistants and RPA are all examples of use cases where AI has impacted the telecom industry, delivering enhanced CX and added value for enterprises.
As Big Data tools and applications become more available and sophisticated, AI can be expected to continue to accelerate growth in this highly competitive space.
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