- Partner Event
- July 9, 2025
- Hema Kadia
Start: Oct 14, 2025
End: Oct 15, 2025
Venue: Fontainebleau Las Vegas
Location: Las Vegas
End: Oct 15, 2025
Venue: Fontainebleau Las Vegas
Location: Las Vegas
AI Agent Competitive
Intelligence
Intelligence
Private Network
Intelligence
Intelligence
Intelligence Tools
By Tool Type
By Industry
Access Type
Monthly Trackers
Resources
Technology Hubs
Partner Hubs
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.
- Tech News & Insight
- July 9, 2025
- Hema Kadia
India’s internet subscriber base hit 969.10 million in FY25, with broadband users rising 2.17% and narrowband shrinking. TRAI data shows wireless ARPU up 16.89%, strong wireline growth, rural connectivity gains, and Jio-Airtel dominance — while DTH shrinks and OTT rises.
- Tech News & Insight
- July 8, 2025
- Covalense Digital
Covalense Digital’s new Csmart iPaaS–an AI-enabled API integration platform designed for telecom and enterprise ecosystems.
- Tech News & Insight
- July 5, 2025
- Hema Kadia
Virgin Media O2’s multi-year transformation redefines UK telecoms with digitalization, AI, and customer-first thinking. From legacy network upgrades and automation to AI tools like Daisy and Digital Twins, the operator’s strategy focuses on trust, reliability, and sustainable growth.
- Tech News & Insight
- July 5, 2025
- Hema Kadia
BT’s global fabric redefines telecoms by collapsing legacy silos into a fully digital, AI-ready network. With virtualization, cloud agility, and NaaS, BT supports critical infrastructure at global scale while tackling data sovereignty, resilience, and modern skills challenges.
- Tech News & Insight
- July 1, 2025
- Heather Gore
Panzura Symphony's latest update introduces a first-of-its-kind solution to combat permission sprawl and enhance AI readiness. Unlike other security tools, Symphony proactively identifies, monitors, and automatically resolves permission inheritance problems, preventing exploitation by malicious actors and eliminating the need for manual intervention. This update also allows users to apply custom metadata to files, facilitating advanced policy automation and populating AI pipelines based on file attributes without needing content scanning.
- Tech News & Insight
- June 27, 2025
- Hema Kadia
5G Advanced and AI are reshaping utility private networks into hyper-intelligent, resilient grids. Learn how edge AI, programmable networks, digital twins, and human-in-the-loop automation will enable predictive maintenance, real-time grid optimization, and new energy services.
- Tech News & Insight
- June 26, 2025
- Hema Kadia
Utilities are unlocking real-time intelligence and predictive maintenance by combining edge computing and AI with private LTE/5G networks. This blog explores how utilities process critical data locally to automate decisions, detect anomalies, optimize asset performance, and improve operational resilience—laying the foundation for the autonomous grid.
- Usecase
- June 25, 2025
- Hema Kadia
Verizon Business and Nokia will deploy six private 5G networks across Thames Freeport’s major logistics sites, including the Port of Tilbury, London Gateway, and Ford Dagenham to create a high-performance digital infrastructure supporting real-time logistics, AI automation, and edge computing. With plans to generate 5,000 skilled jobs and power sustainable trade, this initiative positions Thames Freeport as a next-gen smart trade corridor.
- Tech News & Insight
- June 16, 2025
- Robert James Armstrong
Telecom engineers know OSS systems aren’t broken—they just pretend to work. Outdated data, broken integrations, and overwhelming alerts create false confidence and slow operations. Discover how VC4’s Service2Create delivers real-time, trusted inventory and smarter workflows that engineers can actually rely on.
- Tech News & Insight
- June 16, 2025
- Suyash Rai
As the telecom world accelerates toward 5G-Advanced and sets its sights on 6G, artificial intelligence (AI) is no longer a peripheral technology — it is becoming the brain of the mobile network. AI-driven Radio Access Networks (RANs), and increasingly AI-native architectures, are reshaping how operators design, optimize, and monetize their networks. From zero-touch automation to intelligent spectrum management and edge AI services, the integration of AI and machine learning (ML) is unlocking both operational efficiencies and new business models.
This article explores the evolution of AI in the RAN, the architectural shifts needed to support it, the critical role of Open RAN, and the most promising AI use cases from the field. For telcos, this is not just a technical upgrade — it is a strategic inflection point.
This article explores the evolution of AI in the RAN, the architectural shifts needed to support it, the critical role of Open RAN, and the most promising AI use cases from the field. For telcos, this is not just a technical upgrade — it is a strategic inflection point.
- Tech News & Insight
- May 30, 2025
- Hema Kadia
Deutsche Telekom, Orange, and the Linux Foundation outline their 2025 cloud-native telecom roadmap, highlighting Kubernetes-native workloads, AI integration, observability, and zero-trust security models. Learn how open-source tooling, GitOps automation, and cultural transformation are reshaping next-gen telco 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.
