INTERVIEW: SOFTWARE & SERVICES By leveraging data insights and applying


analytics through AI platforms, network providers can more easily evolve their networks to be faster, smarter and governed by data-driven business policies that ensure profitability through providing a superior customer experience.


Will AI change the way networks function, and, if so, how? AI has the potential to realise significant change in the telecoms industry – enabling intelligent, programmable and adaptive networks that can better meet the demands of the customer and the increasingly data- heavy services to which they subscribe. Take network bandwidth management as a specific example. Today’s dynamic environment contains millions of devices that continue to multiply. Operators are increasingly facing the challenge of ensuring that each of these devices is connected at all times and receiving the services being paid for. AI can address this directly by providing real-time deep network intelligence insights that help operators to properly allocate bandwidth depending on demand; thereby ensuring that the path from data centre to user is established and maintained. Another area in which AI will thrive in


telecoms is in conducting management and maintenance operations without human input. Self-healing networks are envisioned to be the next step in intelligent networking, enabling the network to completely repair (and potentially even reconstruct) itself in a matter of minutes, should a failure occur. Using real-time data analysis, AI will compress decision-making timelines by orders of magnitude, minimising, or even eliminating, disruptions from damaged cables or attempted network intrusions to save service providers and the operators significant revenue losses. More generally, it will bring greater


oversight, and therefore control, to operators, while allowing for usage optimisation without network disruption, and prediction of scaling requirements for hardware and virtualised assets, depending upon demand patterns. In this way, operators can ensure that the user receives a premium experience.


Where can we see examples of AI at work in telecom networks today? We’re already seeing AI tools incorporated in networks in more progressive markets across the globe, such as Australia (Telstra), South Korea (SKT), Singapore (Singtel), and the US (AT&T). Looking at AT&T specifically, the company


is incorporating AI and machine learning into customer interactions, as well as software- defined networking (SDN) functions – where signals from different nodes in the network can notify the operator of an impending failure ahead of time. Engineers can then be dispatched to complete maintenance work proactively, thereby safeguarding services


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AI tools have been incorporated in networks in progressive global markets such as Australia, South Korea, Singapore and the US


and ensuring that the customer experience is not disrupted. For other customers, such as Comcast,


there’s a significant drive on implementing AI chatbots to more effectively deal with customer queries and improve customer service ratings. We’re also seeing some managed services organisations implementing AI to manage end customer networks. Traditionally these offerings would require tailored policy provisioning, taking account of the specifics of the network, operational norms and extremes and action preferences, which would all fall to the operator to design and implement. With AI, managed services can be automated, with the machine learning the functions of the network and the various failure situations ahead of time, and then deciding upon appropriate responses – all without human intervention.


How do you see AI evolving, and what does this mean in terms of how the company operates? Though it is still maturing and most ongoing projects are very much in the development stage – managing smaller, less critical aspects and network functions – we expect to see AI branch out into mission critical management


and proactive network maintenance in the coming years. As experimental projects go live and combine with other emerging technologies, AI will evolve to uncover new possibilities and use cases, which will continue to achieve greater efficiency and performance in telecommunications networks. We are helping customers to incorporate


AI for proactive network management, to achieve an enhanced experience for the user, with minimal disruptions due to network failures and improved customer support services. The Blue Planet network health predictor


solution uses advanced analytics and machine learning capabilities to allow network operators to identify potential areas of risk in their network, so that they can proactively take action and maintain service delivery. With help from supplier partners,


operators can implement AI in their networks to achieve significant efficiency increases and cost savings, while reacting faster and with better decision making to network operations developments, and offering a greater experience to the user, thereby achieving a competitive edge in their markets. n


Issue 23 n Spring 2019 n FiBRE SYSTEMS 25


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