Imagine walking into a telecom Network Operations Center at midnight.
The video wall is alive with traffic maps, service health, customer experience and network performance.
Thousands of network elements are operating.
Millions of subscribers are connected.
Somewhere in the network, congestion is beginning to develop.
A cloud workload is behaving abnormally.
A transport path is approaching a risk threshold.
And yet the room is unusually quiet.
Not because nothing is happening.
Because the network is handling it.
One system detects the abnormal behaviour.
Another determines which services could be affected.
The network evaluates possible actions.
A Digital Twin tests the safest option.
An AI agent coordinates the response.
Automation executes the approved action.
Service assurance verifies the result.
The network learns from what happened.
By the time an engineer looks at the event, the customer impact has already been prevented.
This is the promise of a Level 5 autonomous network.
It sounds impressive.
It also raises an uncomfortable question:
How much of this can telecom networks actually do today?
Because there is a significant difference between automating network operations and creating a network that can truly operate autonomously.
And that gap is where the real story begins.
Level 5 Sounds Impressive. But Where Are We Today?
The midnight NOC described above represents the destination.
The reality today is more complicated.
A telecom operator may already have sophisticated automation in one part of the network, predictive analytics in another, and largely manual processes somewhere else.
A RAN optimization use case might operate with considerable autonomy, while a high-risk core-network change still requires several layers of human approval.
In other words, a telecom network does not necessarily have one single autonomy level.
Autonomy can vary by domain, process, use case and operational risk.
This is why the journey from Level 0 to Level 5 is better understood as a progression of operational capabilities rather than simply a technology upgrade.
From Human-Controlled to Self-Driving Networks
| Level | Network Behavior | Who Takes the Lead? |
|---|---|---|
| Level 0 — Manual | Network detects problems, but investigation and action are manual | 👤 Engineer |
| Level 1 — Assisted | Tools provide alarms, insights and basic recommendations | 👤 Engineer + Tools |
| Level 2 — Automated | Repetitive tasks and predefined workflows run automatically | ⚙️ Automation |
| Level 3 — Conditional Autonomy | The system analyzes situations and handles selected decisions | 🧠 AI + Automation |
| Level 4 — High Autonomy | Intelligent closed loops detect, decide, act and verify across defined areas | 🔄 Network + AI |
| Level 5 — Full Autonomy | The network continuously learns, adapts and manages itself | 🚀 Autonomous Network |
Think of it like moving from a manually driven car toward a self-driving one.
At the beginning, the human makes almost every decision.
With each level, the system takes responsibility for more of the journey.
But telecom networks have one major difference:
Not every road has the same risk.
Automatically optimizing radio resources is very different from automatically making a critical core-network change affecting millions of customers.
That is why the real question may not be:
“When will telecom reach Level 5?”
It may be:
“Where does Level 5 actually make sense?”
So, Where Is the Telecom Industry Today?
The journey from Level 0 to Level 5 looks simple on paper.
But real telecom networks are much more complicated.
An operator may have advanced automation in one part of the network, while another area still depends heavily on engineers and manual processes.
For example, some activities in RAN optimization, energy management or fault handling can already run with a high level of automation.
But a critical change in the Core Network may still require careful engineering review and approval.
This means telecom operators are not moving toward autonomy at the same speed everywhere.
The journey is happening use case by use case, and domain by domain.
So where are operators today?
Most telecom networks are not yet fully autonomous.
Many operators are working around Level 2 and Level 3, while higher levels of autonomy are appearing in selected use cases and network domains.
Level 4 is becoming the important next target.
At this level, the network moves beyond basic automation. It can use intelligence and closed-loop processes to detect, analyze, decide, act and check the result with much less human involvement.
But reaching Level 4 in one use case does not mean the entire network has reached Level 4.
And this is where the challenge becomes interesting.
Why Is Level 4 Such a Big Step?
Imagine that a group of cell sites suddenly starts showing unusual performance degradation.
In a Level 3 environment, the system may detect the abnormal behavior, correlate several alarms and KPIs, identify a likely cause, and recommend an action to the operations team.
The engineer reviews the recommendation and decides whether to proceed.
Now imagine the same situation in a Level 4 environment.
The system detects the problem, understands the service impact, identifies the likely cause and selects an approved corrective action.
It then executes the action automatically within predefined policies.
But it does not stop there.
It checks whether the action actually solved the problem.
If network performance returns to normal, the loop is closed.
If it does not, the system can reassess the situation or escalate it to the operations team.
That ability to Detect → Decide → Act → Verify is what makes Level 4 much more powerful than simple automation.
Level 3 helps the engineer make the decision. Level 4 increasingly allows the network to complete the operational loop.
Which Parts of the Network Could Become Autonomous First?
Not every part of a telecom network will reach higher autonomy at the same time.
Some operations are repetitive, measurable and relatively low-risk, making them easier to automate.
Others involve complex dependencies and much greater customer or business risk.
This means the journey toward autonomous networks will probably happen domain by domain.
| Network Area | Autonomy Opportunity | Why? |
|---|---|---|
| 📡 RAN | High | Continuous optimization of traffic, capacity, interference and energy |
| ⚡ Energy Management | High | Clear patterns, measurable results and repeatable actions |
| 🔍 Service Assurance | High | AI can correlate alarms, detect anomalies and identify service impact |
| 🌐 Transport Network | Medium–High | Traffic rerouting and capacity optimization can support closed loops |
| ☁️ Cloud / Telco Cloud | Medium–High | Workload scaling and resource optimization are highly programmable |
| 🧠 Core Network | Selective | Automation is possible, but critical actions may carry much higher service risk |
This does not mean the Core Network cannot become highly autonomous.
It means the acceptable level of autonomy may depend on the risk of the decision.
Automatically optimizing energy usage is very different from automatically changing a critical routing, subscriber or policy function affecting millions of users.
So perhaps the future is not:
“Everything at Level 5.”
It may be:
“The right level of autonomy for the right operational decision.”
Level 4 Is Starting to Move From Vision to Reality
For many years, autonomous networks were mainly discussed as a future vision.
That is beginning to change.
Telecom operators are now testing and deploying higher levels of autonomy in specific network domains and operational use cases.
The important point is that operators are not trying to make the entire network autonomous overnight.
They are starting with areas where automation can deliver clear, measurable and controlled results.
KDDI — Moving Network Optimization Toward Level 4
In Japan, KDDI and Ericsson have been working on AI-driven network optimization as part of KDDI’s journey toward higher autonomous-network levels.
A large-scale commercial-network trial used AI for uplink optimization across both 4G and 5G cells.
The interesting part is not only the performance improvement.
It shows how AI can increasingly analyze network conditions and support optimization across a large live network—one of the building blocks required for higher autonomy.
China Mobile — Building Autonomy at Scale
China Mobile has also been one of the operators actively developing autonomous-network capabilities across network operations.
Its journey shows an important lesson:
Autonomy becomes meaningful when it can move beyond individual automation scripts and operate across large, complex network environments.
This requires much more than AI alone. It needs reliable data, automation platforms, operational policies and closed-loop assurance working together.
Level 4 is not one technology. It is what happens when AI, automation, data and closed-loop operations begin working together.
What Actually Makes Level 4 Possible?
Level 4 does not come from adding one AI tool to the NOC.
It happens when several capabilities begin working together.
Network data provides visibility into what is happening.
AIOps connects alarms, KPIs and events to identify what matters.
AI and intelligent agents help understand the situation and determine possible actions.
Network Digital Twins can help test selected decisions before they reach the live network.
Automation executes approved actions.
And closed-loop assurance checks whether the action actually worked.
Together, they create something much more powerful than individual automation:
Observe → Understand → Decide → Test → Act → Verify → Learn
If any part of this chain is missing, true autonomy becomes much harder.
A network that can detect a problem but cannot act is still dependent on humans.
A network that can act but cannot verify the result is simply automated—not truly autonomous.
The closed loop is what changes the game.
NETWORK DATA
↓
AIOps
↓
AI / AGENTIC INTELLIGENCE
↓
DIGITAL TWIN
Test the Decision
↓
AUTOMATION
Execute Safely
↓
SERVICE ASSURANCE
Did It Work?
↓
LEARN
↺
Do We Really Need Level 5 Everywhere?
Level 5 sounds like the final destination.
A network that can monitor, decide, act and learn with minimal human involvement appears to be the ultimate goal.
But telecom operations are not all the same.
Consider two decisions.
Decision A: Reduce energy consumption at selected radio sites during very low traffic.
Decision B: Make a major configuration change in the Core Network that could affect millions of subscribers.
Both decisions can potentially use AI and automation.
But should they have the same level of autonomy?
Probably not.
The first decision is repetitive, measurable and relatively easy to reverse.
The second may carry much greater service and business risk.
This suggests that the future autonomous network may not operate at one fixed level.
Instead, autonomy could be based on risk, confidence and potential customer impact.
The smartest autonomous network may not be the network that automates everything. It may be the network that knows what should—and should not—be autonomous.
| Type of Decision | Possible Approach |
|---|---|
| Low risk + repetitive | Higher autonomy |
| Predictable + reversible | Closed-loop automation |
| Moderate risk | AI recommendation + policy control |
| High customer impact | Stronger validation |
| Critical / irreversible | Human approval may remain appropriate |
So perhaps Level 5 should not be treated as a target for every network action.
The more practical goal may be something different:
Maximum useful autonomy — with the right level of control.
What Could the Autonomous NOC of 2030 Look Like?
Now return to the NOC we imagined at the beginning.
It is midnight.
The network is busy, but the operations center feels different.
Engineers are no longer spending most of their time moving between hundreds of alarms, dashboards and repetitive operational tasks.
Many routine events are already being handled through intelligent closed loops.
AIOps identifies what matters.
AI analyzes the situation.
Digital Twins test selected high-impact decisions.
Automation executes approved actions.
Service assurance checks the result.
And unusual or high-risk situations are brought to the operations team with the context already available.
The NOC has not disappeared.
Its role has changed.
Instead of managing every network event, operations teams increasingly manage exceptions, policies, risk and service outcomes.
Perhaps this is a more realistic picture of the autonomous NOC.
Not a network without people.
And not a network where AI controls everything.
But a network where machines handle more of what is predictable, while people focus on what requires judgment.
The journey to autonomous networks is not about removing humans from operations. It is about removing unnecessary human intervention from operations.
The Road to Level 5
The journey toward autonomous networks has already started.
Telecom operators are moving from manual operations toward automation, intelligence and increasingly closed-loop networks.
But Level 5 will not arrive everywhere at the same time.
Some network activities will become highly autonomous much faster than others.
The real challenge is not simply adding more AI.
It is building the right combination of trusted data, AIOps, automation, AI, governance and closed-loop assurance.
And perhaps the biggest lesson is this:
The goal should not be autonomy for the sake of autonomy.
The goal should be a network that can make the right decision, at the right time, with the right level of human control.
Level 5 may be the destination on the maturity model.
But the real transformation happens in every step we take toward it.
“But the real transformation happens in every step we take toward it.”
The next evolution may be intent-driven telecom operations, where operators define the required business outcome and AI helps translate it into coordinated network decisions.
As networks progress toward higher levels of autonomy, cybersecurity must evolve with them. AI-driven decisions, automated actions and increasingly connected network domains require security controls that can detect threats, govern autonomous actions and protect services at machine speed.
How Ready Is Your NOC for This Journey?
Before planning for Level 4 or Level 5, operators first need to understand where their operations stand today.
The TelcoMind AI — AI-Ready NOC Maturity Assessment helps evaluate NOC readiness across key areas including data, automation, AIOps, predictive operations, AI-assisted decision-making and closed-loop capabilities.
Take the Free AI-Ready NOC Maturity Assessment →
Discover your current maturity level and identify the areas that need attention on the journey toward intelligent and autonomous operations.
References & Further Reading
1. TM Forum — Autonomous Networks: Exploring the Evolution from Level 0 to Level 5
This is the foundation for the six-level maturity framework used throughout the article. TM Forum describes the journey from Level 0 manual operations through Level 5 full autonomy.
2. TM Forum — Assessing CSPs’ Progress Towards Level 4 Autonomous Networks
This is especially useful because it’s from March 2026 and discusses operators increasingly validating Level 4 autonomy in specific network domains, rather than claiming whole networks are already Level 4.
3. Ericsson & KDDI — AI Uplink Optimization Field Trial Toward Autonomous Networks Level 4
This directly supports the KDDI example in our article. The 2026 commercial-network trial covered approximately 1,500 5G cells and 1,300 4G cells, and the AI optimization application was assessed at Level 3.86 using an adaptation of TM Forum’s methodology.
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