The telecommunications industry has witnessed significant changes in the last few years—all instigated by numerous technological advancements from near-limitless connectivity to automation. And as more consumers continue to demand enhanced user experiences, communication service providers (CSPs) are getting serious about the tools that will help them adapt to the new 5G era: complex technological challenges, rising customer expectations and escalating costs. This will be no easy feat given the fact that CSP networks are famous (even infamous) for their complexity. The volume of data they generate and require to function is increasing, while the services being delivered are becoming ever more diverse.
With all of the pressures that CSPs are now under to deliver more with less, there is a clear and logical case to be made for distilling OPEX savings through a judicious application of Artificial Intelligence (AI) and Machine Learning (ML). The starting point could be the use of AI to automate network operations and reduce deployment and operational complexity. By leveraging AI/ML to simplify and automate tasks, service providers can lower OPEX and increase service velocity. In the digital age especially, delivering and maintaining service quality in highly heterogeneous and dynamic networks will eclipse the innovative and creative capacity of humans, and hence mandates the introduction of AI/ML driven automation.
It appears that the C-suite of many telecommunication companies are now also recognising the opportunity of AI/ML. In a recent Gartner survey, 95% of telecom CIOs said they plan to implement AI/ML technologies by 2026, and 79% have said they plan to increase their AI/ML investments. These CSPs are struggling to drive expense out of their networks as they have yet to see meaningful returns on their substantial 5G RAN and spectrum investments. And the problem is particularly acute as mobile ARPU remains flat across many countries worldwide, and some reports have even shown significant ARPU declines in places like the UK and Germany.
Application lifecycle management, but smarter
Application lifecycle management is a good starting point when it comes to reducing OPEX through AI. The sum of a CSP’s network—hardware-based equipment, virtualized software elements, newer cloud-native network elements—mean that they are never homogenous. Testing and deploying new applications or software releases remains a manual process that is resource-intensive, time-consuming and disruptive. Oftentimes, a significant software upgrade becomes a protracted process that can take months, or even years, to complete.
Fortunately, AI excels at processing and sifting through large volumes of data. AI-based tools can interrogate the network, detect edge cases, and automatically create and run test suites tailored to a CSP’s unique environment. Without AI-based solutions, test teams – even the most experienced ones are limited in terms of their ability to analyse and test a large network. This then impedes their work to identify bugs and performance issues, evaluate large network changes, and measure network performance. On the other hand, AI-powered testing expands test coverage and exposes flaws more quickly. It also makes more efficient use of testing resources and helps to re-assign testers to more pressing, higher order tasks. Identifying and resolving issues earlier in the application lifecycle makes it possible to expediate deployments and upgrades. It also increases the probability that new software will enjoy a 100 percent success rate from the point at which it is deployed.
AI/ML-powered toolsets can continue working once the upgrade or deployment is complete. As the network evolves the toolset can adapt test cases, automatically covering new call flows, network elements, and connections.
Troubleshooting
During moments of crisis, CSPs over-rely on their most experienced and knowledgeable SMEs to identify and isolate service quality issues, performance bottlenecks, and security breaches. Even the most seasoned support engineer requires hours, or even days, to contend with multiple alarms and thousands of datapoints before they are able to identify the root-cause that spans numerous systems and network technologies. But when knowledge and experience (usually accumulated by years of service and resulting from the repetition of well-known issues) is confronted by an entirely new problem that presents with similar symptoms, SMEs often struggle and get sent off course. For the CSP, such a scenario can result in serious delays in issue resolution, increased downtime, and suboptimal network performance – which result in decreased customer satisfaction and SLAs.
AI is no match for human ingenuity and creativity, yet it can still assist and augment the skills of technical teams tasked with managing complex network environments. That’s because AI-powered troubleshooting tools can analyse vast amounts of end-to-end data, all in real-time, to identify the most likely cause of an issue. Not only can they save a considerable number of person-hours, but network engineers can also avoid investigating false or irrelevant leads.
At the same time, AI opens up new opportunities for less-skilled staff to take on complex issues. Intelligent troubleshooting assistants can interpret spoken or written language, so engineers are no longer required to know 20 different command line interfaces (CLIs). They can also translate cryptic system alarms into plain language that less-experienced staff can understand. Intelligent troubleshooting assistants boost the productivity and confidence of staff, regardless of experience.
Predictive analytics
Given that AI is capable of understanding complex data patterns, predicting future scenarios based on this awareness and then making informed decisions, it has a natural role to play when it comes to guaranteeing continuity of service. It is also particularly relevant when it comes to expansive and intricate telecom networks where anomaly detection is a huge undertaking. Using ML, CSP’s can identify potential issues and facilitate their proactive resolution, contributing significantly to reducing downtime and improving service quality.
At the same time, AI algorithms can see the difference between “normal” activity and the one associated with service-impacting incidents. The result is that by automating the detection of and response to service impacting incidents, AI can reduce the impact of service disruptions. Intelligent analysis and predictive analytics can save substantial amounts of time and money, all while minimizing customer experience problems.
The true value of AI/ML for service providers
The integration of AI/ML into a CSP’s strategic roadmap represents a transformative opportunity for the telecommunications industry as a whole. By leveraging advanced algorithms and ML models, it is possible to significantly enhance network reliability, reduce operational costs, and improve customer satisfaction. In taking these valuable outcomes to their logical conclusion, CSPs have the chance be competitive once again, and in turn, significantly contribute to the future success of an ever evolving and advancing telecoms industry.
