Português
Español
The evolution of telecommunications networks is profoundly transforming the way operators and companies manage their infrastructure. With the growth of 5G, cloud computing, the Internet of Things (IoT), Open RAN, and the volume of data traffic, networks have become more distributed, dynamic, and complex.
In this scenario, relying exclusively on manual processes or traditional monitoring tools can limit the responsiveness of teams. The evolution to autonomous networks allows for the automation of decisions, operations, and recovery actions, making the infrastructure more efficient, resilient, and prepared to meet new demands.
But how do you know what stage a network is really in? The answer lies in measuring the level of maturity of autonomy.
What is network autonomy maturity?
Autonomous maturity represents the level of capability a network has to perform activities automatically, intelligently, and with less need for human intervention.
This autonomy can involve different functions, such as monitoring, provisioning, fault detection, performance analysis, decision-making, and resource recovery.
A network may have predominantly manual processes in certain areas while possessing highly automated resources in others. Therefore, it is not enough to assign a single level to the entire infrastructure. The evaluation must consider the different domains and capabilities of the network.
Among the environments that can be analyzed are RAN, Transport, Core, IP and Cloud.
Why measure maturity?
Measuring maturity before making new investments allows the company to understand its current situation and identify which initiatives should be prioritized.
Without a structured diagnosis, there is a risk of investing in automation or artificial intelligence tools without addressing fundamental problems related to processes, systems integration, or data quality.
Measurement helps answer questions such as:
- How much of the operation still depends on manual intervention?
- Which processes are already automated?
- Can the network detect faults automatically?
- Are there self-healing mechanisms?
- Can the systems operate in an integrated manner?
- Where can artificial intelligence have the greatest impact?
- What gaps prevent the evolution to higher levels of autonomy?
With these answers, the company can transform the evolution of autonomy into a structured strategy.
The Autonomous Network Levels model from the TM Forum
One of the main references used worldwide to assess the maturity of autonomy is the Autonomous Network Levels (ANL) do TM Forum.
The model allows for the classification of different network capabilities according to their level of autonomy, considering the evolution from predominantly manual operations to environments capable of executing processes in a highly autonomous manner.
Using an internationally recognized methodology makes it easier to identify gaps, define objectives, and compare the organization’s progress over time.
Furthermore, references such as ANL e ANLET can support more structured benchmarking and evaluation processes.
Which indicators should be evaluated?
Measuring maturity should not be based solely on team perceptions. It is necessary to use metrics and indicators capable of demonstrating the actual performance of the operation.
Among the Key Effectiveness Indicators (KEIs) which can be considered are:
Response time
How long does it take the network and teams to identify and respond to events, incidents, or degradations?
The greater the automatic response capability, the higher the level of autonomy tends to be.
Automated provisioning
It is important to assess how much of the resource configuration and deployment process can be automated.
Reducing manual intervention increases speed and decreases the possibility of errors.
Fault detection and recovery
A more mature network not only identifies problems, but it can also take action to automatically recover affected resources or services.
The ability to self-heal is an important element in the evolution of autonomy.
Use of artificial intelligence
The application of AI and Machine Learning allows for the analysis of large volumes of data, the identification of patterns, and the anticipation of potential degradation events.
This allows for a shift from a reactive operation to a more predictive approach.
Integration and orchestration
Autonomy depends on the ability of different systems to work in a coordinated manner.
Therefore, it is necessary to evaluate the level of integration between management tools, monitoring systems, automation platforms, and orchestration mechanisms.
Network domain-based evaluation
Another important aspect is to conduct the assessment individually on the different components of the infrastructure.
An operator may have a high level of automation in the RAN, for example, but still have significant manual processes in the Core, Transport, or IP.
Domain-based assessment allows you to identify exactly where the biggest gaps are and direct investments more efficiently.
This perspective also makes it easier to create a specific roadmap for each area.
From diagnosis to roadmap
After the measurement, the next step is to transform the results into an improvement plan.
The roadmap should identify key gaps and establish priorities related to:
- Automation;
- Artificial intelligence;
- Machine Learning;
- Orchestration;
- Systems integration;
- Closed-Loop Automation;
- Operational processes;
- Team training.
Evolution should be gradual and aligned with the company’s strategy, avoiding investments that are disconnected from the real needs of the business.
Autonomy is more than just technology.
A key point is understanding that network autonomy does not depend exclusively on technological tools.
Processes, people, data, governance, and operational models also need to evolve.
Therefore, a proper maturity assessment should consider both technological and organizational aspects. The combination of these factors allows for building a more efficient, resilient operation that is prepared for new services.
What are the benefits?
The evolution of maturity and autonomy can lead to a reduction in OPEX greater network availability and reliability, faster incident response, and greater agility in deploying new services.
It also contributes to improving the customer experience, since problems can be identified and addressed before they generate significant impacts.
Furthermore, autonomy prepares the organization for increasingly complex scenarios involving 5G, 6G, Open RAN, IoT and digital services.
Measuring the maturity level of a network is the first step in defining a consistent strategy for the evolution of autonomy. Using recognized methodologies, such as Autonomous Network Levels from TM Forum is possible to evaluate different domains, measure indicators, identify gaps, and establish a transformation roadmap.
The ultimate goal is not simply to automate tasks, but to build an infrastructure capable of monitoring, analyzing, deciding, and acting with increasingly less human intervention.
For operators, ISPs, and companies that rely on critical connectivity, understanding the current level of autonomy allows for more assertive decision-making, targeted investments, and structured progress towards smarter, more efficient, and resilient networks.
