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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.

FCC approves AT&T–UScellular 5G spectrum deal

FCC approves AT&T–UScellular 5G spectrum deal
The FCC has approved AT&T’s agreement to acquire a portfolio of UScellular wireless spectrum licenses for $1.02 billion, advancing AT&T’s mid-band capacity strategy and reshaping competitive dynamics in U.S. 5G markets. The licenses span select UScellular markets, bolstering AT&T’s holdings in areas where UScellular has long operated, including rural and midwestern regions. With FCC consent in hand, the parties can proceed to closing market by market, subject to routine administrative steps and any local obligations. Mid-band spectrum remains the sweet spot for balanced capacity and coverage. This positions AT&T to better support RedCap devices, uplink-sensitive applications, and the early wave of 5G-Advanced features.

Comcast Versant Spinoff: Diversifying Beyond Pay TV

Comcast Versant Spinoff: Diversifying Beyond Pay TV
Versant’s lineup spans USA Network, CNBC, MS NOW (formerly MSNBC), Oxygen, E!, SYFY and Golf Channel, plus Fandango, Rotten Tomatoes, GolfNow, GolfPass and SportsEngine. Management argues the reach of up to ~65 million households and a 62% live programming mix gives it durable leverage in news and sports while it builds digital and direct-to-consumer (DTC) revenue. For MVPDs, vMVPDs and broadband providers, this is a new negotiating counterparty with incentives to protect affiliate value while expanding FAST, OTA and DTC channels that can bypass bundles. Versant stock will trade on Nasdaq as VSNT starting January 5, 2026.

ZTE & China Unicom deploys Private 5G ISAC for Airport Security

ZTE & China Unicom deploys Private 5G ISAC for Airport Security
ZTE, China Unicom Liaoning and Dalian Changhai Airport have put a 5G-Advanced private network with integrated sensing and communications into live service to address low-altitude security at an island test flight field. The partners deployed a private 5G-Advanced architecture that fuses high-throughput connectivity with precision sensing on the same infrastructure, tailored for a maritime, island airport where traditional patrols and single-sensor radars leave blind spots for “low, slow, small” targets such as drones and bird flocks. According to the partners, the network is running 24/7 at the test flight field and has lifted low-altitude detection accuracy near 98%. By consolidating connectivity and sensing on one footprint, the deployment claims about 30% less space and roughly 25% lower capital intensity versus separate radios and radars.

Private 5G for Croatia’s Smart Airports | Hrvatski Telekom

Private 5G for Croatia’s Smart Airports | Hrvatski Telekom
Hrvatski Telekom will deploy dedicated private 5G networks at Zagreb, Zadar, and Pula airports under a €5.6 million “NextGen 5G Airports” program co-financed by the European Commission’s CEF Digital initiative. The project was selected in a competitive CEF Digital call focused on 5G and edge for smart communities, with €3.09 million in EU grant funding and the remainder financed by Hrvatski Telekom and partners. The program targets operational efficiency, safety, and a better passenger experience through dedicated, configurable, and SLA-backed wireless infrastructure. Edge computing on or near the airport premises will enable low-latency processing for video, safety systems, and time-sensitive control.

Deutsche Telekom & Schwarz plan EU-backed AI data centre in Germany

Deutsche Telekom & Schwarz plan EU-backed AI data centre in Germany
Two German heavyweights are in advanced discussions to co-build large-scale AI data centre capacity in Germany, a move that would tap European Union funding and accelerate sovereign AI infrastructure. Deutsche Telekom and the Schwarz Group are exploring a joint bid to develop EU-supported “AI Gigafactory” facilities, data centres purpose-built for high-density AI training and inference. According to multiple reports, the talks are well progressed but not yet final. Infrastructure investor Brookfield has been flagged as a potential financial partner alongside EU capital, adding balance-sheet depth and construction expertise to the consortium.

Nokia invests $4B in AI-ready US networks

Nokia Shifts Focus to AI and Network Infrastructure
Nokia is making a multi‑year, $4 billion push to expand US R&D and manufacturing as it pivots to AI‑native networks under CEO Justin Hotard. The company will invest roughly $3.5 billion in US‑based R&D spanning networking technologies, defense applications, automation, quantum‑safe networking, and semiconductor development. A further $500 million targets manufacturing and R&D expansion in Texas, New Jersey, and Pennsylvania, strengthening domestic supply chains for critical telecom gear. The plan follows Nokia’s strategy revamp and creation of a Mobile Infrastructure unit to advance an AI‑native network portfolio across RAN, transport, IP, and cloud.

Ericsson Mobility Report: differentiated 5G connectivity

Ericsson Mobility Report: differentiated 5G connectivity
Ericsson’s latest Mobility Report points to a clear shift: operators are turning 5G capabilities into differentiated, SLA-backed services rather than just selling more data at higher speeds. After years of building coverage and capacity, 5G networks are mature enough to commercialize features like guaranteed latency, uplink boosts, and application-aware prioritization. The catalysts are in place: more 5G Standalone (SA) cores, rising traffic from video creation and immersive apps, and enterprise demand for predictable performance across sites and clouds. The net result is momentum behind premium, differentiated connectivity that can be priced, assured, and exposed to partners.

Deutsche Telekom Industrial AI Cloud wins SOOFI contract

Deutsche Telekom Industrial AI Cloud wins SOOFI contract
Deutsche Telekom’s T-Systems has secured a multi-million-euro contract from Leibniz University Hannover to power SOOFI, a flagship initiative to build a 100-billion-parameter, European-operated large language model. The SOOFI (Sovereign Open Source Foundation Models) project will train a next-generation, open-source LLM focused on European languages and industrial requirements, replacing the current 7-billion-parameter Teuken7B with a model two orders of magnitude larger. T-Systems will host and operate the training environment in its new Industrial AI Cloud—an NVIDIA-powered facility that DT and NVIDIA unveiled as part of a €1 billion partnership.

Verizon layoffs: 13,000+ job cuts and AI-first pivot

Verizon layoffs: 13,000+ job cuts and AI-first pivot
Verizon will cut more than 13,000 roles as part of a broader restructuring aimed at simplifying operations and resetting its cost base for the next phase of growth. The reduction represents roughly 13% of Verizon’s reported ~100,000 full-time workforce and about one-fifth of its non-union management ranks, according to figures shared alongside the announcement. In parallel, Verizon plans to curb outsourcing and other external labor spending, convert 179 company-owned retail stores to franchise operations, and shutter one store. The restructuring reflects subscriber headwinds and a need to rebalance costs as 5G investment priorities shift from buildout to monetization and automation.

Palo Alto Networks to Acquire Chronosphere for AI Observability

Palo Alto Networks to Acquire Chronosphere for AI Observability
Palo Alto Networks is buying Chronosphere to fuse cost-efficient, large-scale observability with AI-driven automation for modern cloud and AI data centers. Palo Alto Networks agreed to acquire Chronosphere for approximately $3.35 billion in a mix of cash and replacement equity, with closing expected in the second half of PANW’s fiscal 2026 (ending July 31). Chronosphere brings a next-generation observability architecture and telemetry pipeline built for scale and cost control. Together, they aim to turn observability from passive dashboards into autonomous, governed remediation that blends performance and security insights.

Nokia AI Strategy for 6G and AI-Native Networks

Nokia AI Strategy for 6G and AI-Native Networks
Nokia is restructuring to monetize the AI supercycle across fixed and mobile networks while tightening focus on profitable growth. The company’s new strategy concentrates on: accelerating in AI and cloud; leading the next era of mobile with AI-native networks and 6G; co-innovating with customers and partners; concentrating capital where it can differentiate; and unlocking sustainable, consistent returns. Nokia will move from four primary segments to two, with changes effective 1 January 2026. The company is targeting comparable operating profit of €2.7 billion to €3.2 billion by 2028.

Cisco Acquires NeuralFabric for Enterprise AI

Cisco Acquires NeuralFabric for Enterprise AI
Cisco’s intent to acquire Seattle-based NeuralFabric signals a decisive shift toward practical, domain-specific AI that meets real-world constraints around data, compliance, and infrastructure. Cisco plans to acquire NeuralFabric, an enterprise AI platform focused on building small language models (SLMs) from proprietary data with deployment across SaaS and on-premises environments. By focusing on SLMs trained on enterprise data and deployable in hybrid environments, Cisco aims to shorten time-to-value while keeping control where it belongs—inside the business. They reduce inference cost, improve latency, and can be deployed on-premises or at the edge—critical for sectors like telecom, financial services, and healthcare.

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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