The Shift Toward Digital Operators: Why Service Providers Are Embracing the Change

As the network has become a constant and ubiquitous aspect of daily life, demands have advanced far beyond the legacy expectation of just providing connectivity: from relying on ‘always connected’ services to operating mission-critical networks. The network needs to deliver a superior experience for operators, internet of things (IoT) devices, business and residential users, while reducing power and space consumption, operational expenses and removing pain-points like repetitive manual tasks.

Service providers are evolving into Digital Operators by strategically crafting and providing services centred around customer experience, adopting an experience-first networking approach. This transformation involves adept utilisation of automation, AI (Artificial Intelligence) and ML (Machine Learning) and orchestration to optimise and enhance service delivery, ensuring a seamless and customer-centric digital experience.

This can be done with AI and ML capabilities; detecting and resolving network issues before disruptions occur, essentially making the network self-driving and self-healing by leveraging closed loop automation. Also, translating business intent and data-driven decisions to make consuming digital services easy and rewarding for end customers (businesses and individuals alike) is important.

This decade is all about transparency of the network and its components to the operator and actionable insights where the network operator’s intent is enriched with queried (network) data, interpreted by AI/ML and ultimately closing the loop with suggested optimisations and potential fully automated operations. That is what makes the digital operator.

Challenges Before Success

Building a fully autonomous network, however, presents significant challenges. Many of these issues arise from the fact that network operations today still heavily rely on manual actions and lack robust detection mechanisms of security threats and vulnerabilities. Limited visibility, manual configuration and provisioning, reactive troubleshooting, complexity and scale, lack of predictive capabilities, as well as skill gaps and resource constraints, are just some of the challenges that operators face. Addressing these challenges requires a paradigm shift towards automation, AI/ML-driven analytics and closed-loop systems.

By implementing advanced automation technologies, such as intent-based networking, software-defined networking and network orchestration, service providers can overcome these hurdles. By embracing automation and AI/ML-driven analytics, service providers can enhance network visibility, improve efficiency and proactively detect and resolve issues, which also contribute to lowering the energy consumption thus making operations more sustainable. They can shift from reactive to proactive operations, predict and prevent network problems (not to mention automatically mitigate the problem 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.

Customer Journey Analysis

What would this look like in practice? Well, imagine the journey of a couple who have been using a shared mobile phone plan. Recently, they realised that their teenage son 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 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.

Digital Operator Journey

Now let’s look at the journey of a digital operator as they recognise the growing demand for a service and aim to efficiently initiate, deploy and monitor it to meet customer needs within a short timeframe. The operator's intelligent orchestration systems should leverage AI and ML algorithms to continuously analyse network performance, customer behaviours and usage patterns. These algorithms also prove very beneficial to manage the SLA’s. The systems are designed to detect signals that indicate the need for a new service, such as increased data consumption, specific app usage or customer requests. By monitoring these signals in real-time, the operator can proactively identify the demand for the service and initiate the deployment process.

Once the need for the new 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. It should dynamically adjust network parameters, reroute traffic or allocate additional resources to ensure uninterrupted service delivery. This closed-loop feedback mechanism enables 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 will help the system to identify new opportunities for improvement. It will autonomously fine-tune network configurations, optimise resource allocation and even suggest personalised service enhancements for individual customers. Thus, the digital operator could efficiently initiate, deploy and monitor a new service on their network. This automation not only accelerates the time-to-market for the 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.

In short, network automation and orchestration is a foundational component for the digital operator while improving the customer experience, eliminating the impact of human error and reducing the cost of customer churn. But not just that, also the network should automatically adjust for optimal bandwidth and power usage using closed-loop monitoring and so reduce carbon footprint, the number of manual actions and become much more end-customer centric.