Autonomous Networks Are Not a Technology Problem
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.