Author: mohamedezawi

Understanding ANL and ANLET

Written on June 2, 2026. Posted in Autonomy, Autonomous Networks.

Telecoms are under constant pressure to reduce operating costs, improve service quality, and respond faster to network issues. In that environment, autonomous networks are not just a technology trend; they are becoming a strategic necessity.

Autonomous Network Level Evaluation Tool (ANLET) is an invaluable asset for telecoms moving from manual network management toward intelligent, self-managing network operations.

As networks grow more complex, ANLET provides a structured way to evaluate and measure network autonomy based on the Autonomous Network Level (ANL) model. It helps operators understand how autonomy develops and how they can progress in a measurable way.

Why Autonomous Networks Are Needed

Telecom networks now operate across multiple domains, vendors, services, and layers of infrastructure. This complexity makes traditional manual management slow, expensive, and difficult to scale. Operators can no longer rely on human intervention alone to maintain performance, reliability, and customer experience.

Autonomous networks are needed to address three major challenges:

First, they help reduce operational complexity by automating routine and repetitive work.
Second, they improve responsiveness by allowing networks to detect, analyze, and act on issues faster than humans can.
Third, they support business growth by helping operators launch services more efficiently and create a more resilient operating model.

For telecom leaders, the need is not only technical. It is also commercial. Better automation means lower costs, fewer errors, improved service continuity, and stronger customer satisfaction.

The ANL Model

The Autonomous Network Level (ANL) model defines six levels of autonomy, from Level 0 to Level 5. It provides the classification framework used to describe how autonomous a network is. Each level represents a different balance between human control and system autonomy.

Level 0 – Manual management: the system delivers assisted monitoring capabilities, which means all dynamic tasks must be executed manually.
Level 1 – Assisted management: the system executes a certain repetitive sub-task based on pre-configuration to increase execution efficiency.
Level 2 – Partially Autonomous Networks: the system enables partial automatic O&M for certain units based on pre-defined rules or policies under certain external environments.
Level 3 – Conditionally Autonomous Networks: building on L2 capabilities, the system with awareness can sense real-time environmental changes, and in certain network domains, optimize and adjust itself to the external environment.
Level 4 – Highly Autonomous Networks: building on L3 capabilities, the system enables, in a more complicated cross-domain environment, analysis and decision-making based on predictive or active closed-loop management of service and customer experience-driven networks.
Level 5 – Fully Autonomous Networks: this level is the goal for telecom network evolution. The system possesses closed-loop automation capabilities across multiple services, multiple domains and the entire lifecycle, achieving Autonomous Networks.

Source: Autonomous Networks Framework v2.0.0 (IG1218F), TM Forum.

The ANL model is the foundation of ANLET. It provides the classification system that turns network autonomy from a vague ambition into a clear and measurable maturity framework.

The ANL model also supports a closed-loop view of operations through the IAADE loop: Intention, Awareness, Analysis, Decision, and Execution. This makes it easier to evaluate whether a process is still human-led or has become genuinely autonomous.

Why the ANL Classification Matters

One of the biggest strengths of the ANL model is that it gives telecom operators a standard way to classify network autonomy. Without such a framework, it would be difficult to compare progress across networks, vendors, or business units.

It also helps operators benchmark their current state. A CSP can assess whether a fault management process is still manual, partially automated, or moving into predictive and self-correcting behavior according to the ANL classification. That makes planning more practical and less abstract.

The ANL model also supports the evolutionary path toward autonomy. Instead of trying to jump directly to full automation, operators can move step by step, building capabilities in a controlled and stable way. This reduces risk and improves adoption while progressing through the autonomy levels.

ANLET: Autonomous Network Level Evaluation Tool

Evaluating Autonomous Network Levels (ANL) is not a simple task. While the industry continues to advance toward autonomous operations, many CSPs still face challenges related to standardization, tool availability, and assessmentconsistency. Without a structured evaluation approach, it can be difficult to understand current capabilities, compare progress, or identify the next steps in the autonomy journey.

This is where ANLET comes in.

ANLET is designed to assess network capabilities against the ANL model and determine the level of autonomy achieved. By providing a consistent and repeatable evaluation methodology, it transforms network autonomy from a conceptual objective into something measurable and actionable.

Rather than simply describing a network as “automated” or “autonomous”, operators can use ANLET to identify its ANL classification and understand what capabilities are required to progress further. This provides a solid foundation for planning investments, prioritizing initiatives, and tracking transformation outcomes over time.

ANLET also creates a common language across the organization. Network engineers, operations teams, architects, and business leaders can all align around the same assessment framework, making it easier to communicate goals, measure progress, and coordinate transformation efforts.

Business Value of ANLET for Telecom Leaders

For telecom executives, ANLET is invaluable because it links technical maturity to business outcomes. By measuring a network’s autonomy level against the ANL model, it helps define where automation creates measurable value and where further investment is needed.

The benefits include lower operating costs, faster issue resolution, better service continuity, and improved customer experience. It also supports compliance, training, and innovation by giving organizations a common framework for understanding and measuring network autonomy.

ANLET is especially relevant for transformation programs because it helps leaders answer practical questions:

Where are we today?
What ANL level has our network achieved?
What capabilities are missing?
What should we prioritize next?

The journey toward autonomous networks is not defined by technology alone. Success depends on an organization’s ability to understand its current capabilities, measure progress objectively, and make informed decisions about where to invest next.

This is where ANL and ANLET work together. The ANL model provides the classification framework that defines network autonomy, while ANLET provides the practical means to evaluate and measure that autonomy consistently across domains, processes, and organizations.

As telecom networks become more dynamic, distributed, and service-driven, the ability to accurately assess autonomy will become increasingly important. Operators that can clearly measure where they are today will be better positioned to accelerate transformation, improve operational efficiency, and unlock the full value of autonomous networking.

In that sense, ANLET is more than an assessment tool. It is an enabler of the autonomous network journey, helping CSPs turn ambition into measurable progress and measurable progress into business value.

To better understand your network’s current autonomy level and define a practical roadmap for autonomous network transformation, Makman offers the ANLET assessment service to evaluate your network against the ANL model, identifies capability gaps, benchmarks your current state, and delivers actionable recommendations aligned with your business objectives. Whether you are taking the first steps toward autonomy or accelerating an existing transformation program, we can help you move forward with confidence through a structured, outcome-driven approach.

From Automation to Autonomy

Written on May 22, 2026. Posted in Autonomy, Autonomous Networks, Autonomous Operations.

Telecom operators and vendors have invested heavily in automation, but the more important strategic question is whether they are ready to delegate responsibility to systems in a controlled and measurable way. Autonomy is not simply more automation; it is a different operating model built around responsibility, governance, and closed-loop decisioning.

What Autonomy Means in Telecom

In telecom, autonomy means that a system can take responsibility for a defined scenario within policy boundaries, while humans focus on oversight, validation, and exception handling. It is broader than task execution because it extends across network, service, and business operations.

This is why autonomy should be treated as an operating capability rather than just an innovation theme. The question is not whether an organization has a few smart tools in place, but whether it can delegate responsibility in a way that is reliable, measurable, and sustainable.

Why Automation Is Not the Same as Autonomy

Automation usually refers to task execution triggered by rules, with static logic and humans still remaining in the loop. It works well for repetitive work, predefined playbooks, and siloed use cases.

Autonomy goes further. It means delegating responsibility to a system that can make dynamic decisions within policy constraints, operate in closed loops, and support broader scenarios across domains. In this model, humans do not disappear; they govern outcomes, handle exceptions, and retain strategic oversight.

The Autonomy Delegation Ladder

A useful way to frame readiness is the delegation ladder, which runs from manual work to cascading intent. L0 and L1 represent manual and assisted operations, where tools help but do not decide. L2 introduces rules, L3 adds policy-based adaptation, L4 enables cross scenario active and predictive close loop, and L5 fully delegates intent driven operations in a scenario agnostic manner.

This ladder helps make autonomy measurable rather than abstract. It also explains why one scenario may already operate at a higher level while the broader organization remains much lower on the scale. Autonomy readiness should therefore be assessed by scope and responsibility, not by a single headline score.

A Three-Layer Model for Autonomy Readiness

In telecom, autonomy develops across three layers: network, service, and business. Each layer follows the same L0-L4 scale independently, but each one has a different maturity profile and operating challenge.

The network layer is the most mature, covering RAN, core, IP, and transport, where closed-loop operations are already well established. The service layer includes assurance, complaints, and cross-domain orchestration. The business layer is newer and includes areas such as order management, billing, revenue assurance, and churn prediction.

The key point is that maturity in one layer does not automatically translate into another. A strong network automation program does not mean the service or business layers are equally ready. For that reason, autonomy readiness needs to be evaluated separately across layers and then aligned through architecture and governance.

Five Questions to Assess Autonomy Readiness

A practical autonomy readiness assessment can be built around five questions.

Can the system take responsibility in a specific scenario?
How far does that responsibility extend in the loop?
Where has it been deployed beyond the demo stage?
Can it be sustained operationally shift after shift?
And did it deliver measurable business outcomes?

These questions are useful because they separate capability, deployment, sustainability, and value. An organization may have a technically capable system that has not yet been deployed, or a deployed system that is difficult to sustain, or a working solution that creates value in one area but remains limited by other constraints.

What Telecom Autonomy Readiness Requires

Buying tools does not make an organization autonomy-ready. Readiness depends on a platform foundation, streaming-ready data, architecture clarity, and a governance model that supports new operating roles. In the telecom context, that may include SRv6 and IOAM for IP closed loops, 5G SA GitOps for AIOps-ready core operations, streaming, and multi-agentic operations.

Data readiness is often the most important constraint. Only a small share of organizations have streaming-ready data, which means many autonomy programs hit a ceiling before they reach true closed-loop operation. If the data layer is not ready, then Level 4 is less a strategy issue and more a data architecture issue.

People and governance matter just as much. As systems take on more responsibility, people move from doing the work to validating outcomes and managing exceptions. That requires clear accountability, role design, and operating discipline so that autonomy remains controlled and reliable.

How to Measure Autonomy Capability and Business Value

A common mistake in autonomy programs is mixing capability and value into one score. They are connected, but they answer different questions. Capability tells you whether the system can operateat a certain level, while value tells you what happened once it did.

This separation is important because a high capability score with weak value often points to adoption or operating-model issues. A modest capability score with strong value may indicate a well-scoped automation effort that is already delivering return. Keeping the two dimensions separate makes the conversation clearer and the investment decisions more disciplined.

Why Autonomy Matters Now for Telecom Operators

The business case for autonomy is becoming stronger because the operational cost of delay is rising. Our research points to benefits such as faster MTTR, lower NOC cost, and fewer trouble tickets, while also noting the risks of human-error outages and the tendency of transformation programs to fall short of their ambition.

That combination makes the strategic choice more urgent and more practical. The question is no longer whether autonomy is interesting in theory. The question is whether the organization has the platform, data, operating model, and governance to make it real in production.

A Practical Path to Autonomy in Telecom

A staged approach is usually the most realistic path. Makman’s Minimum Viable Transformation (MVT) model begins with an independent autonomy assessments, then maps capability gaps to autonomy levels, designs the architecture and intent model, and moves through proof of concept, pilot, production ring-fence, and scale.

This approach reduces risk while building trust step by step. It also reflects the reality that autonomy becomes credible through evidence, not declaration. In telecom, where service quality and operational complexity are both high, that disciplined sequence is especially valuable.

Autonomy readiness is not a destination; it is a discipline. The organizations that progress fastest are those that can define delegated responsibility clearly, operationalize it reliably, and measure its impact with precision. For telecom operators and vendors, the priority is to build the platform, govern the delegation, and advance from automation to autonomy in a controlled and sustainable way.

10 Myths About Autonomous Networks

Written on May 12, 2026. Posted in Autonomous Networks, Autonomous Operations.

Disclaimer: This article was first published in Luqman Shantal’s LinkedIn profile, CEO of Makman and Co-chair of MAMA project for Measuring and Managing Autonomy.

I have been a practitioner of this craft for six years. Most weeks, I have the same conversation three times. Once with a client. Once with a vendor. Once with a software provider. The conversation is about what Autonomous Networks actually is, and why their assumption about it is wrong.

The term was coined in 2019, a year before the pandemic remade everything, by a TM Forum working group I sat in. We meant something specific: a measurement of how much operational responsibility had transferred from people to systems, scenario by scenario. Six years later the term has become the hottest topic in our industry’s conferences. Operators are pursuing level four validations. Vendors are positioning around it. Software providers keep asking what they need to build. Almost no one knows what Autonomous Networks is.

This article is the conversation I have been having three times a week, written down once. Ten myths show up over and over. Here is the simplification, then the ten.

Picture a workforce analyst whose entire job is measuring how work is distributed between managers and their teams. The analyst watches how tasks transfer. Watches where the handoff fails. Recommends fixes. The analyst does not build the headcount model. Does not redesign the org chart. Does not define the role taxonomy. Does not run the capability assessment. They do one thing: measure delegation between people.

Autonomous Networks (AN) is the same role, scoped to a different object. The “manager” is the operations team. The “team” is the operational systems. The role watches how work transfers from people to systems. Watches where the handoff fails. Recommends fixes.

That is the work. Ten myths show up around it.

Myth 1: Autonomous Networks (AN) are only about the network

The brand name is misleading. AN, like AV (autonomous vehicles), is named after the most visible element but covers more than that element. AV includes the dealership, the workshop, and the regulatory layer that surrounds the vehicle. AN covers three layers: the resource layer (the network), the service layer, and the business operations layer.

ITIL was originally the IT Infrastructure Library. The acronym stayed; the explanation quietly stopped being unpacked. ITIL now describes service management across industries, and the meaning of the letters has receded into the background.

eTOM was the Enhanced Telecom Operations Map. The acronym was avoided for a while, then returned with a different framing: the business process framework.

AN will follow the same path. The letters stay. Their meaning widens. The “Networks” qualifier will fade the way “IT Infrastructure Library” faded inside ITIL. AN remains AN; the N stops pointing only at the network.

For now, the name stays. The scope does not.

Myth 2: Autonomous Networks and Autonomous Operations are the same program

They are different programs with different sponsors and different scopes.

AN is the focused closed-loop program. It measures how much operational work has transferred from people to systems, scenario by scenario. The CTO is its natural sponsor.

Autonomous Operations (AO) is the wider transformation program. It looks at all the enablers around the closed loop: operating model, capability maturity, data foundations, culture, technology readiness. The CEO is its natural sponsor.

AN is the depth instrument. AOMM (the Autonomous Operations Maturity Model) is the breadth instrument. AOMM has six dimensions: two are core (Operations and Party, both centered on the closed loop) and four are enablers (Strategy, Technology, Data, Culture). The core dimensions measure delegation in operations and the people who run them. The enablers measure the conditions that allow delegation to scale.

Each AOMM dimension has a natural owner at the C-level. Operations and Party sit with the CTO/CTIO, where the closed-loop work happens today. Technology is also the CTO’s, as an enabler rather than as the closed-loop core. Data sits with the CIO. Culture sits with the Chief People Officer. Strategy sits with the CSO. No single C-level peer can audit across all six dimensions without political friction, because four of them belong to peers. The CEO is the only role that sits above the dimension owners. That is why a full AOMM program needs CEO sponsorship: the alternative is friction, not insight.

Myth 3: Autonomous Networks are architecture work

Architects design the systems. AN watches how delegation moves through them.

The architect’s frameworks (TOGAF, ODA, eTOM) describe how the architect does their work. AN does not produce those artifacts. The architect’s input source is the operations people who already speak the language of operations. Two different crafts with two different practices, and no overlap between them.

Myth 4: Autonomous Networks are technology selection

Technology is auxiliary in this role. We do not pick the agentic framework, design the digital twin, specify the machine learning algorithm, or evaluate the data platform vendors. Those are different specializations.

What we do say about technology is what kind of help is needed at each level of delegation. Auxiliary tools that assist on individual tasks at level one. Static rules that trigger actions automatically at level two. Policies that specify a goal and apply the right rules conditionally at level three. AI models that drive the work and predict what is coming at level four.

Software providers regularly ask what they need to build. We answer in classes of capability, not in product specifications: tools that assist on individual tasks, rules that trigger automatically, policies that adapt to conditions, AI models that drive the work. The class signals what to build. The product is theirs to shape.

That is the language we use. The specific products and architectures are someone else’s work.

Myth 5: Autonomous Networks should produce target and source eTOM process designs

RFPs sometimes ask AN specialists to produce target and source process maps in eTOM. This is not AN scope. It might be part of the wider AO scope when the operating model is being redesigned, but AN itself does not redesign processes.

AN x-rays the existing process through the lens of delegation. We map who does which step today (the worker or the system) and what the level of delegation is at each closed-loop stage. We do not redraw the process. The process is already there. We measure how much of it has been delegated.

When the architecture or operating-model function does its work, they may produce target and source process maps. We give them the requirements about delegation. They produce the maps.

Myth 6: Autonomous Networks claim credit for KPI improvements

Operations teams improve their KPIs through their own work, with or without delegation. We do not claim full credit for the KPI movement. There is attribution to share with operational changes, architecture upgrades, training programs, and many other levers. What we measure is how much of the value moved through the delegation lever, because that is the lever that scales.

Myth 7: Use cases are the unit for Autonomous Networks

Companies in the Americas, Europe, Asia, and the Gulf keep asking us to deliver use cases as if those were the unit. They are not. The IOH CTO put it on TM Forum’s podcast clearly: “We are not focused on use cases. We are focused on fixing the platform.”

When you remove the architectural ceilings, your level rises across many use cases at once. Adding frequency bands enables the multi-radio-access-technology sub-scenario in energy efficiency at level four. Upgrading from TWAMP to iFit improves the awareness stage in service quality. Moving from segment routing to SRv6 improves execution. Adding telemetry streaming where polling used to be improves what the system can analyze. None of those are use cases. They are platform moves that lift the autonomy ceiling.

The framework was inherited from SAE in autonomous vehicles for a reason. We observe the driver, not the manufacturer. We watch how delegation moves from the human to the car at each level: basic cruise control, advanced cruise control, traffic jam autopilot, robotaxi. The intelligence and the systems are the manufacturer’s work. The handoff at the wheel is what we measure.

Myth 8: We should run the full AOMM enterprise assessment from day one

Not in 2026.

The industry started AN with the driver of the car: the operations team. That is where the technology is feasible today and where the ROI is clearest. The operations dimension of AOMM is the most mature dimension because that is what the industry has been working on. It covers technical and service operations. It does not yet cover BSS, core commerce, or the dealer side of the network.

Much of the core transaction processing in BSS is deterministic by design: billing, charging, order management. Deterministic work does not need autonomy in the same way operational closed loops do. Determinism is the right tool for those flows. That is part of why BSS sits lower on the autonomy priority list. The non-deterministic BSS flows, including offer management, churn prediction, and fraud detection, will move up the priority list as the AN scenario map matures into the business operations layer.

The Party dimension follows the same pattern. The other four dimensions (Strategy, Technology, Data, Culture) are enablers and they are not yet at a maturity level where a full enterprise audit produces actionable insight.

Roughly 80% of the industry today scopes AOMM to its Operations dimension only. That is the right scope for 2026 because the technology feasibility and ROI lie there.

If you are the CEO and want a cross-organization AOMM audit, the technology may not yet support your claim across all dimensions. Better to scope to where the keys actually open the doors. The driver of the car is the right starting point. The salesman at the dealership comes later, as technology matures.

There is also a sponsorship reason to scope. The full AOMM audit cuts across dimensions owned by different C-levels. Without the CEO’s authority behind it, the assessment cannot speak honestly about peer dimensions without creating friction. A CTO sponsor gets Operations and the Technology enabler. A wider audit waits for a higher-authority sponsor to commission it. We do not run an enterprise assessment without the enterprise’s senior owner asking for it.

Myth 9: Planning, design, and deployment deserve equal priority in Autonomous Networks

They are in the AN scenario map. That part is real. But they are not on the high-value scenario list, and that is not an oversight.

The current high-value scenarios are concentrated in operations: fault management, optimization, change, service assurance, customer care. The driver of the car. Operations is where the technology is feasible today and where the ROI is clearest in 2026.

Planning, design, deployment, inventory, and resource readiness have their own closed loops with different feasibility curves and different ROI. Some of that work can be autonomized, particularly the parts that are largely software-driven. Those cases will move up the priority list as the technology matures.

For now they are second-priority work, not the focus. The driver of the car comes first. The planner of the factory and the deployment engineer come later.

Myth 10: Autonomous Networks require a new competency framework and process redesign

People teams asked to support AN programs often start by trying to recreate the competency framework. They do not need to. The skills are the same skills, and so are the processes. eTOM does not need to be redesigned because of AN. We x-ray the existing process through the lens of delegation. We do not redraw it.

What changes for the worker is small but specific. Less time spent doing the task, more time spent validating what the system did. The worker becomes a checker at the edge, providing trust through judgment. The experience the worker built over years matters more, because that experience is what catches the AI’s mistakes. The role does not vanish. It moves up.

Why this matters

Most of the confusion in our industry comes from collapsing this specialization into one of the bigger boxes. RFPs, architecture practices, use-case demands, competency redesigns, process re-mappings, enterprise audits scoped beyond what the technology supports: all of them try to make AN look like something it is not.

AN should produce one thing only: a clear picture of how much work has actually moved from people to systems, and what would close the rest.

Get the role right and the program works. Get it wrong and you get confusion at premium prices.

Subscribe to the Level 4 Autonomy Insights newsletter, by Luqman Shantal, to stay informed about all things Autonomous Networks.

Autonomous Networks Upskilling Hub (ANUH)

Written on March 24, 2026. Posted in Autonomy, Autonomous Networks.

The telecom industry has made significant progress in defining Autonomous Networks. AI-native operations, closed-loop automation, and Level 3/ Level 4 maturity are now well understood.

But there is a structural issue the industry has not yet solved: the ability to scale skills at the same pace as technology.

As highlighted by Shuvo Saha (VP Education, TM Forum) during Innovate Asia 2025, learning across the telecom ecosystem is not happening at the speed, scale, or consistency required to make Autonomous Networks a reality.

Autonomous Networks require mastery of so many elements: AN Levels (ANL), reference implementations, Al-native architectures, closed loops, ANLET, high-value scenarios, data management, intent modeling, and more. No one organization — not even the biggest vendors — has all that knowledge on its own or can realistically centralize or distribute this knowledge fast enough.

A Model That Reflects How the Industry Actually Operates

The AN Upskilling Hub (ANUH) represents a deliberate move toward a more realistic model of capability development. Built through collaboration between the founding members of the Hub: Huawei and Ericsson (as technology vendors), Makman Technology Consulting (as a specialist integrator), and TM Forum (a vendor-agnostic standards and certification body), it introduces a blended approach to learning aligned with real-world deployment environments. And as the hub matures, more partners will be invited to join.

The reality is straightforward: Autonomous Networks are not built in isolation. They are delivered across multi-vendor ecosystems, shaped by cross-company collaboration, and dependent on shared frameworks, common language, and aligned benchmarks. The structure of the Hub reflects this.

A vendor-neutral foundation, led by the TM Forum, provides the baseline: standardized frameworks, consistent terminology, and industry-recognized certifications. On the other hand, partners contribute applied expertise through hands-on training, implementation experience, and domain-specific depth.

This combination addresses a long-standing gap. Industry alignment without execution depth lacks impact, while vendor-specific training without a shared foundation creates fragmentation. The Hub is designed to reconcile both.

From Learning to Capability

The central issue is not knowledge availability, it is capability transformation.

As organizations move from Level 2 toward Level 4 autonomy, the change is not limited to systems. Roles evolve, decision-making becomes more data-driven, and new competencies emerge around AI-enabled operations and orchestration. In many cases, this is where progress slows, not because the technology is immature, but because the organization is not yet equipped to operationalize it.

By combining industry-standard learning with real-world application, the Hub creates the conditions for capability to develop at scale. Its success, however, will depend on how effectively learning translates into execution, how well knowledge is retained, applied, and embedded into operational workflows.

If Autonomous Networks are to move from ambition to implementation, the industry must invest as deliberately in capability building as it has in technology.

Makman works directly with telecoms who are trying to operationalize Autonomous Networks. And what we see every day is that AN maturity is fundamentally a people journey. As organizations move from Level 2 to 3 to 4, roles change. Teams change. Skillsets change. Decision-making changes. We’ve been mapping how the AN workforce evolves and what new competencies are required at each stage of autonomy. This perspective is quite different from vendor-specific training but perfectly complementary to it. Our goal through the AN Upskilling Hub is to make sure the industry doesn’t just learn the technology; it learns how to change the way it works. We’re excited to help shape the real-world, practitioner-focused part of this collaboration. — Luqman Shantal (CEO, Makman Technology Consulting).

As part of advancing this initiative on a global stage, Luqman Shantal will be speaking at TM Forum’s Digital Transformation World (DTW) 2026 in Copenhagen this June. Building on its earlier introduction at Innovate Asia 2025, the session will further develop the AN Upskilling Hub concept and explore how organizations are redefining skills frameworks, certifications, and career pathways in response to autonomous, software-defined networks. It will also address the growing convergence between IT and network teams, highlighting the critical role of workforce strategy in enabling successful autonomous operations.

Autonomous Networks Are Not a Technology Problem

Written on March 14, 2026. Posted in Autonomous Networks.

For years, the telecom industry has approached Autonomous Networks as an AI challenge. The prevailing assumption has been that stronger algorithms and more advanced orchestration platforms would steadily unlock higher levels of autonomy.

The industry has made meaningful technical progress. Operators operate on average at approximately Level 1.9, largely in “Assisted Mode”. Many CSPs are advancing toward Level 4 Phase 1, achieving single-domain autonomy with increasing maturity. Cross-domain coordination across service and resource layers is becoming achievable through Graph Neural Networks (GNNs), digital twins, and cloud-scale AI infrastructure.

Makman’s TM Forum Catalyst, “Business-aware GNN-healing networks” builds in this domain. The initiative focuses on enabling Level 4 Phase 2 autonomy by healing faults across service and resource domains through advanced AI modeling and digital representations of network states.

The industry’s constraint now sits elsewhere.

As Luqman Shantal, CEO at Makman, states, “Autonomous Networks are not blocked by a lack of technology. They are blocked by unclear intent, weak links to end-to-end business value, and operating models never designed for autonomy.”

The transition from Level 4 to Level 5 introduces a broader coordination challenge. Level 4 focuses on cross-domain automation and system optimization. Level 5 requires alignment between business intent and network execution across the entire value chain.

At TM Forum’s Accelerate 2026 event, the discussion moved beyond referencing the business layer toward defining it in structural terms. That includes clarifying its domains, lifecycle stages, and its formal connection to service and network layers. The telecom industry has spent decades formalizing the resource and service domains. The business domain often remains insufficiently modeled in comparison.

Without explicit modeling of business intent, autonomy lacks full decision context.

Consider a scenario discussed during the event: a content creator uploads a video that quickly goes viral, and within minutes, it generates a massive surge in traffic. In most operational environments, this spike triggers immediate capacity scaling and traffic management responses, yet it also represents a commercial signal that can affect potential earnings and service level commitments. Coordinating these implications effectively requires visibility and alignment across commercial, service, and network layers simultaneously.

Level 5 autonomy involves systems that incorporate this economic and lifecycle context directly into orchestration logic. Execution decisions reflect commercial priorities alongside technical performance conditions.

This evolution introduces architectural and operating model implications. Many service providers continue to rely on escalation-based workflows, functional silos, and manual approval structures. Commercial and network KPIs are frequently managed within separate domains of accountability.

Autonomous coordination at scale requires operating models that embed economic logic, decision rights, and lifecycle dependencies into machine-executable structures. Business architecture and operational architecture must align in a consistent and traceable way.

The next stage of Autonomous Networks centers on value-chain coordination. Systems need the capacity to interpret structured business intent and translate it into coordinated action across service and resource domains.

Technology has progressed significantly. The architecture now defines the pace of advancement.

Measuring ROI on Autonomous Networks

Written on October 6, 2025. Posted in Autonomy, Autonomous Networks.

Telecom operators worldwide are investing heavily in autonomous networks; networks capable of self-monitoring, self-healing, self-optimizing, and self-configuring with minimal or no human intervention. Autonomous networks promise enhanced operational efficiency, faster service delivery, improved customer experience, and significant cost savings, positioning telcos to compete effectively in today’s digital landscape.

Defining Autonomous Networks and Measuring Levels

Autonomous networks are dynamic, self-governing systems leveraging AI and automation to manage network functions and services with minimal human oversight. To help telecom operators assess and benchmark their autonomous network levels, TM Forum has developed the Autonomous Network Levels Evaluation Tool (ANLET).

The TM Forum defines six levels (L0–L5) of network autonomy:

L0 – Manual: All operations (configuration, monitoring, fault management, provisioning) are fully manual.
L1 – Assisted: Basic automation supports repetitive tasks; humans still handle most decisions and execution.
L2 – Partial Autonomous: Automation covers specific domains or functions; limited closed-loop control; human coordination required.
L3 – Conditional Autonomous: Multi-domain automation with some autonomous decisions in predefined scenarios; human validation needed for complex cases.
L4 – High Autonomous: Broad, dynamic automation across domains; minimal human involvement limited to oversight and strategy.
L5 – Full Autonomous: Complete, end-to-end automation with self-configuring, self-healing, and self-optimizing networks; humans handle only strategic or exceptional situations.

These levels provide a structured roadmap, enabling telcos to evaluate their current “as-is” state, plan incremental advancements, and define their future “to-be” autonomous network goals aligned with strategic business objectives. It is worth noting that, from our experience, not all operators are aiming for Level 5, and that the immediate focus is on achieving practical levels of network autonomy (typically Level 3 or 4) in specific, high-value domains.

From Level Scores to ROI Metrics

While ANLET scores offer clear, quantifiable evidence of progress and a valuable benchmarking tool, these levels may not fully satisfy executive stakeholders who seek tangible business outcomes. This prompted us to conduct a preliminary analysis of multiple autonomous network pilot and real projects and identified recurring themes and metrics that telcos track to quantify their Return On Investment (ROI). These metrics, which sometimes overlap and vary by project scope, fall into four key categories:

[1/4] Operational Efficiency

This category captures how network autonomy accelerates operations and reduces manual workload:

# of Fault Tickets
Service Onboarding Time
Time Spent in Troubleshooting
Root-Cause Detection Speed
Ticket Handling Time
Repair Time
MTTR (Mean Time To Resolve)
Customer Support Interaction Duration

[2/4] Process and Workforce Automation

These metrics reflect network autonomy’s impact on workforce efficiency, which also contributes to operational efficiency:

% of Backend Manpower Replaced by AI Agents
% of Frontline Manpower Replaced by AI Agents
% of Operations Performed by Agents
% of Automated Resolution Workflows
% of Manual Operations

[3/4] Customer Experience

Autonomous networks enhance customer satisfaction by minimizing downtime and elevating service quality. Key metrics include:

# of Customer Complaints
Complaint Pre-emption Rate
Customer Experience Improvement Scores

[4/4] Cost Optimization

ROI is markedly influenced by decreased costs and better resource utilization:

OPEX
CAPEX
SLA Credits
Cost of Ownership
Energy Consumption
Resource Utilization

Telecoms investing in autonomous networks have reported substantial ROI that transcends abstract levels. Here are some time savings, which contributed to enhanced operational efficiency, as reported by telecom operators:

↓ Service onboarding time, reduced by 95%
↓ Time spent in troubleshooting, reduced by 80%
↓ Repair time, reduced by 70%
↓ Ticket handling time, reduced by 25%
↓ Customer support interaction duration, reduced from 30 minutes to realtime self-service

And regarding MTTR (Mean Time To Resolve):

↓ MTTR in IP backhaul fault handling, reduced by 25%
↓ MTTR in RAN fault management, reduced by 27%
↓ MTTR in home broadband complaint handling, reduced by 50%
↓ MTTR in end-to-end complaint handling, reduced by 65%
↓ MTTR in core network fault handling, reduced by 87%

(Source: All data is from public case studies on autonomous networks published by the TM Forum).

While TM Forum’s ANLET scoring provides a valuable framework for telcos to plan and track their transformation journey towards Autonomous Networks, real business value is realized through measurable improvements in operational efficiency, workforce automation, customer experience, and cost optimization. By understanding and monitoring effective ROI metrics, telecom executives can confidently justify investments, steer transformation initiatives, and maximize the value derived from autonomous network deployments.

Reaching Peak Network Autonomy

Written on July 21, 2025. Posted in Autonomous Networks, Autonomy.

The concept of autonomy in the realm of telecommunications represents a pivotal shift in how networks are managed and services are delivered. In theory, full autonomy might be envisioned as a state of complete, self-governing operations, where human intervention is eliminated. This aligns with the aspirational vision of fully autonomous networks, capable of self-configuration, self-healing, and self-optimization. However, the practical reality is far more nuanced. Achieving full autonomy across complex, dynamic telecom environments presents significant challenges, leading to a critical question: Is full autonomy always the ultimate goal, or is there an optimal level of autonomy that balances efficiency, cost, risk, and human oversight?

Artificial Intelligence (AI) is rapidly emerging as a transformative force in this evolving landscape. Its growing role in network operations and service delivery is enabling unprecedented levels of automation and intelligence, moving beyond traditional scripting and rule-based systems to more adaptive and predictive capabilities.

To provide a standardized framework for understanding and measuring autonomy in networks, the TM Forum® and its members have developed the autonomous network levels model (ANL model), classifying autonomous networks into six distinct levels (L0 to L5):

L0 (Manual Operations): At this foundational level, all network operations, including configuration, monitoring, fault management, and service provisioning, are performed manually by human operators. There is minimal to no automation in place.
L1 (Assisted Operations): This level introduces basic automation for repetitive and routine tasks. Human operators are still heavily involved in decision-making, analysis, and execution, but they are assisted by tools and scripts that streamline certain processes. The primary goal is to increase operational efficiency for specific subtasks.
L2 (Partial Autonomous Networks): Automation extends to specific domains or functions within the network, allowing for automated execution of certain tasks or processes. While some closed-loop automation may exist within these isolated domains, human intervention is still required for cross-domain coordination, complex problem-solving, and overall strategic decision-making. This level often involves partial automation of operations and maintenance.
L3 (Conditional Autonomous Networks): This level signifies a more advanced stage where automated operations span multiple domains, with some degree of cross-domain coordination. The system gains the ability to analyze situations and make decisions autonomously in predefined scenarios. However, human oversight remains critical for handling complex or unforeseen situations, and for validating decisions before execution. This is often characterized by conditional automation, where human approval is needed for certain actions.
L4 (High Autonomous Networks): At this level, automation is extensive and dynamic, covering most operational processes across multiple domains. The system can analyze and make decisions in a wide range of complex scenarios, often with minimal human intervention. Human involvement is primarily reserved for governance, ethical considerations, strategic planning, and managing rare exceptions that fall outside the system’s learned parameters. This represents a significant leap towards self-governing capabilities.
L5 (Full Autonomous Network): This is the pinnacle of network autonomy, characterized by complete, end-to-end automation across all network domains. The system is fully self-configuring, self-healing, and self-optimizing, operating with no human intervention. Human roles are limited to high-level strategic direction and extreme, unforeseen circumstances. This level embodies a truly zero-touch operational model.

To assist telecom operators in evaluating and comparing the levels of their autonomous networks, TM Forum has created the Autonomous Network Levels Evaluation Tool (ANLET).

Challenges to Autonomy

It is important to clarify that, as of today, there are no telecom operators who have reached L5 full autonomous networks. The journey to higher levels of autonomy is complex and ongoing. Several factors contribute to this, including:

Cultural resistance: A significant barrier is the human element. Employees may fear job displacement, loss of control, or a lack of understanding of AI-driven decisions. This can lead to resistance to change and a reluctance to fully embrace autonomous operations.
Unclear ROI on autonomous networks: The upfront investment in technologies and processes required for higher levels of autonomy can be substantial. Operators may struggle to clearly quantify the financial benefits, making it difficult to justify the investment, especially when competing with other strategic priorities. While some studies suggest significant ROI, the perception of unclear returns can slow adoption.
Legacy infrastructure and systems: Many existing telecom networks are built on complex, siloed legacy systems (OSS/BSS). Integrating new AI-driven autonomous solutions with these older systems is a massive undertaking, often fraught with technical difficulties and high costs. This complexity can derail automation efforts.
Technological feasibility: Even in cases where legacy systems are not a constraint, the technology required to support full autonomy is not yet fully available. Level 5 capabilities—such as strategic decision-making, self-evolving policies, and autonomous intent generation—depend on technologies like Artificial General Intelligence (AGI), which remain in early stages of development. As a result, some autonomy goals are currently beyond reach.
Trust in AI-driven decisions: A critical aspect is building trust in AI systems. Operators need to be confident that autonomous systems will make correct and reliable decisions, especially in critical network operations.
Skills and talent: Moving from traditional operations to AI-enabled autonomy demands specialized expertise in areas such as DevOps, MLOps, and AIOps. Many telecom operators face significant challenges in attracting, developing, and retaining such talent. This is not merely a cultural issue—it is a structural skills gap. Without the right human capital, even the most advanced tools cannot be effectively deployed or scaled.

According to recent research by the Capgemini Research Institute, conducted in collaboration with TM Forum, 84% of telecommunications operators currently operate at Level 1 or Level 2 autonomy, with 61% of operators planning to achieve at least Level 3 autonomy within the next five years. However, despite this growing ambition, most use cases remain at the proof-of-concept stage. And while they recognize the benefits of higher autonomy, they are often ‘stuck’ due to a combination of the factors aforementioned, particularly the challenges of integrating disparate systems and overcoming internal cultural resistance.

Peak Autonomy vs. Full Autonomy

While the concept of full autonomy might seem like the ultimate goal, not all telecom operators are actively pursuing it, nor do all contexts benefit from it. The desirability of full autonomy often clashes with practical realities, leading many to aim for an optimal level rather than the maximum.

For many operators, achieving L5 is neither necessary nor desirable given their specific business needs and operational contexts. Instead, levels like L3 or L4 may represent their practical “peak autonomy,” offering substantial benefits without the prohibitive costs and complexities of L5.

Peak autonomy can be defined as the maximum beneficial level of autonomy within a given system or context. This does not necessarily mean reaching the highest possible level (L5), but rather identifying the point where the benefits of increased autonomy—such as efficiency, cost reduction, faster service delivery, and improved reliability—are maximized, while associated complexities, risks, and investments remain manageable. Beyond this point, the marginal gains may diminish, or the trade-offs may outweigh the benefits.

Peak Autonomy in Telecom: What It Looks Like

For telecom operators, achieving higher levels of autonomy translates into tangible benefits across various operational domains. While L5 full autonomy remains a distant goal for most, significant progress is being made in implementing advanced automation and AI-driven capabilities that exemplify peak autonomy in practical terms.

Here are a few examples:

Network Operations

Predictive maintenance: AI and machine learning models analyze vast amounts of network data (e.g., sensor readings, performance metrics, historical fault data) to predict potential equipment failures or performance degradation before they occur. This allows operators to proactively schedule maintenance, replace components, and prevent service outages, shifting from reactive to proactive network management. This reduces downtime and operational costs.
Closed-loop fault detection and resolution: This involves automated systems that can detect network faults, diagnose their root causes, and initiate corrective actions without human involvement. This closed-loop automation significantly reduces the time to resolve issues, minimizes service impact, and improves network reliability. It moves beyond simple alarms to intelligent analysis and automated remediation.

Customer Experience

Personalized service delivery: AI enables telecom providers to offer highly customized services and experiences based on individual customer behavior, preferences, and usage patterns. This includes personalized product recommendations, tailored service plans, and proactive support based on predicted needs.
Intent-Based service provisioning: While still in early stages, this involves translating high-level business intents (e.g., “provide high-bandwidth connectivity for a new enterprise customer”) into automated network configurations and service deployments. AI interprets the intent and orchestrates the necessary network changes, enabling rapid and error-free service provisioning.

Service Delivery and Lifecycle

Zero-touch onboarding and provisioning: Automation streamlines the process of onboarding new customers and provisioning services, eliminating manual steps and reducing errors. This enables faster service activation and a seamless customer experience.
Dynamic service scaling: Networks can automatically scale resources up or down based on demand fluctuations, ensuring optimal performance and efficient resource utilization. This is particularly crucial for managing traffic spikes and supporting dynamic services.

Human + Machine: Not an Either/Or

The increasing autonomy in telecom networks does not signal the end of human involvement. Instead, it marks a shift in workforce roles—from manual operators to orchestrators, overseers, and strategic decision-makers. The relationship between humans and machines in autonomous networks is symbiotic, not a zero-sum game.

Makman’s CEO, Luqman Shantal, during his keynote in DTW 2025.

As our CEO, who is also a Co-chair on the Measuring and Managing Autonomy (MAMA) Project at the TM Forum®, Luqman Shantal, puts it, “The more systems automate, the more humans matter.” In his keynote at DTW, he emphasized that in an era where machines optimize decisions and processes, it is human qualities like emotional clarity, strategic discernment, and the ability to lead through uncertainty that become the defining advantages. Automation may handle the technical, but it is human insight that guides direction, inspires confidence, and transforms complexity into coordinated action.

We believe that even at the highest autonomy level, human oversight should remain essential—especially in areas such as governance, ethics, exception handling, and strategic alignment, where AI lacks judgment and contextual awareness. To meet these demands, telecom professionals must build new capabilities in data literacy, AI oversight, critical thinking, and communication—ensuring effective human-AI collaboration across autonomous network operations.

In conclusion, peak autonomy in telecom is not about reaching a theoretical maximum, but about finding the most effective and beneficial level of automation for a given context. It is a journey of continuous improvement, driven by AI and data, and characterized by a collaborative human-machine partnership that prioritizes optimal outcomes over absolute automation. The road ahead is not about a single destination, but a series of evolving milestones that enable telecom operators to build more resilient, efficient, and customer-centric networks for the future.

Reaching Your Peak Network Autonomy

Reaching your peak network autonomy starts with a clear understanding of where you stand today. Telecom operators must begin by assessing their current level of network autonomy—technically, operationally, and organizationally. This as-is assessment helps define a realistic target: the next level that aligns with their business priorities and operating context.

From there, the journey forward doesn’t have to be disruptive. Instead of large-scale transformations, operators can move through iterative steps—pilots, focused initiatives, or domain-specific upgrades. This Minimum Viable Transformation (MVT) approach reduces risk, builds internal momentum, and delivers measurable impact without overwhelming the organization.

There’s no universal blueprint. But with the right foundation, a clear ambition, and a pragmatic path forward, operators can advance toward smarter, more autonomous networks—at a pace and scale that works for them.

Getting Started with Autonomous Operations Maturity Model (AOMM)

Written on March 2, 2025. Posted in Autonomous Operations, Autonomy.

In today’s fast-paced digital landscape, organizations are constantly seeking ways to enhance efficiency, reduce costs, and improve service delivery. One of the most promising approaches to achieving these objectives is Autonomous Operations—a field that leverages Intelligent Automation to create integrated, self-sustaining processes with minimal human intervention.

To guide telecoms and digital service providers on their journey toward operational autonomy, TM Forum’s Autonomous Operations Maturity Model (AOMM) serves as an invaluable framework. The AOMM provides a structured pathway to assess and improve an organization’s maturity in autonomous operations, ensuring a seamless transition toward fully automated, intelligent processes.

Understanding the AOMM dimensions

The Autonomous Operations Maturity Model (AOMM) consists of six key dimensions that collectively determine an organization’s level of maturity in autonomous operations. They are, in no specific order:

Party – Involves intelligent automation in customer, partner, and device interactions, ensuring seamless communication across all stakeholders.
Technology – Focuses on the technological infrastructure that supports digital transformation, automation, and innovation.
Culture – Encourages an organizational mindset shift toward adaptability, innovation, and collaboration to drive automation efforts forward.
Strategy – Establishes a clear roadmap for transitioning to autonomous operations, ensuring alignment with business goals.
Operations – Enhances processes by leveraging automation to maximize efficiency and minimize human errors.
Data – Utilizes self-analysis and automated decision-making processes to achieve full-scale automation.

Understanding the AOMM maturity levels

The AOMM (Autonomous Operations Maturity Model) taxonomy categorizes organizations based on their maturity in adopting autonomy-backed technology and business operations. The levels progress as follows. Each level reflects an increasing contribution to business outcomes as organizations mature in their adoption of autonomous operations.

Initiating: The organization is in the early stages of formulating autonomy-backed technology, behaviors, and capabilities.
Emerging: Isolated cases of autonomy-backed technology and business operations are implemented in specific parts of the enterprise, with targeted improvements.
Performing: A coordinated and innovative approach is taken, with autonomy-backed technology expanding across multiple areas of the enterprise. Ongoing improvements aim to achieve scale and scope.
Advancing: Organization-wide implementation of autonomy-backed technology is realized, with operations designed to achieve competitive advantage.
Leading: Represents best-in-class adoption, where autonomy-backed technology is embedded into enterprise and ecosystem lifecycle management. The organization leverages best practices to drive leadership in autonomous operations.

The Role of AOMM in Digital Transformation

Autonomous operations go beyond traditional automation by integrating self-learning, self-governance, and self-adaptation into organizational workflows. The AOMM framework helps businesses assess their current state of automation, identify gaps, and implement targeted strategies for improving operational maturity.

And by leveraging AI-driven insights, closed-loop automation, and data-driven decision-making, organizations can:

Enhance efficiency and reduce operational costs
Improve agility and scalability
Minimize human errors and optimize resource utilization
Foster innovation and accelerate digital transformation

Useful Resources

For organizations looking to deepen their understanding of Autonomous Operations, TM Forum offers several essential resources (membership required for access):

GB1042A: Autonomous Operations Maturity Model (AOMM) – Offers a structured approach to assessing automation performance, integrating various industry maturity models.
GB1042B: AOMM Operations Best Practice – Serves as a practical guide for organizations to evaluate their autonomous operations maturity and set strategic transformation targets.
IG1346: TM Forum AOMM Survey Report – Presents industry insights and detailed observations on Autonomous Operations, serving as a self-assessment tool for organizations.
IG1403: Comparing AOMM and Autonomous Network Levels Evaluations – Provides insights into how AOMM supports enterprise-wide maturity assessment, while the Autonomous Network Level Evaluation Tool (ANLET) focuses on domain-specific network scenarios.
Members of the TM Forum can access these resources on the website. Simply visit the website, log in, and search for the mentioned standards and guidebooks to obtain the latest versions.

The journey toward autonomous operations is a strategic imperative for organizations striving for digital excellence. By adopting the Autonomous Operations Maturity Model (AOMM), businesses can systematically enhance their automation capabilities, align their strategies with technological advancements, and drive meaningful innovation.

With the right framework, resources, and commitment to transformation, organizations can unlock new levels of efficiency, agility, and competitiveness—ensuring sustained growth in an increasingly automated world.

To better understand your organization’s current position and define a practical roadmap toward autonomous operations, Makman offers AOMM assessment service, a.k.a autonomous operations maturity assessment, tailored to telecom operators and digital service providers. Our assessment helps identify maturity gaps across all AOMM dimensions, benchmark current capabilities, and provide actionable recommendations aligned with your business and transformation objectives. Whether you are at the initiating stage or advancing toward industry leadership, we can support your journey with a structured, outcome-driven approach.

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