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Autonomy Shifts From Tech Goal to Economic Strategy

For years, telecom has talked about automation as a technical ambition. Closed loops. AI-driven optimization. Zero-touch operations. The language has been consistent, almost ritualistic. But what became clear at MWC26 was not just that automation was advancing, it was that autonomy was being reframed as an economic strategy. That was a subtle but IBM Mar 4, 2026 Title: Autonomous Networks at MWC26: When AI Becomes Operational Intelligence The telecom industry’s early automation efforts were largely centered on the network itself. Across conversations at MWC26 in Barcelona, a subtle shift in how the industry talks about autonomous networks began to emerge. Autonomous networks are no longer being framed only as a telecom engineering challenge. They are increasingly being reframed as an AI and data orchestration opportunity. At the IBM booth, the discussion around autonomy was less about network elements themselves and more about the intelligence layer that orchestrates them. Instead of focusing exclusively on automation within network domains, IBM’s approach emphasized the role of AI, observability, and hybrid cloud platforms in creating the conditions where networks can become autonomous. In other words, autonomy is not just about networks learning to operate themselves. It’s about building the intelligence fabric that allows them to do so. From Network Automation to AI-Driven Operations Telecom has spent years building automated workflows: fault detection, traffic optimization, closed-loop remediation. These systems improved operational efficiency, but they still required humans to interpret signals and guide decisions. IBM’s perspective at MWC26 pushed the conversation a step further. Autonomy begins when AI systems can understand operational context, correlate signals across domains, and recommend — or execute — corrective actions in real time. This requires something telecom networks historically struggled with: a unified view of data across infrastructure layers. Network telemetry, IT systems, customer experience metrics, and business operations have traditionally lived in separate silos. IBM’s approach focuses on integrating those signals into AI-driven operational platforms — such as IBM Cloud Pak for Network Automation — that can reason across the entire environment. When networks gain that cross-domain visibility, the shift from automation to autonomy becomes possible. Observability as the Foundation of Autonomy A recurring theme across IBM’s demonstrations at MWC26 was observability. Autonomous systems cannot function without accurate and continuous awareness of the environment they operate in. Modern telecom infrastructure generates enormous volumes of telemetry: performance metrics, alarms, logs, user behavior data, application traffic patterns. The challenge is no longer collecting this data. It’s making sense of it fast enough to act. IBM’s focus on AI-powered observability highlights how machine learning models can detect patterns, correlate anomalies across network and IT domains, and surface root causes far faster than human operators could manually. In practice, this means a network issue might be detected, analyzed, and mitigated before customers ever experience degradation. Autonomy begins with awareness. Observability becomes the nervous system of the network. Hybrid Cloud as the Control Plane Another consistent theme across the IBM Telco narrative was the role of hybrid cloud architectures. Telecom networks are no longer confined to centralized infrastructure. They operate across data centers, public cloud environments, edge locations, and private operator platforms. Autonomous operations require a control plane capable of operating across all of these domains. IBM’s hybrid cloud strategy positions platforms such as Red Hat OpenShift as the operational layer enabling AI-driven automation and observability to function consistently across distributed infrastructure. This architecture matters because the future telecom network is not a single system. It is a distributed digital platform. Autonomy therefore requires orchestration across environments — not just within individual network functions. The Role of Generative and Agentic AI One of the most interesting signals at MWC26 was how generative AI is beginning to enter telecom operations. IBM demonstrated how AI assistants and large language models can help operations teams interpret complex network data, automate troubleshooting workflows, and generate remediation steps. These systems effectively act as operational copilots for network engineers, translating complex telemetry into actionable insights. But the trajectory goes further. As AI systems evolve into more capable agentic architectures, they will increasingly move from recommending actions to executing them autonomously under defined policies. This progression — from human-assisted operations to AI-directed operations — is likely to define the next phase of telecom transformation. It also highlights why autonomy is not purely about network automation. It is about AI-native operations. The Economic Imperative While different vendors approach autonomy from different architectural starting points, the strategic destination increasingly looks the same — a trajectory also reflected in the industry frameworks developed by TM Forum around autonomous network maturity. Like Ericsson’s framing of autonomy around intent-driven architectures, IBM’s message ultimately converges on the same underlying reality. Autonomous networks matter because they affect economics. Telecom operators face a structural challenge: network complexity continues to grow while margins remain under pressure. Manual operations cannot scale with the complexity of modern networks. AI-driven autonomy changes the equation by enabling: • Faster fault detection and remediation • Lower operational costs • Improved customer experience • More efficient infrastructure utilization The value is not theoretical. It is measurable in operational efficiency and service reliability. Autonomy becomes investable when it produces those outcomes. The Convergence of AI and Connectivity What stood out most from IBM’s presence at MWC26 was the recognition that telecom networks are becoming part of a broader AI-driven digital infrastructure. Connectivity platforms, cloud platforms, and AI platforms are converging into a single operational ecosystem. The network is no longer just transport. It is becoming an intelligent system capable of sensing, learning, and adapting. Autonomous networks represent the operational manifestation of that transformation. And while the industry still has work ahead — particularly around data integration, interoperability, and organizational change — the direction of travel is unmistakable. Telecom is entering an era where AI becomes the operating system of the network. And when that happens, autonomy stops being a feature. It becomes the foundation of the modern network. shift. In conversations around the Ericsson pavilion, the emphasis wasn’t on isolated AI capabilities. It was on intent-driven architectures, systems designed to translate business objectives directly into network behavior. Automation reduces effort. Autonomy aligns outcomes. And that difference changes how networks are built. From Tasks to Intent The industry often references TM Forum ’s Autonomous Network Levels as a roadmap toward full autonomy. Most operators remain between Levels 1 and 3, structured automation with humans still central to decision-making. But maturity is no longer being defined purely by how many processes are automated. It’s being defined by whether the network understands intent. Intent-driven systems begin with an outcome: a performance target, an SLA guarantee, an energy efficiency objective. That objective cascades through service and resource layers, dynamically adjusting parameters to maximize fulfillment. The network stops reacting to alarms. It begins optimizing toward goals. That transition from reactive workflows to intent orchestration is where autonomy starts to become tangible. The KPI Inflection Point Here’s the real inflection: autonomy only matters if it moves business metrics. Reducing mean time to repair is useful. Improving spectral efficiency is important. But the moment autonomy translates into measurable cost takeout, revenue assurance, and differentiated service performance, that’s when it becomes strategic. The industry has learned that automation without economic alignment stalls. Architecture must map directly to KPIs. What’s compelling in Ericsson’s framing is the emphasis on intent-driven autonomous domains, where business objectives cascade through service and resource layers, and feasibility is continuously observed across the stack. That architecture implies something critical: autonomy must be measurable, enforceable, and economically aligned. And measurable autonomy becomes investable autonomy. Intelligence Moves Into the RAN Another shift that unfolded at MWC26 was the embedding of intelligence directly into the radio access network. As 5G Advanced evolves and AI-RAN architectures mature, AI is no longer simply analyzing network data after the fact. It is influencing resource allocation, spectral utilization, and traffic behavior in near-operational timeframes. This compresses decision cycles and increases adaptability. The RAN begins to behave less like static infrastructure and more like a living system, learning, adjusting, recalibrating. That architectural change lays early groundwork for 6G, but it is happening now, not in a distant roadmap phase. The Organizational Challenge Autonomy, however, is not purely a technical challenge. Data integrity, interoperability, and skills maturity remain the gating factors. Intent-driven systems depend on clean telemetry and cross-domain visibility. Without that foundation, autonomy risks becoming fragmented automation. The operators that will progress fastest toward higher autonomy levels will be those that treat transformation as organizational redesign, not just software deployment. Architecture and culture must evolve together. The Broader Signal What stood out most at MWC26 was not the promise of autonomous networks, but the seriousness of execution. Autonomy is being positioned less as a future aspiration and more as a stepwise economic program embedded in today’s 5G Advanced cycle. The industry is moving from “Can we automate?” to “How do we align autonomy with value creation?” That question reframes everything. Because when autonomy becomes tied to energy efficiency, operational resilience, and revenue models, it stops being a feature set. It becomes the control philosophy of the network. And that is where autonomy becomes the operating model of the modern network. #MWC26 Ericsson Ambassador | #EricssonMWC Ericsson Software Mats Karlsson Mattias Lindvall Chelsea Larson-Andrews Ronald van Loon Evan Kirstel Glen D Gilmore Peter Linder