4 AI Trends that will Transform the Telecom Industry in 2019

artificial intelligence applications in the telecommunications industry

“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 its 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 for this growth? Artificial intelligence (AI).


Artificial Intelligent applications are revolutionizing the way telecoms operate, optimize and provide service to their customers

 

Today’s communications service providers (CSPs) face increasing customer demands for higher quality services and better customer experiences (CX).  Telecoms are addressing these opportunities by leveraging the vast amounts of data collected over the years from their massive customer base. This data is culled from devices, networks, mobile applications, geolocations, detailed customer profiles, services usage and billing data.

Telecoms are harnessing the power of AI to process and analyze these huge volumes of Big Data in order to extract actionable insights to provide better customer experiences, 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 jumping on the bandwagon, recognizing the value of artificial intelligence applications in the telecommunications industry.

Forward-thinking CSPs have focused their efforts on four main areas where AI has already made significant inroads in delivering tangible business results: Network optimization, preventive maintenance, Virtual Assistants, and robotic process automation (RPA).  

 

Network optimization

 

AI is essential for helping CSPs build self-optimizing networks (SONs), where operators have 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 telecoms to both detect and predict network anomalies, and allowing operators to proactively fix problems before customers are negatively impacted. 

IDC indicates that 63.5% of telecoms are investing in AI systems to improve their infrastructure. Some popular AI solutions for telecoms are 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, and 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.

 

Predictive maintenance

 

AI-driven predictive analytics are helping telecoms provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. This means telecoms can use data-driven insights to can monitor the state of equipment, predict 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 business demands. 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 infrastructure maintenance of its cell towers.

Preventive maintenance is not only effective on the network side, but on the customer’s side as well. Dutch telecom KPN analyzes the notes generated by its call center agents, and uses the insights generated to make changes to the interactive voice response (IVR) system.  KPN also tracks and analyzes customer behavior from home – with their permission – such as switching channels on their modem, which may signify a Wi-Fi issue. Once identified, KPN proactively follows up, driving greater successes for technical teams.

 

Virtual Assistants  

 

Conversational AI platforms – known as virtual assistants – 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 in the next five years. Telecoms 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 support centers. Using AI, telecoms can implement self-service capabilities that instruct customers how to install and operate their own devices.

Vodafone introduced its new chatbot — TOBi to handle a range of customer service-type questions. The chatbot scales responses to simple customer queries, thereby delivering the speed that customers demand. Nokia’s virtual assistant MIKA suggests solutions for network issues, leading to a 20% to 40% improvement in first-time resolution.

Voice assistants, such as Telefónica’s Aura, are designed to reduce customer service costs generated by phone inquiries. 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 further delivers an efficient usage of time – reducing average handling times (AHT) and customer hold times, and ultimately driving a better CX.

 

Robotic process automation (RPA)

 

CSPs all have vast numbers of customers and an endless volume 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 telecoms to more easily manage their back office operations and the large volumes of repetitive and rules-based processes. By streamlining execution of once complex, labor-intensive and time-consuming processes such as billing, data entry, workforce management and order fulfillment, RPA frees 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, extracts key data for the correspondence, validates it and presents a suggested response to a service rep, who then amends the message before responding to the customer. Kryon assists telecoms with identifying key processes to automate in support of both digital and human workforces for optimal process efficiency.

 

Summary

 

Artificial intelligence applications in the telecommunications industry is increasingly helping CSPs manage, optimize and maintain not only their infrastructure, but their customer support operations as well. Network optimization, predictive maintenance, virtual assistants and RPA are examples of use cases where AI has impacted the telecom industry, delivering an enhanced CX and added value for the enterprise overall.  Technology is already a big part of the telecommunications industry, and as Big Data tools and applications become more available and sophisticated, AI can be expected to continue to grow in this space.