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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.

The Connected Terminal: Rethinking Safety and Scalability with Smarter Infrastructure

The Connected Terminal: Rethinking Safety and Scalability with Smarter Infrastructure
Airport terminals are evolving into connected, intelligent environments powered by biometrics, IoT, and scalable infrastructure. These technologies are helping airports manage increasing passenger volumes, improve security, and deliver seamless experiences. From facial recognition at check-in to IoT-based baggage tracking and AR navigation, the connected terminal offers faster processing, predictive safety, and energy-efficient operations. Scalable, cloud-native systems future-proof infrastructure for demand surges and enable rapid integration of emerging tech like AI, digital twins, and virtual queuing. As global air travel rebounds, the connected terminal represents a blueprint for smarter, safer, and more sustainable airport growth.

The Digital Transformation of Airport Operations and Passenger Experiences

The Digital Transformation of Airport Operations and Passenger Experiences
Airports are no longer just transit points - they’re evolving into intelligent, connected environments powered by AI, private 5G, and digital twins. These technologies enable predictive maintenance, real-time baggage tracking, and biometric check-ins, while optimizing operational efficiency and sustainability. Private 5G ensures low-latency, high-reliability communication across airport systems, from autonomous luggage handling to AR-powered passenger navigation. Digital twins create real-time simulations of airport environments, helping operators plan, respond, and allocate resources more effectively. This digital transformation is redefining how passengers experience travel — with less stress, fewer delays, and more personalization, while equipping operators with tools to boost resilience, performance, and environmental responsibility.

T-Mobile Names Srini Gopalan CEO to Drive 5G and AI

T-Mobile Names Srini Gopalan CEO to Drive 5G and AI
T-Mobile has set a clear handover plan that pairs continuity with a sharpened focus on digital, AI, and new growth vectors. Srini Gopalan, currently Chief Operating Officer, will become CEO of T-Mobile US, succeeding Mike Sievert. Sievert moves to a newly created Vice Chairman role, remaining on the management team and Board to advise on strategy, innovation, talent, and external relations. The structure signals operational continuity and a deliberate next phase for the Un-carrier playbook across wireless, broadband, and adjacent services. Expect Gopalan to intensify investments in AI across care, sales, and network operations.

Lumen AI Network Expansion: 47M Fiber Miles by 2028

Lumen AI Network Expansion: 47M Fiber Miles by 2028
Lumen is accelerating a multi-year, multi-billion-dollar expansion of its U.S. backbone to match the explosive rise of AI-driven traffic. The company plans to add 34 million new intercity fiber miles by the end of 2028, targeting a total of 47 million intercity fiber miles. In 2025, Lumen has already added more than 2.2 million intercity fiber miles across 2,500+ route miles, with a year-end target of 16.6 million intercity fiber miles. Network capacity grew by 5.9+ Pbps year-to-date, and Lumen earmarked more than $100 million to push 400Gbps connectivity across clouds, data centers, and metros—now covering over 100,000 route miles with 400G-enabled transport.

VodafoneThree taps Ericsson and Nokia for UK 5G SA rollout

VodafoneThree taps Ericsson and Nokia for UK 5G SA rollout
Fresh off its merger, VodafoneThree has locked in eight-year vendor deals with Ericsson and Nokia to underpin a £11 billion UK network build that is front-loaded for rapid 5G Standalone coverage gains. VodafoneThree selected Ericsson and Nokia as primary technology partners for one of the largest privately funded mobile infrastructure programs in Europe, with contracts collectively valued at over £2 billion. In year one, close to three quarters of the population are targeted for access to its fastest 5G services, rising to about 90% population coverage on 5G Standalone by year three and reaching roughly 99.95% by 2034 under a regulated, fully funded build plan.

Verizon Launches 6G Innovation Forum

Verizon Launches 6G Innovation Forum
Verizon has launched a 6G Innovation Forum to accelerate research, trials, and standards alignment for the next generation of wireless. The forum convenes major RAN suppliers, including Ericsson, Samsung Electronics, and Nokia - alongside platform and device ecosystem players such as Meta and Qualcomm Technologies. The stated goal is an open, diversified, and resilient 6G ecosystem with global alignment from the outset. Verizon will back the forum with hands-on environments, starting with a dedicated 6G Lab in Los Angeles. Early priorities include testing new spectrum bands and bandwidths, and validating interoperability with mainstream standards bodies.

Malaysia 5G and AI Upskilling for Government by DNB and Ericsson

Malaysia 5G and AI Upskilling for Government by DNB and Ericsson
Digital Nasional Berhad (DNB) and Ericsson have launched a national upskilling program to train 40,000 municipal and government employees in 5G, AI, IoT and automation, signaling a shift from network build to service delivery readiness. Malaysia’s 5G footprint is expanding and the country is positioning for AI-led growth by 2030. Infrastructure alone will not unlock outcomes. Cities and agencies need people who can specify, procure, secure and operate digital services at scale. This initiative targets the execution gap by training frontline staff and policy makers on how to translate connectivity into citizen services, operational efficiency and data-driven decisions.

DFW Airport telecom outage reveals ATC network weak points

DFW Airport telecom outage reveals ATC network weak points
A multi-hour outage in the Dallas–Fort Worth airspace tied to legacy telecom services triggered cascading delays and cancellations, spotlighting urgent modernization needs for U.S. air traffic networks. On Friday afternoon, a telecommunications failure forced a ground stop across Dallas Fort Worth International (DFW) and Dallas Love Field, with ripple effects at several regional airports. The FAA attributed the incident to multiple failures in TDM-based data services delivered by a local telecom provider, compounded by redundancy gaps overseen by a prime contractor. Initial field reports tied the outage to fiber damage that simultaneously knocked out primary and backup data paths.

Private 5G Manufacturing Modernization in America

Private 5G Manufacturing Modernization in America
Manufacturers and wireless providers are shifting 5G from promising pilots to scaled, revenue‑relevant deployments across American factories. A joint report from the National Association of Manufacturers (NAM) and CTIA underscores a clear inflection point: commercial 5G, industrial AI and edge computing are maturing together. With 3GPP Release 16/17 capabilities such as URLLC, time‑sensitive networking integration, network slicing and non‑public networks, 5G is increasingly able to support time‑critical control, quality inspection and safety systems at scale. Production use cases are expanding and delivering measurable benefits. The message is consistent: companies that operationalize 5G alongside AI and automation will capture disproportionate productivity and resiliency advantages.

RANsemi wins Tidal Wave deal for industrial private 5G

RANsemi wins Tidal Wave deal for industrial private 5G
Tidal Wave Technologies has selected UK-based RANsemi to supply AI-enhanced Open RAN small cells for next-generation industrial private 5G networks across India. The companies will integrate RANsemi’s small cell platform into private 5G systems targeted at harsh, safety-critical environments. Initial focus areas include open-cast coal mines, large port terminals, and complex logistics hubs. The goal is to deliver resilient, low-latency connectivity for automation, remote operations, and worker safety. The partnership will be showcased at India Mobile Congress (IMC) 2025 with a live demonstration of integrated small cells and edge intelligence.

Argentina 2.3 GHz Spectrum for Private Networks

Argentina 2.3 GHz Spectrum for Private Networks
Argentina’s regulator ENACOM has created a new licensing framework and reserved spectrum to let enterprises run stand-alone private mobile networks across critical industries. ENACOM has designated the 2300–2400 MHz band for Private Wireless Broadband Systems, a category designed for on-premise, non-public LTE/5G networks serving operational technology and enterprise applications rather than consumer subscribers. The framework supports high-throughput, low-latency, and massive IoT use cases, enabling enhanced video, automation, and machine communications across industrial campuses and field operations; 2.3 GHz maps to widely supported 3GPP Band 40 (LTE TDD) and NR n40, giving enterprises access to a mature device and radio ecosystem.

Gartner Projects $1.5T AI Spend by 2025

Gartner: $1.5T AI Spend by 2025
Gartner’s latest outlook points to global AI spend hitting roughly $1.5 trillion in 2025 and exceeding $2 trillion in 2026, signaling a multi-year investment cycle that will reshape infrastructure, devices, and networks. This is not a short-lived hype curve; it is a capital plan. Hyperscalers are pouring money into data centers built around AI-optimized servers and accelerators, while device makers push on-device AI into smartphones and PCs at scale. For telecom and enterprise IT leaders, the message is clear: capacity, latency, and data gravity will dictate where value lands. Spending is broad-based. AI services and software are growing fast, but the heavy lift is in hardware and cloud infrastructure.

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.
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