AI Agent Competitive
Intelligence
Intelligence Journeys
AI Use Cases for Utilities
Start New Project →
Private Broadband for Utilities
Start New Project →

Automation

Network automation replaces manual configuration and operations with software-driven processes, ranging from scripted tasks to fully autonomous, self-optimizing networks. Driven by the complexity of 5G standalone, cloud-native architectures, and multi-vendor environments, operators are pursuing higher autonomy — often framed against the TM Forum’s autonomous networks levels — to cut costs, speed service delivery, and improve reliability. Automation increasingly relies on AI and closed-loop systems that sense, decide, and act with diminishing human intervention. For operators and enterprises, the practical path runs through incremental autonomy: automating well-defined tasks before attempting end-to-end self-operation. This channel covers network automation across RAN, core, and operations — including orchestration, closed-loop control, and the AI techniques behind autonomous networks — with analysis of where automation is delivering measurable results and how operators are progressing toward higher levels of autonomy.

SK Telecom, Arm and Rebellions Launch Sovereign AI Inference Platform

SK Telecom, Arm and Rebellions Launch Sovereign AI Inference Platform
A new alliance between SK Telecom (SKT), Arm, and Rebellions targets the fast-growing AI inference market with a server platform designed for sovereign AI and telecom-grade data centers. SKT will validate a new AI server that combines Arm’s AGI CPU—its first Arm-designed data center processor, based on Neoverse CSS V3—with Rebellions’ RebelCard inference accelerator in live AI data center environments. The partners will co-develop the full software stack, from firmware up, and test telco-specific models and large-scale workloads, including SKT’s proprietary foundation model, A.X K1. Industry focus is shifting from training to inference at scale, where energy, latency, and total cost of ownership (TCO) are decisive.

Bosch and Qualcomm Advance ADAS with Snapdragon AI

Bosch and Qualcomm Advance ADAS with Snapdragon AI
Bosch and Qualcomm are extending their in-vehicle compute collaboration from digital cockpits into advanced driver-assistance systems, signaling a tighter convergence of safety, user experience, and centralized vehicle compute. Automakers are accelerating the shift from fragmented electronic control units to zonal and centralized architectures that run on fewer, more powerful processors. This deal aligns Bosch’s experience integrating automotive-grade compute with Qualcomm’s Snapdragon platforms to create scalable ADAS solutions that can be deployed across mainstream and premium vehicles. As software-defined vehicle strategies mature, consolidating safety, perception, and cockpit functions on shared compute is a pragmatic step to reduce cost and complexity.

The ‘Agentic Telco’ Crystallizes at MWC 2026 as Operators Build Their Own AI Models: AI & Automation Insights, March 2026

The 'Agentic Telco' Crystallizes at MWC 2026 as Operators Build Their Own AI Models: AI & Automation Insights, March 2026
March 2026's roundup: GSMA, Google Cloud, Huawei, and AWS all converge on an "agentic telco" framing at MWC Barcelona, SoftBank and SK Telecom build proprietary telecom-specific AI models and infrastructure, and physical AI reaches airports, drones, and network edge sites — with AI-RAN and connectivity infrastructure covered separately in our companion Advanced Connectivity Insights, March 2026.

AT&T, Cisco and NVIDIA Deliver Network-Driven Edge AI

AT&T, Cisco and NVIDIA Deliver Network-Driven Edge AI
AT&T’s new collaboration with Cisco and NVIDIA signals a decisive shift from cloud-centric AI to network-driven edge intelligence for enterprise operations. Enterprises want real-time decisioning without shipping sensitive data to distant clouds, and operators need a scalable way to deliver it. By combining AT&T’s dedicated IoT core with Cisco’s mobility services platform and NVIDIA-powered AI infrastructure, the trio is packaging deterministic connectivity, near-device inference, and policy enforcement into a single, operator-grade platform. The promise: lower latency, tighter data control, and a path to production for AI at industrial scale.

Trusted AI enterprise voice from Orange Business

Trusted AI enterprise voice from Orange Business
Orange Business is putting authenticated, AI-augmented voice back in the critical path of CX and employee workflows as enterprises confront fraud, fatigue, and falling answer rates. As digital touchpoints proliferate, the phone channel faces a crisis of confidence: spoofed identities, impersonation scams, and AI-generated content have eroded user trust and pushed customers to ignore legitimate calls. Despite surging chat and self-service volumes, voice remains the preferred medium for resolving complex or high-stakes problems, and the most-used channel for many service agents. The new capabilities combine authenticated caller identity, deepfake detection, generative AI in the contact center, and agentic telephony that can autonomously manage call flows.

AT&T $250B Investment in U.S. 5G and Fiber

AT&T $250B Investment in U.S. 5G and Fiber
AT&T’s five-year, $250 billion U.S. network commitment sets the tone for the next phase of fiber, 5G, and satellite convergence as traffic, AI workloads, and resilience requirements climb sharply. The 2026–2030 window aligns with the industry’s transition into 5G-Advanced (3GPP Release 18/19), the scaling of edge AI, and increased cloud traffic between homes, enterprises, and hyperscalers. Data growth is no longer linear, and the cost of downtime is rising. Large, front-loaded builds in fiber and 5G Radio Access Network (RAN), paired with new satellite overlays, are how national carriers will chase coverage, performance, and reliability targets simultaneously.

Cargill Scales Private 5G with NTT DATA

Cargill Scales Private 5G with NTT DATA
NTT DATA’s private 5G rollout across 50 Cargill facilities signals that industrial connectivity is moving from pilot projects to standardized, multi-site execution. NTT DATA has deployed private 5G at Cargill manufacturing and processing locations worldwide—mostly in the United States with live sites in Europe—enabling a connected workforce, robotics, and edge AI across plants that are often too large and complex for conventional Wi‑Fi or wired networks to cover reliably. Manufacturers are consolidating on common digital platforms and need predictable, low-latency wireless for operational data, mobile human–machine interfaces, and autonomous systems; private 5G—built on 3GPP standards with SIM-based security and policy-based QoS—offers deterministic performance at scale where legacy networks struggle.

TELUS Digital AI in Telecom: Scaled 20+ GenAI use cases on Fuel iX

TELUS Digital AI in Telecom: Scaled 20+ GenAI use cases on Fuel iX
TELUS Digital is using Mobile World Congress 2026 to move the AI-in-telecom conversation from pilots to proven production at scale. TELUS Digital reports processing more than two trillion tokens in 2025 through its Fuel iX generative AI platform for TELUS operations and customers. The portfolio spans AI for customer experience, application safety, and network modernization—built and battle-tested within TELUS before client rollout. The Network Design Services practice applies AI to planning and optimization while charting a path from legacy network stacks to cloud-native, automated environments.

SK Telecom ATHENA: AI-Native 6G Blueprint

SK Telecom ATHENA: AI-Native 6G Blueprint
SK Telecom introduced ATHENA—an architecture grounded in AI-native operations, Zero Trust security, hyper-connectivity, openness, and cloud-native design—to guide mid- to long-term evolution across RAN, core, transport, and network data platforms. The operator positions “AI for network” and “network for AI” as dual tracks: the former embeds AI into decision loops for autonomous optimization, while the latter tunes the network fabric to serve AI workloads efficiently. SK Telecom will showcase related technologies at MWC Barcelona 2026, including AI agents for networks, AI-RAN for combined connectivity and compute, device-side AI for antenna tuning, and integrated sensing-and-communications.

Deutsche Telekom telco cloud cuts 5G core energy by up to 65%

Deutsche Telekom telco cloud cuts 5G core energy by up to 65%
Deutsche Telekom’s early live results showing up to 65% energy savings in its 5G core spotlight a pragmatic path to cut opex and carbon as traffic surges and standalone 5G scales. Operators have wrung out much of the easy efficiency from hardware refreshes; the next gains come from software-driven, demand-aware control. DT is applying that logic to the core, shifting components to run only when needed rather than idling at full power. The results are enabled by DT’s “Horizontal Telco Cloud,” a unified, standards-based platform that replaces fragmented stacks with one common layer for core services. Initial live-network tests have been completed, with broader rollout planned and further detail expected at MWC Barcelona 2026.

Nokia and AWS Launch Agentic AI for 5G-Advanced Network Slicing

Nokia and AWS Launch Agentic AI for 5G-Advanced Network Slicing
Nokia and Amazon Web Services (AWS) are bringing agentic AI to 5G-Advanced network slicing, moving closed‑loop, intent-based services from PowerPoint to live pilots with du and Orange. The partners unveiled an agentic AI-powered slicing solution that fuses Nokia’s RAN-to-core slicing, AirScale radio, and MantaRay SMO with AWS’s Bedrock AI platform and EKS Hybrid Nodes to turn external context—events, traffic, maps, weather—and live network KPIs into real-time policy decisions. The result is adaptive, premium slices provisioned when and where they’re needed, without manual reconfiguration.

Frequently Asked Questions

What’s the difference between automation and AI in a telecom context?
Automation, in its traditional form, generally follows pre-defined rules and scripted logic: a specific condition triggers a specific, predetermined response, with no real interpretation or judgment involved beyond what was explicitly programmed in advance. AI adds the capacity to learn from data, recognize patterns that weren’t explicitly anticipated, and make more nuanced decisions about what action makes sense in a given situation, even one the system hasn’t seen in exactly that form before. In practice, most modern telecom systems combine the two: AI analyzes a situation and decides what should happen, while automation infrastructure actually executes that decision reliably and consistently across the network, a combination often described as the foundation for agentic AI.
What is ‘zero-touch’ network operation, and how close is the industry to achieving it?
Zero-touch network operation describes the long-term industry goal of running networks with minimal direct human intervention, where the network itself handles configuration, fault recovery, and optimization automatically and continuously. It’s a meaningful aspiration rather than a fully achieved reality; the industry is progressing toward it in stages, with specific functions, like automated fault detection or dynamic capacity adjustment, achieving meaningful levels of automation well before the broader vision of an entirely self-managing network becomes reality. Standards bodies including ETSI have working groups specifically dedicated to defining the requirements for zero-touch network and service management, reflecting that this remains an active area of ongoing standardization rather than settled, widely deployed technology.
Why is automation considered essential for managing modern 5G networks specifically?
5G networks are substantially more complex than earlier generations for several compounding reasons: they rely heavily on virtualized network functions running across cloud infrastructure rather than fixed dedicated hardware, they support network slicing, meaning managing multiple distinct virtual networks with different performance guarantees simultaneously, and they often combine equipment and software from multiple different vendors rather than one integrated supplier. Traffic patterns also shift constantly and unpredictably as device density and application types continue to grow. Attempting to manually manage this level of complexity at the scale of a national or global network simply isn’t realistic, making automation effectively a practical requirement for operating a modern 5G network reliably at all.
How does automation relate to network orchestration?
Automation and orchestration are closely related but distinct concepts. Automation generally refers to executing a specific task without manual intervention, like automatically restarting a failed process or adjusting a configuration parameter. Orchestration refers to the broader coordination of multiple automated tasks and virtualized network components across their full lifecycle, deciding where different network functions should run, how they should scale, and how they interact with each other to deliver a complete service. In practice, orchestration systems often rely on underlying automation capabilities to actually carry out the individual tasks they coordinate, providing higher-level coordination while automation provides the lower-level mechanism for reliably executing decisions.
What are the risks of relying heavily on automated network systems?
Heavy reliance on automated systems introduces specific risks alongside its clear efficiency benefits. If an automated system makes an incorrect decision, that error can potentially propagate quickly and broadly across the network before a human notices and intervenes, compared to a manual process where mistakes tend to be more contained. There’s also a risk of reduced visibility and understanding among human staff over time, since heavily automated systems can create a gap between what the network is actually doing and what engineers fully understand about why, particularly as AI-driven decision-making becomes more involved. Operators generally manage these risks by maintaining careful guardrails for automated actions, expanding autonomous scope gradually as confidence grows.
How has automation changed the day-to-day role of network engineers?
Automation has shifted network engineers’ day-to-day work away from repetitive, manual configuration and troubleshooting tasks and toward higher-level responsibilities like designing automation policies, overseeing AI-driven systems, and handling more complex or novel problems that automated systems aren’t yet equipped to resolve independently. Rather than manually configuring each new service or personally diagnosing every fault, engineers increasingly spend time defining the rules, guardrails, and escalation criteria that govern how automated systems behave, then stepping in directly for situations that fall outside those parameters. This represents a meaningful skills shift, with growing demand for engineers comfortable working with automation platforms and AI systems alongside traditional networking expertise.
What’s ‘closed-loop automation,’ and why does it matter?
Closed-loop automation refers to systems that don’t just execute an automated action once, but continuously monitor the result of that action, compare it against the intended outcome, and adjust automatically if the result doesn’t match expectations, creating a self-correcting cycle without requiring a human to manually verify and re-trigger each step. A closed-loop system managing network capacity, for example, might automatically increase resources in response to rising traffic, then continuously monitor whether that adjustment actually resolved the congestion, and make further automatic adjustments if it didn’t. This concept is considered a meaningful step toward the zero-touch network vision, since it moves automation beyond simple one-off actions toward genuinely self-managing behavior based on real-world feedback.
AI Agent for Telecom Competitive Intelligence

Partner Hubs

Download content, access intelligence tools, and hear from executives.

Partner Events

  • Network X Vienna 2026
Scroll to Top