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: the IoT telecom services market was estimated to grow from $2.90 billion in 2016 to $17.67 billion in 2021, at a CAGR of 43.6%. The driver for this growth? Artificial intelligence (AI).
Emerging trends in telecom sector
AI 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). Companies 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.
Let’s take a look at some of the industry trends that are being driven by the expansion into AI: Network optimization, preventive maintenance, Virtual Assistants, and robotic process automation (RPA).
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. AI applications trending in the telecommunications industry use advanced algorithms to look for patterns within the data, enabling them to both detect and predict network anomalies, and allowing operators to proactively fix problems before customers are negatively impacted.
According to Gartner, the number of CSPs investing in artificial intelligence (AI) technologies for improving their infrastructure planning, operation, and products will rise from 30% in 2020 to 70% in 2025. Some popular AI solutions 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 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 help CSPs automate network operations and deliver service assurance.
AI-driven predictive analytics are one of the latest trends 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 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.
AT&T is using machine learning to enhance their end-to-end incident management process by detecting network issues in real-time. The technology can address 15 million alarms per day, restoring service before the customers notice an outage. The company is also relying on AI to support its maintenance procedures: the telecom giant is using drones 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. Dutch telecom KPN – in partnership with Accenture – is using ultra-high-definition cameras that leverage 5G to scan and analyze wide areas of connected piping in real-time, identifying high-risk corrosion areas and determining the best corrective actions.
Conversational AI platforms – known as virtual assistants – have learned to automate and scale one-on-one conversations so efficiently that the market is expected to grow to $13.9 billion by 2025, a CAGR of 21.9% from 2020-2025. Virtual assistants are an emerging trend in this sector, tapped 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’s website-located AI assistant, Julia, can assist customers with a range of tasks from technical support to invoicing queries, and then feeds critical, insightful data back to Vodafone to aid in future decision-making. Another Vodafone chatbot — TOBi – has already launched in 11 markets and handles a range of customer service-type questions. The chatbot scales responses to simple customer queries, thereby delivering the speed that customers demand. Deutsche Telekom’s chatbot, Tinka, acts as a search engine by providing targeted assistance to customers in Austria (see her at the bottom right of screen). The 20% of queries Tinka is unable to handle gets passed to a human agent for follow up. Deutsche Telekom also relies on recruitment chatbot Hub:rom, a conversational AI assistant that fields questions about job offers and other personnel-related issues.
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 that is one of the latest technology trends. RPA can bring greater efficiency to telecommunications functions by allowing easier management of 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.
CSPs in 2021 are recognizing that applying RPA to alleviate even some of the staff’s workload will have a major impact on streamlining processes and improving profitability. AT Kearney reports that RPA costs 1/3 as much as an offshore employee and 1/5 the cost of on-site staff, and can cut costs by 25-50%. With these numbers, it’s no surprise that Forrester data shows that over 44% of customer service organizations are already using RPA to help them gain a competitive advantage.
Celaton helps companies 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. AutomationEdge helps network providers automate data entry, invoice processing, and responses to queries.
Top AI Trends in the Telecom Industry
Artificial intelligence applications are among the latest trends in the telecom industry, 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 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 into 2021 and beyond.
This article was first published on the TechSee blog.