The telecom industry no longer is limited to just providing basic Internet and phone service. It is now at the very heart of technical growth, with its broadband and mobile services leading the way in this Internet of Things (IoT) time. It is fully expected that this growth will continue, and Technavio is predicting that by 2020 the worldwide IoT market will be posting a very healthy CAGR of over 42%. Artificial intelligence (AI) is driving this growth.
Artificial Intelligent applications have been revolutionising how telecoms optimise, operate and provide their customers with service.
Communications service providers (CSPs) today are faced with increased customer demands for improved customer experiences (CX) and higher quality services. Telecoms are addressing these opportunities by leveraging the massive amounts of data that they have been collecting for many years from their huge customer base. The data is drawn from billing data, service usage, detailed customer profiles, geolocations, mobile applications, networks, and devices.
The power of AI is being harnessed by telecoms to analyze and process these massive amounts of Big Data so that actionable insights can be extracted in order to improve operation, provide improved customer experiences, and increasing revenue through introducing new services and products.
Gartner has forecast that by 2020 there will be 20.4 billion connected devices in use, which has resulted in an increasing number of CSPs jumping on this bandwagon since they recognize how valuable artificial applications are within the telecommunications industry.
CSPs that are forward thinking have been focusing their efforts on four major areas where AI has already made significant inroads in terms of delivering tangible results for businesses: Robotic process automation (RPA), Virtual Assistants, preventive maintenance, and network optimization.
Network optimization
Artificial intelligence is essential in assisting CSPS with building self-optimizing networks (SONs). These give operators the ability to optimize network quality automatically based on time zone and regional traffic information. Advanced algorithms are used by AI applications within the telecommunications industry to search for patterns in the data, to allow telecoms to detect as well as predict network anomalies, and enable operators to fix problems proactively before customers are impacted in a negative way.
According to IDC, 63.5% of telecoms are making investments in AI systems in order to make improvements to their infrastructure. Some of the most popular telecom AI solutions include ZBrain Cloud Management by ZeroStack,which analyzes the storage and use of private cloud telemetry to improve general management, upgrades, and planning; NetFusion by Sedona Systems which optimizes speed delivery and routing of traffic of 5G-enabled services such as AR/VR; and Aria Networks, an artificial intelligence-based network optimization solutions which is being used by more and more Tier-1 telecom companies. Nokia launched a machine learning-based AVA platform of its own. It is a cloud-based network management solution for better managing capacity planning and for predicting service degradations in advance on cell site by as much as seven days.
Predictive maintenance
Predictive analytics that is AI-driven are helping telecoms to be able to provide improved services by using machine learning techniques, sophisticated algorithms, and data and use this historical data to predict future results. That means data-driven insights can be used by telecoms for monitoring the state of their equipment, predict the failure of equipment based on patterns, and fix communications hardware problems proactively, including data center servers, power lines, cell towers, and then the set-top boxes that are inside the homes of their customers.
Over the short-term, artificial intelligence and network automation will allow for improved prediction of issues and root cause analysis. Over the long term, the technologies will be underpinning an increased number of strategic goals, like providing new customer experiences and being able to deal with business demands more efficiently. AT&T has devised an innovative solution that uses IA for supporting its maintenance procedures: a drone is being tested by the company to expand coverage of its LTE network and to use analysis of video data that the drones captures for technical support and maintenance of its cell tower infrastructure.
Not only is preventive maintenance effective for the network, but also for the customer. KPN, a Dutch telecom, analyzes the notes made by the company’s call center agents and the insights generated are used for making changes to its interactive voice response (IVR) system. In addition, KPN analyzes and tracks customer behaviour from their homes – with the customer’s permission – like channels switching on their modem, which could indicate a Wi-Fi problem. Once a potential issue has been identified, KPN follows up proactively, to provide its technical teams with greater successes.
Virtual Assistants
Virtual assistants – which are a type of conversational AI platform – have earned how to scale and automate one-on-one conversations to such an efficient degree that it is projected that over the next five years they will be cutting business expenses by up to $8 billion. Telecoms are turning to virtual assistant to help manage the huge amounts of support requests for set up, installation, maintenance and troubleshooting, which customer support centers are so often overwhelmed with. Telecoms can use AI to implement self-service capabilities to explain to customers how to operate and install their own devices.
A new chatbot-TOBi was introduced by Vodafone for handling a set of customer service-type questions. This chatbot scales responses to basic customers questions, to deliver the speed that is demanded by customers. MIKA is Nokia’s virtual assistant who suggests solutions for network problems, which has resulted in i a 20-40% improvement on first-time resolutions.
There are voice assistants, like Telefónica’s Aura, that has been designed to cut customer service costs that phone inquiries generate. Comcast has introduced their voice remote which lets customers interact using natural speech with their Comcast system. DISH Network has partnered with Amazon’s Alex to allow its customers to use spoken word to buy or search for media content instead of via remote control. Visual support integrated into IVR is further delivering efficient use of time-reducing customer hold times and average handling times (AHT), and driving an improved CX, ultimately.
Robotic process automation (RPA)
All CSPs have large customer bases and a nearly endless volume of transactions on a daily basis, which are all susceptible to human errors being made. Robotic Process Automation (RPA) is a type of business process automation technology that is based on artificial intelligence. RPA can provide telecommunications functions with greater efficiency by enabling telecoms to manage their back-office operations more easily along with large volumes of rules-based and repetitive processes. By streamlining the execution of formerly time-consuming, labour-intensive, and complex processes like order fulfilment, workforce management, data entry, and billing, RPA frees up CPS staff so that they can perform higher value-added work instead.
Deloitte conducted a survey which showed that 40% of Telecom, Tech and Media executives say that cognitive technologies has provided them with “substantial” benefits, while 25% have invested $10 million or more. Over three-quarters of those surveyed expect that cognitive computing will be “substantially transforming” their companies over the next three years.
Telecoms use Celato to help streamline inbound data, like posts, web forms, and email, extract key information for correspondence, validate the data and then present a service rep with a suggested response, who can then amend the message before they respond to the customer. Telecoms use Kryon to help identify key processes for automating support of both human and digital workforces to optimize process efficiency.
Summary
AI applications within the telecommunications industry are increasingly helping CSPs with managing, optimizing and maintaining their customer support operations along with their infrastructure. RPA, virtual assistants, predictive maintenance, and network optimization are examples of situations where the telecom industry has been impacted by AI, to deliver enhanced CS and add value to the overall enterprise. Technology is a major component of the telecommunications industry already, and as Big Data applications and tools become more sophisticated and available, it can be expected that AI with continue to grow rapidly within this space.
