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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.

Connected Industries: How Private 5G and AI Are Powering Smart Manufacturing

Connected Industries: How Private 5G and AI Are Powering Smart Manufacturing
The future of manufacturing is intelligent, autonomous, and sustainable. Powered by private 5G networks, AI, and digital twins, smart factories are revolutionizing how goods are produced and maintained. From predictive maintenance to immersive virtual twins and AI-optimized energy systems, smart manufacturing is unlocking new levels of efficiency and innovation across industries—from ports and shipyards to agriculture and healthcare.

Connected Industries: The Future of Smart Mobility with 5G, AI, and Intelligent Transport

Connected Industries: The Future of Smart Mobility with 5G, AI, and Intelligent Transport
Smart mobility is reshaping how the world moves, powered by 5G, AI, and edge computing. From autonomous vehicles and real-time logistics to AI-driven drones and connected public transport, intelligent transportation systems are redefining urban mobility, logistics, and industrial automation. As global investment and collaboration grow, the transportation industry is transforming into a $11.1 trillion smart ecosystem focused on sustainability, efficiency, and connectivity.

AI in Indian Telecom: Automation, 5G, and Future Growth

AI in Indian Telecom: Automation, 5G, and Future Growth
India’s telecom sector is rapidly evolving with AI and automation enhancing network operations, customer service, and 5G deployment. With over 125 million 5G users and major investments from companies like Reliance Jio and Bharti Airtel, AI technologies are proving essential for scalability and efficiency. Despite challenges like infrastructure integration and talent gaps, India’s growing AI ecosystem and government support are driving the future of smart telecom solutions.

Private 5G to Power Industry 4.0 and Automation in India

Private Network Readiness Assessment for the Utilities Sector
Private 5G is poised to transform India’s telecom and industrial landscape, enabling Industry 4.0 through automation, AI, and ultra-reliable connectivity. At the 5GCongress, TRAI chief AK Lahoti and DoT’s Manish Sinha projected ₹4 lakh crore revenue for the telecom sector by 2026, highlighting private 5G’s critical role in enhancing machine-to-machine communication, operational efficiency, and real-time data exchange. Government support through spectrum allocation and Make in India initiatives further boosts industry momentum.

AMD and Rapt AI Partner to Optimize GPU Utilization for AI Workloads

Observe.AI Launches VoiceAI for Call Center Automation
AMD and Rapt AI are partnering to improve AI workload efficiency across AMD Instinct GPUs, including MI300X and MI350. By integrating Rapt AI's intelligent workload automation tools, the collaboration aims to optimize GPU performance, reduce costs, and streamline AI training and inference deployment. This partnership positions AMD as a stronger competitor to Nvidia in the high-performance AI GPU market while offering businesses better scalability and resource utilization.

5G Open RAN in US Defense: From Prototype to Full Deployment

5G Open RAN in US Defense: From Prototype to Full Deployment
The US Department of Defense has transitioned 5G Open RAN from prototype to full operational deployment, enhancing military logistics, automation, and cybersecurity. With industry partners like JMA Wireless and Federated Wireless, the DoD is leveraging 5G for mission-critical operations. This article explores how 5G Open RAN improves operational resilience, workforce efficiency, and future military applications, including spectrum management and AI-driven network optimization.

AI-Powered Service Assurance – Leveraging DPUs, GPUs, and GenAI for Next-Gen Networks

AI-Powered Service Assurance – Leveraging DPUs, GPUs, and GenAI for Next-Gen Networks
Telecom networks are facing unprecedented complexity with 5G, IoT, and cloud services. Traditional service assurance methods are becoming obsolete, making AI-driven, real-time analytics essential for competitive advantage. This independent industry whitepaper explores how DPUs, GPUs, and Generative AI (GenAI) are enabling predictive automation, reducing operational costs, and improving service quality. Discover key insights, real-world case studies, and strategic actions for telecom leaders. Download the Full Report Now to stay ahead in AI-powered service assurance.

MWC 2025 Keynote 12: Future of Work and Economic Growth

MWC 2025 Keynote 12: Future of Work and Economic Growth
At MWC 2025 Keynote 12: Future of Work and Economic Growth, industry leaders explored how AI, talent shortages, and startup growth are reshaping global markets. From Europe's role in applied AI to the importance of scaling startups internationally, the discussions offered crucial insights for entrepreneurs, investors, and tech professionals. Discover key takeaways on AI-driven industries, workforce transformation, and economic innovation. Featuring Euan Blair (Multiverse), Saadia Zahidi (WEF), Yoram Wijngaarde (Dealroom.co), Renate Nikolay (European Commission), and Jordi Romero (Factorial), this session explores workforce transformation, AI’s role in labor markets, and strategies to boost Europe’s innovation and competitiveness.

MWC 2025 Keynote 5: The AI Frontier: Transformative Visions & Societal Impact

MWC 2025 Keynote 5: The AI Frontier: Transformative Visions & Societal Impact
AI is reshaping the world—transforming business, governance, and human interactions while raising critical questions about ethics, security, and digital equity. At MWC 2025, global AI pioneers, including Ray Kurzweil, Vilas Dhar, and industry leaders, will discuss AI’s role in automation, human augmentation, and the future of work. Join this thought-provoking keynote to explore how we can harness AI responsibly for an inclusive, innovative, and sustainable future.

MWC 2025 Keynote 1: The Gateway to a New Future – Open APIs & 5G Evolution

MWC 2025 Keynote 1: The Gateway to a New Future - Open APIs & 5G Evolution
As the digital world evolves, connectivity is reshaping industries through API-driven telecom solutions. The GSMA Open Gateway initiative is pioneering this shift by enabling seamless integration between telecom operators, developers, and enterprises. With 5G expected to power 1.2 billion connections by 2025, Open Gateway’s standardized APIs are fostering innovation in smart cities, fintech, logistics, and more. Join us at MWC25 Barcelona to explore the future of telecom connectivity.

Can Smart Network Management Reduce the Carbon Footprint in Telecom?

Sustainability in telecom is no longer optional—it’s essential. With rising energy costs, regulatory pressure, and consumer demand for greener operations, telecom operators must act now. Smart network management powered by AI, automation, and lifecycle management can significantly cut energy waste, reduce e-waste, and enhance operational efficiency. Discover how VC4’s Service2Create (S2C) platform is revolutionizing telecom sustainability through real-time inventory tracking, AI-driven energy optimization, and automated network reconciliation—helping operators go green while improving their bottom line. Read more to explore how technology is shaping a sustainable telecom future!

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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