The past two decades have seen service assurance experience a remarkable overhaul. Evolving from siloed systems focused on collecting network events and measurements for engineers to achieving automation by leveraging network and service assurance data and AI/ML.
For our industry, this represents a true transformation fueled by two main drivers: the shifting needs of communication service providers (CSPs) and the rapid surge in technological capabilities.
Specifically, these needs encompass two main challenges. The first is financial – notably the plateauing or decline in average revenue per user (ARPU), which has catalyzed a push to optimize operational expenses. The second is technological, with advancements such as network softwarization through virtualization, cloud integration and disaggregation playing a vital role, while software evolution – and its integration with AI and automation – are creating the groundwork for streamlining manual processes.
It’s these strides in technological innovation, both in networks and software, that have substantially shaped the evolution of service assurance, thereby creating demand for more advanced functionalities.
Embracing automation in modern service assurance
Traditionally, service assurance systems focused on collecting network events and measurements, and then manipulating them to provide monitoring capabilities to the network operations center (NOC) and the service operations center (SOC). This provided engineers with information to do their work, albeit mostly manually. With the introduction of new technologies however, the focus shifted towards achieving automated self-maintenance and self-healing by leveraging assurance data and AI/ML techniques to increase the level of autonomy.
In the communication & media industry, this played out in a shift from standardized, static services to personalized, dynamic ones, a process which is now in its advanced stages. As a result, automation with AI/ML has today become a mandatory capability, empowering CSPs to cope with competitive pressures, customer expectations, operational efficiency and scalability.