How AI-enabled networks keep the digital transformation momentum going

As digital transformation continues to take place at a massive scale across Asia, connectivity will take centre stage in the next decade. We are seeing organizations of all types transforming to meet the requirements of 5G, IoT and other emerging technologies being adopted. For example, 5G is forecasted by GSMA to account for half of total mobile connections in Asia by 2025 – just five years away from today.

Many countries have laid out digital transformation plans as an essential investment for the future, and these plans will see further accelerated progress due to the impact of COVID-19. Indonesia’s government, for example, is speeding up its digital transformation plans to meet surging demands for faster connectivity. Singapore also recently announced that it will boost spending on the information and communications technology (ICT) sector to US$2.5 billion this year, citing investment in digitalization as instrumental in the government’s technological response to the pandemic.  

These national initiatives and the advent of digital technologies will require a robust network that delivers seamless, reliable connectivity in order to function. The consequences of an overwhelmed network will not only hinder day-to-day operations for both personal and business use alike, but also hamper the progress of national digital initiatives.

Today’s digital world is driving new challenges

Optimising network management for digital transformation is certainly easier said than done. When it comes to managing infrastructure, Communication Service Providers (CSPs) often struggle with a few ongoing challenges such as resourcing constraints and increasingly complex networks involving many vendors. 

If network infrastructures are not robust enough to handle sudden surges in bandwidth demand, poor customer experience, network outages and increased downtime could become a frequent occurrence. It is critical that these disruptions are minimised as connectivity underpins technologies such as cloud services and 5G; failure to accommodate these networking demands will hinder users from tapping into the full potential of these opportunities.

As networks undergo upgrades, they inevitably become more and more complex which adds on to the workload of network technicians over time. The maintenance process can be extremely arduous and inefficient, especially if technicians have to manually monitor and root out delicate and non-obvious dependencies among different parts of the network.

A network that can adapt

So we’ve established that network management is crucial for CSPs to meet user expectations of connectivity. But the crux of the matter is, CSPs need new methods to manage their networks efficiently. In today’s digital environment, millions of devices are connected and added to networks on a constant basis. Relying solely on manual processes is simply not feasible if CSPs were to ensure that each and every one of those connected devices deliver the same consistent quality user experience.

While CSPs provide a critical infrastructure resource, they need to be one step ahead, planning and optimising network capacity accurately to meet fluctuating service demands. It also means being aware of any impending network outage that might cause service disruptions so that the necessary preemptive actions can be taken.

Networks of our digital future need to be more predictive and intelligent, programmed to automatically make recommendations, implement policies, and respond dynamically to changing service demands. One of the keys to making this possible is advanced analytics that leverages artificial intelligence (AI).

AI is the brain of the future’s smart network

AI helps CSPs gain deep insight into the network and draw on this insight to control and manage the network efficiently and accurately and do so in a highly scalable manner. According to Omdia’s 2019 report Artificial Intelligence: Impact and Opportunities, over 40% of CSPs are prioritizing the implementation of AI in network fault prediction, detection and correction by 2020.

One use case would be helping CSPs avoid network outages by identifying and preemptively resolving anomalous network behavior that lead to them. AI-powered analytics enables simultaneous monitoring and analysis of hundreds of thousands of network ports and millions or even billions of events. It then draws intelligent insights to drive specific actions on the network to remedy the issues that lead to outages. By granting CSPs foresight and taking on a proactive approach rather than a reactive one, AI is fundamental to operating communications networks of the future. AI has a direct impact on the ability to meet customer SLAs and overall customer experience.

In another use case, AI helps in efficient real-time traffic management, making it easier for CSPs to dynamically adapt to changing service demands and traffic patterns despite the fixed hardware capacity at any given time. What this means is being able allocate available resources to where they are most needed. The end result is the ability to deliver consistent, quality customer service and end-user experience across the entire network while optimising the use of resources and associated costs. This is especially pertinent following the exploding network traffic consumption over the last few months due to the drastic rise in remote working, virtual streaming and video conferencing.

True AI is intended to predict, learn and react, and it will pave the way in automating network management and optimising service quality of experience. It has the potential to lead Asia into the digital future, enabling CSPs to deliver on the promises of an interconnected world. As we start moving towards this exciting reality, CSPs must adapt their network infrastructure and invest in AI to keep up with the demands of the time, or risk overwhelming their networks and failing to meet user expectations.