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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 Rise of Edge Computing

The Rise of Edge Computing
Edge computing is a rapidly evolving technology that processes data near its source, enabling faster decision-making, reduced latency, and improved data security. This new technology, coupled with 5G, is unlocking new use cases for IoT applications across various industries, including industrial automation, autonomous vehicles, smart cities, healthcare, retail, and AR/VR. However, businesses must address the challenges and considerations related to infrastructure, security, integration, and talent to implement and benefit from edge computing solutions successfully.

ChatGPT | How AI-powered platforms like ChatGPT are enabling businesses to streamline operations and improve customer experiences

ChatGPT | How AI-powered platforms like ChatGPT are enabling businesses to streamline operations and improve customer experiences
AI-powered platforms like ChatGPT are enabling businesses to streamline operations and improve customer experiences by automating routine tasks, optimizing processes, and extracting valuable insights from data. Applications of ChatGPT include customer support, content generation, personalized marketing, process automation, and natural language processing. Benefits of implementing AI-powered platforms encompass enhanced productivity, cost savings, improved customer experiences, scalability, and data-driven decision-making. However, challenges and considerations in implementing AI-powered platforms involve data privacy and security, integration with existing systems, skill requirements, ethical considerations, and maintenance and continuous improvement. By addressing these challenges, businesses can harness the power of AI-powered automation to drive innovation and enhance customer experiences.

EnterpriseWeb Premieres the First Telco-grade Demo of Generative AI for Network Service Orchestration

EnterpriseWeb Premieres the First Telco-grade Demo of Generative AI for Network Service Orchestration
EnterpriseWeb announced that it will premiere the first telco-grade demonstration of generative AI for network service orchestration today at Informa’s Big 5G event in Austin, Texas. The company, which ran the telecom industry’s first Network Function Virtualization proof-of-concept in 2013 and is known for its advanced automation capabilities, partnered with KX to enable next generation AI-powered Telecom operations.

Ericsson and Telia Launch First Enterprise 5G Private Network in Baltics

Ericsson and Telia collaborate to establish the first-ever enterprise 5G private network in the Baltic region, a move set to spur the area's digital transformation. This development in Estonia will enhance business operations, drive innovation, and streamline the introduction of new products. The partnership aims to showcase the vast potential and benefits of 5G technology in improving business operations and accelerating the adoption of Industry 4.0.

Nokia unveils first CE-certified 5G automated drone-in-a-box solution for secure, reliable public sector and industrial operations

5G drone coverage and challenges - TeckNexus
Nokia first to offer CE-certified turnkey automated drone solution, connected over 4G/LTE and 5G, including drones with dual gimbal camera, docking station, and edge data processing.
Use of Nokia Drone Networks docking station enables 24/7 fully remote drone operations and charging.
Open API framework allows integration of third-party applications for extensive customization of the solution to expand use cases and capabilities.

NTT and SES Unveil Satellite-Powered Edge and Private 5G Solutions for Enterprises

NTT and SES have announced a multi-year partnership to utilize SES satellites for delivering NTT’s Edge as a Service to enterprise customers. This collaboration merges NTT’s networking and enterprise-managed services expertise with SES’s satellite capabilities to offer reliable connectivity for enterprises experiencing surges in connectivity demand or operating in areas beyond fixed terrestrial networks.

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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