Manfred Felsberg, senior director, global data center business, Juniper Networks discusses how a paradigm shift towards automation, AI/ML-driven analytics and closed-loop systems is now considered fundamental to future success.
Because the network has become a constant and ubiquitous aspect of daily life, the demands on networks have advanced far beyond the legacy expectation of just providing connectivity: from relying on ‘always connected’ services to operating mission-critical networks that in turn drive mission-critical deployments. Today’s network needs to deliver a superior experience for operators, devices – Internet of Things (IoT) included –, business and residential users, while reducing power and space consumption, operational expenses and eliminating many pain-points, for example, repetitive manual tasks.
It is worth mentioning that the UAE and Saudi Arabia, the region’s leaders in digital economic transformation, have been estimated to have spent approximately US $20 billion and US $34.6 billion, respectively, on information and communications technology (ICT) in 2023. Amidst the current increasingly competitive environment, many service providers are paving the way to become digital operators. A popular method to accomplish this is through the implementation of artificial intelligence (AI) and machine learning (ML) capabilities; detecting and resolving network issues before disruptions occur, essentially making the network self-driving by leveraging closed loop automation. Translating business intent and data-driven decisions to make consuming digital services easy and rewarding for end customers (businesses and individuals alike) is equally important. All of these elements define the new breed of digital operators.
Overcoming Challenges
Building a fully autonomous network presents significant challenges. Many of these issues arise from the fact that network operations today often still heavily rely on manual actions and may lack robust detection mechanisms for security threats and vulnerabilities. Limited visibility, manual configuration and provisioning, reactive troubleshooting, complexity and scale, lack of predictive capabilities, as well as resource constraints, are just some of the challenges that operators face. Furthermore, a shortage of skills persists across the Middle East, although long-term national strategies such as the We the UAE 2031 and the Saudi Vision 2030 already prioritise the development of future talents. Thus, a paradigm shift towards automation, AI/ML-driven analytics and closed-loop systems is now considered fundamental to future success.
By implementing advanced automation technologies, such as intent-based networking, software-defined networking and network orchestration, service providers can overcome these hurdles. For instance, service providers that embrace automation and AI/ML-driven analytics can enhance network visibility, improve efficiency and proactively detect and resolve issues, which also contribute to lowering energy consumption and making operations more sustainable. Considering how Middle Eastern governments now prioritise sustainability, the latter is particularly important.
Automation, AI and ML can also help service providers move from reactive to proactive operations, predict and prevent network problems (as well as automatically mitigate those that do occur), and optimise network performance. Recognising the limitations of manual operations and investing in automation and AI/ML technologies is crucial for service providers to address current network issues, enhance operational efficiency and deliver reliable and high-performing services to customers.
Meeting the Digital Demands of Today’s Customers
So, what does this actually mean for the end customer? Imagine a couple who has shared a mobile phone plan for several years. However, their teenage son has added a new variable to the equation; he now needs his own phone for school and social activities. By leveraging data analysis, personalised offers, intuitive interfaces and efficient backend processes, a digital operator could seamlessly facilitate the journey of adding a new member to a shared mobile phone plan in an optimal way for the customer.
This kind of advanced automation would not only enhance customer experience but also provide the telco with valuable insights for optimising its services and anticipating future needs. When fully automated and integrated, within an hour or two of initiating the transaction, the couple’s son could have his own phone service up and running, ready to enjoy the benefits of connectivity. The telco’s systems would automatically update the shared account to reflect the addition, ensuring accurate billing and transparent management of the family’s mobile services.
How Digital Operators can Effortlessly Adapt to Shifting Expectations
Now, let’s have a look at the journey of a digital operator as they recognise the growing demand for a particular new service and aim to efficiently initiate, deploy and monitor it to meet customer needs within a short timeframe. Using AI and ML algorithms, the operator’s intelligent orchestration systems continuously analyse network performance, customer behaviours and usage patterns. These algorithms also prove highly beneficial for managing service-level agreements (SLAs). The systems are designed to monitor signals that indicate the need for a new service in real-time, such as pay as you go, unlimited streaming during major holidays and add-on for unlimited data.
Once the need for the updated service is detected, the operator’s closed-loop automation system comes into action. It intelligently orchestrates the deployment process by automatically configuring the network resources, allocating bandwidth and ensuring optimal service quality. By leveraging AI/ML, the system can make intelligent decisions on resource allocation based on demand and network conditions.
Throughout the deployment process, the closed-loop automation system continuously monitors the service’s performance. It collects real-time data on network performance, user experience and service quality metrics. In the event of any performance degradation or anomalies, the closed-loop automation system triggers automated remedial actions. For example, it can dynamically adjust network parameters, reroute traffic or allocate additional resources to ensure uninterrupted service delivery. These closed-loop feedback mechanisms enable the operator to swiftly address any service disruptions or issues, minimising downtime and maximising customer satisfaction.
The AI/ML capabilities continuously learn from network data and user feedback, which helps the system identify new opportunities for improvement. It can autonomously fine-tune network configurations, optimise resource allocation and even suggest personalised service enhancements for individual customers. Consequently, the digital operator can efficiently initiate, deploy and monitor a new service on their network. This automation not only accelerates the time-to-market for each new service, but also ensures seamless performance, proactive issue resolution and continuous optimisation. Ultimately, it empowers the operator to deliver exceptional customer experiences and stay ahead in the ever-evolving, highly competitive digital landscape.
This decade is all about actionable insights along with network and component transparency for the operator. Therefore, network automation and orchestration serve as the foundation for the digital operator, enhancing the customer experience, eliminating the impact of human error and reducing the cost of customer churn. Furthermore, the network must automatically adapt to ensure optimal power and bandwidth usage by employing closed-loop monitoring. This has the potential to greatly help operators in managing sustainability, streamlining processes and placing a greater emphasis on end customers. In such a competitive market landscape, the smart operators will surely take the AI-driven network route.
