What are ‘Intelligent Network Operations’ and ‘Autonomous Network Operations’ and what are the differences between them?
Intelligent Network Operations combine automation, advanced analytics, and AI/ML algorithms to optimise network management, performance, and efficiency. The goal is to reduce manual intervention and improve decision-making. Some examples are:
- Proactive network monitoring with AI/ML with functions to anticipate anomalies and predict failures.
- Automatic fault detection and resolution with the support of systems that are capable of correlating various events.
- Ability to perform predictive resource optimisation based on analysis of large amounts of historical data.
- Flexibility and versatility in designing intelligent dashboards that can provide the necessary insight to facilitate data-driven operational decisions.
As you can see in Intelligent Network Operations, human supervision is still necessary. In other words, intelligence supports humans, but does not completely replace them.
Autonomous Network Operations represent the next stage of evolution. Networks capable of operating with minimal or no human intervention. At this level of evolution, principles such as self-configuration, self-monitoring, self-optimisation and self-healing are fundamental.
Without these capabilities, it is impossible to move towards truly autonomous networks. In the industry, these are known as capabilities that align with the ‘Zero-X’ concept (zero-touch, zero-wait, zero-trouble). Among the essential aspects, the fundamental ones are:
- Self-monitoring: the network proactively detects, analyses and corrects problems without human intervention.
- Self-configuration: dynamically adjusts parameters according to context, thanks to algorithms that manage the network in real time.
- Self-healing: autonomously recovers services after failures, ensuring continuity without manual intervention.
- Self-optimisation: continuously improves performance and service quality through real-time analysis.
How do these two concepts interact?
The relationship between these two concepts is evolutionary. In the first stage, operations are optimised through automation and systems that facilitate network management. From there, progress is made towards Intelligent Operation, characterised by a higher level of automation and the use of algorithms that support decision-making.
Since then, a significant effort has been required to achieve Autonomous Networks—on which the entire telecommunications industry has been working intensively—in order to advance through the introduction of increasingly sophisticated algorithms. These algorithms include event and fault correlation, intent engines, predictive models, LLMs, digital twins, among others.
Is it possible to achieve fully autonomous network operations?
It is feasible, but the process is complex and is progressing gradually. The evolution towards autonomous networks is a journey that requires innovation, collaboration, and standards. Organisations such as TM Forum and 3GPP, leaders in the telecommunications industry, are setting the guidelines and defining frameworks that guide this transformation.
One concept driving this transformation in the sector is known as Dark NOC. Dark NOC is inspired by the automotive industry, specifically the idea of a ‘Dark Factory’ or dark assembly line, where vehicles are manufactured without human intervention thanks to total automation and advanced robotics. There are no operators, no lights are needed. Everything is done by robots and intelligent systems.
In telecommunications, the Dark NOC is similar, a virtually autonomous operations centre where human supervision is minimal. Based on technologies such as AIOps and Machine Learning, this model manages alarms, correlates events and makes adjustments in real time. The expected result is fewer errors, lower costs and a major leap towards Autonomous Network Operations.
The industry in general is moving in this direction, but still faces significant challenges, such as technological maturity, process alignment, preparing teams to work in AI-driven environments, and trust in automation. There is still some caution about allowing self-x systems to operate completely autonomously without human validation and supervision, especially in Telco networks, where complexity and operational risks are significant.
How do new technologies impact smart operations and autonomous networks?
Services such as network slicing, edge computing, massive IoT and the arrival of 6G are redefining the operating model of telcos. These technologies require agile and adaptive networks, capable of adjusting in real time to user needs.
This means that the network must not only be flexible, but also autonomous, with ‘self-x’ capabilities such as self-optimisation and self-configuration. Personalisation will be key, with users demanding networks that are dynamically configured according to their requirements, requiring intent-based orchestration and self-defining mechanisms.
In this context, autonomous operations are the only way to ensure agility, resilience and efficiency. It is not just a matter of having a smarter network, but of enabling a network that learns, adapts and acts without human intervention, reducing errors and accelerating the delivery of innovative services.
