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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.

AI layoffs surge: telecom and enterprise IT impact

Verizon layoffs: 15,000 cuts, store franchising
October’s job-cut announcements surged, with AI and cost control reshaping staffing plans across technology and adjacent sectors. Planned layoffs spiked to roughly 153,000 in October, up more than 180% from September and about 175% from a year ago, according to the latest Challenger job-cuts tally. Year-to-date announcements for 2025 have crossed 1.09 million, the highest October-through-period since the pandemic shock of 2020 and above comparable 2009 levels. The cuts reflect a pivot from growth-at-any-cost to profitability, with AI rebalancing roles and budgets across the stack. Across reasons given, cost reduction led by a wide margin, and AI adoption was the second-largest driver, underscoring both macro pressure and structural transformation.

Nokia and OneLayer Secure Utility Private 5G/LTE Networks

Nokia’s tie-up with OneLayer brings carrier-grade security and OT-aware visibility into one stack, addressing the core adoption barrier for private 5G/LTE in utilities: protecting highly distributed, mission-critical operations at scale. Together, the companies deliver a zero-trust model that spans radio to application: authenticated device identity, continuous posture assessment, role-based segmentation at the cellular (DNN/QoS flow) and IP layers, and orchestrated mitigation. Bottom line: With utilities accelerating private LTE/5G rollouts, Nokia and OneLayer are packaging the controls that regulators, insurers, and boards now expect—bringing OT-aware zero trust into the cellular domain without adding operational complexity.

Telefónica 5-Year Plan Targets €3B Savings and Growth

Telefónica 5-Year Plan Targets €3B Savings and Growth
Telefónica has launched a 2026–2030 plan to accelerate growth, simplify operations, and unlock up to €3 billion in savings while doubling down on its core markets and technology investments. Revenue is guided to a 1.5%–2.5% CAGR from 2025–2028, accelerating to 2.5%–3.5% in 2028–2030; adjusted EBITDA is guided to the same ranges across the two periods. Telefónica targets a gross impact of up to €2.3 billion in 2028 and €3 billion by 2030, driven by technology and operational excellence, process simplification, digital transformation, and monetization of legacy network assets as shutdowns progress.

LG Uplus & AWS agentic AI speeds cloud-native network deployment

LG Uplus & AWS agentic AI speeds cloud-native network deployment
LG Uplus is working with AWS on agentic AI that automates installation of cloud‑native network software, with early claims of up to 80% faster turn‑ups versus manual methods. LG Uplus and AWS partnered to develop an AI-driven approach that installs complex network software stacks without human intervention. The system uses Amazon Bedrock alongside AWS’s Strands-Agents SDK to orchestrate multiple cooperating AI agents. These agents are pre-trained on network design and implementation documents so they can execute the full workflow - provisioning cloud infrastructure, collecting device and network parameters, generating configurations, performing installation, and troubleshooting.

OECD broadband: fibre and 5G drive digital transformation

OECD broadband: fibre and 5G drive digital transformation
OECD data shows fixed and mobile broadband have shifted from build-out to scale-up, with fibre and 5G underpinning a new phase of digital infrastructure. Fixed broadband penetration across the OECD rose to 36.5 subscriptions per 100 inhabitants by end-2024, up from 32 in 2019, while the fibre share of fixed lines jumped from 28 percent to 47 percent over the same period. Gigabit-tier offers (≥1 Gbps) moved from 4 percent of subscriptions in 2019 to 19 percent in 2024, signaling both wider availability and growing appetite for very high throughput. On mobile, average monthly data consumption per subscription increased 2.5x—from 6 GB at end-2019 to 15 GB in 2024, aligned with more video, cloud, and AI-assisted applications shifting to handhelds and connected devices.

Orange to fully own MasOrange in Spain for €4.25bn

Orange to fully own MasOrange in Spain for €4.25bn
Orange has reached a non-binding agreement to acquire Lorca’s 50% stake in MasOrange for €4.25 billion in cash, aiming for sole control of Spain’s leading operator by customer base. The transaction would shift MasOrange from joint control (Orange and Lorca JVCO, owner of MásMóvil) to full ownership by Orange. Full control simplifies governance, accelerates synergy capture, and gives Orange greater flexibility in network investment, pricing, and product roadmap execution in Spain. Orange expects to sign a binding agreement before end-2025, subject to agreement on final terms. Completion is targeted for the first half of 2026, assuming standard merger-control review.

CrowdStrike & NVIDIA: Open AI Agents at the Edge

CrowdStrike & NVIDIA: Open AI Agents at the Edge
CrowdStrike and NVIDIA are aligning open models, edge inference, and agentic tooling to push real-time, autonomous cyber defense into data centers, clouds, and MEC sites where telecom and enterprise workloads actually live. By pairing CrowdStrike’s Charlotte AI AgentWorks with NVIDIA’s Nemotron open models, NeMo Data Designer, NeMo Agent Toolkit, and NIM microservices, the partners aim to shrink detection-to-response windows from minutes to milliseconds, and to do so where latency is lowest—at the edge. The companies expanded their collaboration to deliver always-on, continuously learning AI agents that defend cloud, data center, and edge environments using open and enterprise-grade NVIDIA AI components integrated with CrowdStrike’s Agentic Security Platform.

TELUS Digital Takeover: AI CX and 5G Monetization

TELUS Digital Takeover: AI CX and 5G Monetization
TELUS has taken full ownership of TELUS Digital, a move designed to consolidate AI-powered customer experience, SaaS, and automation capabilities across its telecom, health, and agriculture businesses while unlocking material cost efficiencies. TELUS acquired all remaining TELUS Digital shares for US$4.50 per share, valuing the transaction at approximately US$539 million and issuing a small portion of TELUS common shares alongside cash to complete the deal; the entity will be delisted from the TSX and NYSE and cease public reporting. Management targets roughly US$150 million in annual efficiencies from automation, business simplification, and tighter cross-selling.

Vodafone and Dell to Scale Open RAN and 5G in Europe

Vodafone and Dell to Scale Open RAN and 5G in Europe
Vodafone named Dell Technologies a strategic infrastructure provider for a five-year Open RAN buildout across Europe, signaling a move from trials to scaled, automated 5G networks. Vodafone will expand one of Europe’s largest Open RAN footprints using Dell infrastructure as part of a multi-year radio access modernization program. Dell will supply its PowerEdge XR8000 series servers, including the XR8620t and the latest XR8720t with Intel Xeon 6 SoC. Vodafone also plans to adopt the Dell Telecom Infrastructure Automation Suite (DTIAS) to provide the Infrastructure Management Service within its Open RAN architecture, designed to automate Day 0/1/2 lifecycle operations for O-Cloud infrastructure.

Samsung AI Factory Powered by NVIDIA GPUs

Samsung AI Factory Powered by NVIDIA GPUs
Samsung and NVIDIA are scaling a 25-year alliance into an AI-driven manufacturing platform that fuses memory, foundry, robotics and networks on a backbone of accelerated computing. Samsung plans to deploy more than 50,000 NVIDIA GPUs to infuse AI across the company’s manufacturing lifecycle—from chip design and lithography to equipment operations, logistics and quality control. The “AI factory” is designed as a unified, data-rich fabric where models continuously analyze and optimize processes in real time, shrinking development cycles and improving yield and uptime. The scope goes beyond semiconductors to include mobile devices and robotics, signaling a company-wide digital transformation anchored in accelerated computing.

NEC buys CSG for $2.9B to scale SaaS BSS monetization

NEC buys CSG for $2.9B to scale SaaS BSS monetization
NEC is moving to scale its cloud and SaaS business support capabilities with a $2.9 billion acquisition of CSG Systems International, positioning Netcracker at the center of the combined telecom monetization play. CSG brings a sizable recurring-revenue portfolio in digital BSS, billing, charging, and customer engagement used by communications, cable, media, and digital service providers, complementing Netcracker’s OSS/BSS, orchestration, and service automation strengths. The all-cash deal values CSG at approximately $2.9 billion on an enterprise value basis and has unanimous board approval, with closing targeted for 2026 pending CSG shareholder approval and customary antitrust and other regulatory reviews.

NVIDIA and Nokia Launch AI-RAN Platform for 6G

NVIDIA and Nokia Launch AI-RAN Platform for 6G
NVIDIA and Nokia unveiled a strategic partnership to deliver commercial AI-RAN products built on NVIDIA’s Aerial RAN Computer Pro (ARC-Pro) platform and Nokia’s RAN software portfolio, with NVIDIA committing a $1 billion equity investment in Nokia at approximately $6.01 per share, subject to customary closing conditions. The companies are targeting an AI-native RAN that runs both radio workloads and AI inference on a software-defined, accelerated platform, with a cumulative AI-RAN market opportunity that Omdia estimates will exceed $200 billion by 2030. ARC-Pro is positioned as a 6G-ready accelerated computing platform that couples connectivity, compute, and sensing, enabling upgrades from 5G-Advanced to 6G largely via software.

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.
The next evolution of Ciena’s DCOM: OOB management for diverse data center needs
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Q3 2026 Telecom AI Agents Leadership Landscape - Network Equipment Vendors 01 · 10 VENDORS Network Equipment Vendors Market positioning, deployment momentum and operator validation Q3 2026 Telecom AI Agents Leadership Landscape - OSS/BSS Vendors 02 · 9 VENDORS OSS/BSS Vendors Telecom AI, customer experience and workflow automation Q3 2026 Telecom AI Agents Leadership Landscape - System Integrators 03 · 14 COMPANIES System Integrators AI delivery, deployment maturity and operator proof
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