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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.

Boldyn & O2 Boost 5G at AO Arena Manchester via Neutral Host DAS

Boldyn & O2 Boost 5G at AO Arena Manchester via Neutral Host DAS
Boldyn Networks and O2 have upgraded AO Arena Manchester with a cutting-edge neutral host 5G DAS. Designed to support over a million annual attendees, the shared infrastructure enhances livestreaming, digital ticketing, and real-time services. The rollout delivers seamless mobile performance for fans, vendors, and staff, setting a new standard for large venue connectivity in the UK.

Cable Industry Economic Impact 2024: $568.7B and 1.3M Jobs

Cable Industry Economic Impact 2024: $568.7B and 1.3M Jobs
In 2024, the U.S. cable sector generated $568.7 billion in total economic output and supported 1.3 million jobs across the country. This footprint spans broadband networks, video programming, construction, manufacturing, and a broad vendor ecosystem. It underscores why cable remains a central pillar of America’s connectivity and media economy even as consumption shifts to IP and streaming. Cable broadband providers—led by Comcast, Charter Communications (Spectrum), Cox, Altice USA (Optimum), Mediacom, Cable One (Sparklight), and WOW!—accounted for $366 billion in total economic impact and nearly 888,000 jobs.

Telefónica Sustainability: €77B Invested in Sustainable Infrastructure

Telefónica Sustainability: €77B Invested in Sustainable Infrastructure
Telefónica reports €77 billion invested over ten years to expand sustainable, resilient connectivity, with SDG 9 (industry, innovation and infrastructure) as the strategic anchor. The operator now serves nearly 350 million accesses, has passed 81.4 million premises with FTTH, and runs one of the largest ultra-broadband footprints globally, second in scale only to China. Spain is Telefónica’s showcase for fiber-led modernization. Dense FTTH has enabled a managed copper switch-off, which simplifies operations, cuts energy use, and improves service quality. The operator targets net zero by 2040 - ten years ahead of many international timelines—and reports a 52% reduction in CO2 emissions across the value chain from 2015 to 2024.

Ensuring Trusted Network Data in Modern Telecom Operations

Ensuring Trusted Network Data in Modern Telecom Operations
Telecom operators face rising costs and risks when network data can’t be trusted. From ghost circuits and delayed service activations to compliance issues and poor investment decisions, the impact is felt across every team. This article looks at why accurate data matters more than ever and how operators can build lasting trust with their network records.

HUMAIN Horizon Pro: Agentic AI PC on Snapdragon X Elite

HUMAIN Horizon Pro: Agentic AI PC on Snapdragon X Elite
HUMAIN, a Saudi PIF-backed AI company, introduced Horizon Pro, an “agentic AI” PC built on Qualcomm’s Snapdragon X Elite, positioning it as a new class of Windows laptop where on-device AI drives workflows, decisions, and user interaction. At Qualcomm’s Snapdragon Summit in Maui, HUMAIN CEO Tareq Amin unveiled the Horizon Pro PC and the company’s agentic software layer, Humain One, which runs on top of Windows 11 and is slated for formal launch at the Future Investment Initiative in Riyadh.

Wayve Self-Driving AI Trials in Tokyo with Nissan

Wayve Self-Driving AI Trials in Tokyo with Nissan
Wayve’s end-to-end driving AI is now running in Nissan Ariya electric vehicles in Tokyo, marking a pragmatic step toward consumer deployment in 2027. The test vehicles combine a camera-first approach with radar and a lidar unit for redundancy, aligning with Japan’s dense urban environment and complex traffic patterns. The initial commercial target is “eyes on, hands off” Level 2 driver assistance, with drivers remaining responsible and ready to take over. Nvidia has signed a letter of intent for a potential $500 million investment in Wayve’s next funding round, reinforcing the compute-intensive nature of the program.

How Connectivity Will Create a More Seamless and Efficient Experience in Aviation: Smarter Journeys, Lower Costs

How Connectivity Will Create a More Seamless and Efficient Experience in Aviation: Smarter Journeys, Lower Costs
Connectivity is transforming aviation from the ground up. Airports are deploying private 5G, Wi-Fi 6, edge computing, and IoT to deliver two major outcomes: smoother passenger experiences and lower operating costs. Travelers enjoy real-time updates, biometric check-in, and AR wayfinding — while operators benefit from predictive maintenance, smarter gate usage, and energy optimization. This dual-value framework positions connectivity as more than infrastructure, it’s a strategic differentiator that enhances revenue, reduces OPEX, and elevates the brand.

Stakeholder Collaboration in Aviation: Why Connectivity Must Extend Across the Ecosystem

Stakeholder Collaboration in Aviation: Why Connectivity Must Extend Across the Ecosystem
Aviation is no longer a siloed industry - it’s a globally connected ecosystem where airports, airlines, regulators, telecom operators, and tech vendors must work in sync. As digital transformation accelerates, connectivity becomes a critical layer for collaboration, enabling real-time decision-making, safety, operational alignment, and a seamless passenger experience. From private 5G and edge computing to biometric boarding and IoT, the aviation industry must co-invest, co-develop, and co-govern digital infrastructure. Case studies from Heathrow, Changi, and DFW show that stakeholder alignment leads to measurable gains in efficiency, innovation, and trust. Connectivity is the enabler, but collaboration is what makes it scalable and sustainable.

Digital Transformation in Airport Ground Operations: IoT, Automation & Connectivity

Digital Transformation in Airport Ground Operations: IoT, Automation & Connectivity
Airport ground operations — from baggage handling and fueling to aircraft turnaround - are undergoing rapid digital transformation. Powered by IoT, automation, private 5G, and edge computing, airside workflows are becoming more predictive, efficient, and sustainable. Sensors track assets, optimize vehicle dispatch, and enhance worker safety. Autonomous tugs, computer vision, and AI-driven maintenance cut delays and reduce manual errors. Private networks and edge computing provide the real-time connectivity needed for mission-critical applications. Leading airports like Schiphol, Changi, and DFW are already adopting these technologies, proving that digital transformation on the ground isn't just possible, it's essential for next-gen airport performance.

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.

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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