How Computer Vision-Powered Applications Can Drive The Telecom Industry

The global IoT telecom sector has grown to an astonishing figure of $17.67 billion in 2021 with a CAGR of 43.6% and the upward trend will continue for years to come. This is also because, unlike previously, when the telecom sector was limited to those who provided phone and internet services, the telecom industry has expanded across various sectors today, including broadband, mobile and the Internet of Things (IoT). As the customer base is rising exponentially and not just in the mobile or internet sectors, telecom service providers are capitalizing on the opportunity by using AI and the humongous trove of data that they have gathered for years. Computer vision for telecom can provide a better customer experience, improve operations, generate revenue, offer more products and services catered to what customers need, and draw actionable insights.

What is Computer Vision?

Just as how AI aids computers to think, Computer vision, as the name suggests, helps computers recognize objects and others via visual inputs. It uses AI, deep learning, algorithms like Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), and others to recognize objects in an image and video respectively.

Computer vision works similar to human vision, although the distinguishing factor here is that the latter has hundreds of thousands of years of context, while computer vision is still in its early stages and requires a huge trove of data to train over and over again in order to recognize objects, people, things, or others, including how far they are, if they are moving or not, and find anomalies, such as on an assembly line. But technically, there are hundreds of use cases of computer vision for telecommunication.

How Does Computer Vision Work?

Computer vision uses a set of algorithms to work on the huge trove of data fed to it. The AI uses a model and compares it with the prediction until it recognizes the object. It uses deep learning and CNN for images, whereas RNN is used for moving images, i.e., videos, which are essentially a set of still images.

As an image is basically a set of integer values for a computer, computer vision helps it understand the context and content using various algorithms. A CNN breaks images into pixels, tags them and performs convolutions on the sharp edges first to recognize them. As usual, if the prediction fails, the model runs a series of iterations over and over again to recognize what it is ‘seeing.’ Similarly, an RNN is used on videos to help discern what it is actually ‘seeing’ rather than programmers tagging each item, object or people.

What Are the Applications of Computer Vision for Telecom?

Computer vision for telecommunications has become an integral part as the number of customers is increasing exponentially and so is the complexity in the networks used. Here are some of the applications of computer vision for telecommunications that explain just how crucial the budding AI is with respect to its real-world use cases.

Visual Automation

Visual automation is a broad application of computer vision for the telecom industry. It encapsulates everything from attracting customers to establishing connections and providing seamless connectivity round the clock. With computer vision AI in place, telecom service providers can ensure enhanced First Time Resolution (FTR), which is basically getting the connections right at the first time rather than indulging in costly reworks. Apart from that, computer vision can help telcos audit contractors and subcontractors while they lay fiber optic cables or other equipment needed for connectivity.

It also ensures visibility of network status as well as optimum customer satisfaction at every stage of the transaction to keep the customers on board and subscribed to the telcos. Perhaps, visual automation is not limited to these functions and spread across all applications of the telecommunication industry, which is getting bigger each day.

Customer Support and Complaints Redressal

Customer support is an integral part of telcos around the globe. A customer might end up canceling the subscription with a single or multiple bad customer experiences. A computer vision-powered application in this use case can be used for customer support and complaint redressal. One of the primary reasons for bad customer support is probably the influx of users compared to the customer representatives available for support.

AI and computer vision can address complaints from customers and even direct them to resolve the issues using self-service methods, such as restarting the modem or device in question. In fact, an AR overlay can be projected at a customer’s home for visual guidance so that they can troubleshoot the problem saving almost $1.2 billion in technician visits and customer care a year by 2022.

Another application towards complaint redressal is automated chatbots with smart eyes that monitor and diagnose issues and provide necessary fixes. Vodafone’s TechSee AI aids the telecom giant to improve its customer satisfaction by 68%, while Nokia’s MIKA virtual assistant was able to resolve network issues and effectively troubleshoot 20-40% of the issues as per its FTR rate. This is crucial in today’s time when there’s a pandemic spread around the globe, which makes contactless computer vision-powered visual guidance a much-liked pivot for telcos.

AI-Based Network Optimization

A slight surge in internet users of a particular telecom provider may not break it; however, if the change happens suddenly, and that too above the forecast limits that were made, this can cause internet issues to all the users on the network. AI-based network optimization, where computer vision takes the device and network data into account, optimizes networks based on the traffic, user base, devices onboard, and other verticals, spread across a specific time zone and region.

Computer vision can aid telcos plan their moves well in advance as the algorithms integrated into the AI can recognize patterns and find anomalies before it acts as a bottleneck for customers. The use of computer vision across operators is increasing with the likes of ZBrain Cloud Management from ZeroSlack, which analyzes cloud telemetry storage and helps improve capacity, general management, and upgrades. Other telcos that are working towards network optimizations are Nokia, Aria Networks and NetFusion, among others.

Preventive Maintenance

Computer vision is widely used for leaks, falls, damage detection and preventive maintenance. The telecom industry uses various devices and instruments, including cell towers, power lines, data centers, set-top boxes, IoT devices and other hardware that enables it to function properly. Telcos have access to a trove of historical data about their customers, traffic, network, equipment and instruments in use, and more.

The telecom industry uses AI-powered predictive analyses that use the large data set available, analyzes the pattern between all the data, and realizes results that would help in anticipating failure or damage of any equipment in use. This allows service providers to take preventative measures to fix the issue before it balloons and bursts. One such example is with the major US telecom carrier AT&T, which uses computer vision AI-driven drones to find any hardware issues and anomalies and arrange for maintenance.

Future of AI and Computer Vision For Telecom Industry

The telecom market is increasing exponentially. There are more phones in the world than people and that’s just a minute section of users. There are broadband services, dish services, IoT services and more, where computer vision is increasingly being used to improve customer satisfaction, complaint redressal, network optimization and maintenance of infrastructure, to name a few.

Computer vision for telecom is getting sophisticated with each iteration, which hints at a bright future with inventory management, retail industry, insurance, and other sectors, where it is seeing accelerated growth.

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