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Automation of telecommunications networks has become a priority for companies that need to deal with increasingly complex environments. The growth of 5G, the expansion of cloud computing, the increase in data traffic, the Internet of Things (IoT), and the need for greater service availability are making traditional operating models increasingly less efficient.
In this scenario, investing in automation, artificial intelligence, machine learning, orchestration and Closed-Loop Automation can bring significant gains. However, there is a question that must be answered before choosing any technology: What is the current level of maturity of the network’s autonomy?
Measuring this maturity allows one to understand the existing scenario, identify gaps, and define which investments truly make sense for the organization.
Automation does not necessarily mean autonomy.
One of the biggest mistakes companies make is thinking that having automated tools means having an autonomous network.
They are related but different concepts.
A tool can automate a specific task, while autonomy involves a much broader range of capabilities. A truly autonomous network needs to be able to perceive what is happening, analyze information, make decisions, and execute actions in a coordinated manner, progressively reducing the need for human intervention.
Therefore, before investing in new platforms, it is necessary to understand which processes are already automated and which still depend on manual activities.
What does maturity measurement allow us to discover?
Maturity assessment provides a structured view of the current state of the infrastructure.
Among the questions that can be answered are:
- Which processes are still performed manually?
- Where are automation solutions already in place?
- Can the network detect faults automatically?
- Is there a capacity for self-recovery?
- How long does it take to respond to an incident?
- Are systems from different domains integrated?
- Does the organization use AI and Machine Learning in its operations?
- What are the main obstacles to increasing autonomy?
These answers help transform a perception-based decision into a data-driven strategy.
Avoiding inappropriate investments
Investing in technology without knowing its level of maturity can lead to waste.
A company might acquire a sophisticated orchestration platform, for example, but discover that its processes are not yet prepared to use it properly or that existing systems lack sufficient integration.
There may also be problems related to data quality, lack of standardization, or a lack of internal expertise.
Measurement allows us to identify these limitations before large investments are made.
In this way, the company is able to establish a logical sequence: Diagnose, identify gaps, prioritize them, plan, and only then invest.
The role of Autonomous Network Levels
An important reference for this process is the model.Autonomous Network Levels (ANL) from TM Forum.
The model provides a methodology for evaluating the evolution of network autonomy and allows for the identification of different levels of maturity.
Using an internationally recognized benchmark facilitates benchmarking and helps companies establish concrete goals for improvement.
Furthermore, references such as ANL e ANLET can support the identification of gaps and the definition of priorities for infrastructure evolution.
Evaluate different network domains.
The maturity of autonomy is not necessarily uniform across all infrastructure.
An organization may have greater automation in one environment while still relying on manual processes in others.
Therefore, the evaluation can consider different domains, such as:
- RAN;
- Transport;
- Core;
- IP;
- Cloud.
This perspective allows us to identify where the biggest gaps are and which areas can generate the greatest return on investment in automation.
Indicators help in making decisions.
The measurement should also use objective indicators, such as Key Effectiveness Indicators (KEIs).
Aspects evaluated may include response time, level of provisioning automation, fault detection and recovery capabilities, use of artificial intelligence, and system integration.
These indicators help management track the evolution of maturity over time.
More importantly, they allow for setting goals and measuring whether the investments made are effectively increasing the network’s autonomy.
From assessment to roadmap
After identifying the current stage, the organization can build a roadmap for evolution.
This plan should establish which capabilities need to be developed first and which technologies can contribute to achieving the defined objectives.
Possible initiatives include:
- Implementation of orchestration tools;
- Integration between systems;
- Machine Learning;
- Artificial intelligence;
- Process automation;
- Closed-Loop Automation;
- Intelligent monitoring;
- Team training.
Priorities should be determined based on the impact on the business, the complexity of implementation, and the existing level of maturity.
People and processes also need to evolve.
Autonomy is not built solely with technology.
Operational processes, teams, and governance models also need to keep pace with the transformation.
If professionals lack knowledge of new technologies or if processes continue to be based on manual approvals and interventions, their capacity for autonomy will be limited.
Therefore, measurement and optimization initiatives should also consider this: training, processes and organization.
Greater efficiency and lower costs.
A structured evolution of autonomy can generate significant benefits for telecommunications companies.
Among them is the reduction of OPEX greater network availability and resilience, faster incident response, and increased agility in deploying new services.
Furthermore, the ability to identify failure trends before they occur can reduce impacts on customers.
The result is a more efficient operation and a more consistent service experience.
Autonomy as a business strategy
Measuring maturity before investing in automation allows the company to stop making decisions based solely on available technology and start investing according to its real needs.
This is a fundamental point: Network autonomy should not be treated merely as a technological initiative, but as a business strategy.
A more autonomous network allows for lower operating costs, faster response to changes, increased reliability, and prepares the infrastructure for new service models.
Investing in automation without measuring the maturity of autonomy can mean spending resources on technologies that the organization is not yet prepared to fully utilize.
The assessment allows us to understand the current scenario, identify gaps, compare maturity with international benchmarks, and establish a realistic roadmap.
With a structured approach, the company is able to direct its investments to the areas that truly need to evolve, combining processes, people, data and technology.
Before asking which automation tool your company should acquire, it’s more important to answer: Where is your network today and what level of autonomy does it need to achieve?
This answer is the starting point for building a more efficient, sustainable automation journey aligned with the strategic objectives of the business.
