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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.

India Mobile Congress 2025: Telecom GDP to 20% by 2037

India Mobile Congress 2025: Telecom GDP to 20% by 2037
India Mobile Congress 2025 in New Delhi framed a clear ambition: scale domestic innovation, shape 6G, and turn telecom into a larger engine of GDP growth. Leaders underscored a whole-of-government approach, with multiple ministries backing IMC and the Department of Telecommunications and the Cellular Operators Association of India co-hosting. India’s telecom and digital sector is estimated to contribute roughly 12–14% to GDP today. Leaders at IMC projected this could reach about 20% by the mid-2030s if India scales advanced connectivity, software-led services, and domestic manufacturing. India’s 6G push was tied to a potential GDP uplift exceeding a trillion dollars by 2035.

Jio at IMC 2025: AI, Safety and 6G Roadmap

Jio at IMC 2025: AI, Safety and 6G Roadmap
At India Mobile Congress 2025, Jio framed a broad agenda that ties devices, networks, AI skills, and safety into a national-scale digital strategy. The message from Jio’s chairman was clear: India’s telecom flywheel now spans the full value chain, from semiconductors and device platforms to fraud management and the next wave of 6G research. Telcos are shifting from pure connectivity to platform businesses that bundle devices, cloud access, security, and AI services. JioPC is positioned as an “AI-ready” computer that turns any screen into a managed endpoint, delivered through a subscription model.

Vi Protect: AI-Powered Network Security Debuts at IMC 2025

Vi Protect: AI-Powered Network Security Debuts at IMC 2025
Vodafone Idea (Vi) used India Mobile Congress 2025 to unveil Vi Protect, a network-integrated, AI-powered security suite aimed at stopping spam calls, fraudulent messages, and fast-moving cyber threats for both consumers and businesses. By moving detection into the network rather than relying on over-the-top apps, Vi is positioning security as a core service-level capability with lower latency, broader coverage, and tighter control. Unlike app-only caller ID and spam filtering, Vi Protect runs at the DNS, SMS, and voice gateway layers, combining AI models, web crawlers, and subscriber feedback loops. The operator says its systems have already intercepted more than 600 million scam and spam attempts.

Nokia and du test AI for optical networks

Nokia and du test AI for optical networks
Nokia and du completed a production-style trial that applied classical and generative AI to accelerate optical network planning and day-to-day operations. The partners tested Nokia’s WaveSuite AI, an automation assistant that exposes network intelligence through a natural-language interface. du cited faster troubleshooting, fewer errors in routine changes, and better resource utilization. The operator also reported concrete planning gains: roughly half the time to develop optical plans and about 30% greater efficiency in network designs, which translates to less overbuild and faster time-to-market. The net effect is improved service delivery and a smoother experience for operations teams tasked with meeting strict SLAs.

AT&T 5G lights up NYC’s Joralemon Street Tunnel with Boldyn’s neutral-host network

AT&T 5G lights up NYC’s Joralemon Street Tunnel
AT&T has gone live on Boldyn Networks’ neutral-host infrastructure in New York’s Joralemon Street tunnel, with G line tunnel segments next in the rollout. AT&T customers can now access 5G mobile service through the 1.1-mile (1.8 km) Joralemon Street tunnel, the oldest underwater subway tunnel in New York City, which links the 4/5 lines between Borough Hall in Brooklyn and Bowling Green in Manhattan. Subway connectivity has shifted from convenience to critical infrastructure for safety, accessibility, and productivity. AT&T’s first-mover status sets a competitive benchmark; other national carriers (Verizon and T‑Mobile) are expected to follow as on-boarding progresses across the system.

Nokia FTTH digital twin and AI tools boost reliability

Nokia FTTH digital twin and AI tools boost reliability
Nokia has introduced a fiber-to-the-home (FTTH) digital twin and AI-powered applications inside its Altiplano platform to give operators a unified view of active and passive assets and to improve reliability with faster, first-time fixes. The core launch centers on creating a digital twin of the FTTH network that stitches together live data from active elements (OLT/ONT, IP edge, customer premises equipment) with outside-plant passive infrastructure (ducts, cables, splitters) maintained in inventory and geospatial systems. Together, these tools target the highest-impact operational pain points: early anomaly detection, automated topology audits, faster root cause analysis, and improved first-time fix rates.

Fujitsu and NVIDIA expand full-stack AI infrastructure

Fujitsu and NVIDIA expand full-stack AI infrastructure
Fujitsu is expanding its strategic collaboration with NVIDIA to deliver a full-stack AI infrastructure that pairs domain-specific AI agents with high-performance compute for enterprise and industrial use. The companies will co-develop an AI agent platform and a next-generation computing stack that tightly couples Fujitsu’s FUJITSU-MONAKA CPU series with NVIDIA GPUs using NVIDIA NVLink-Fusion. On the software side, Fujitsu plans to integrate its Kozuchi platform and AI workload orchestrator (built with Fujitsu AI computing broker technology) with the NVIDIA Dynamo platform.

California SB 53: AI Safety Law for Frontier Models

California SB 53: AI Safety Law for Frontier Models
California has enacted SB 53, a first-of-its-kind AI safety law aimed at large model developers, with ripple effects for enterprises that build, buy, or operate AI at scale. SB 53 targets “frontier” AI developers—think OpenAI, Anthropic, Meta, and Google DeepMind—requiring public transparency on how they apply national and international standards and industry best practices. It institutionalizes safety incident reporting to California’s Office of Emergency Services and extends protections for whistleblowers who surface material risks. The California Department of Technology will recommend updates annually, ensuring the regime evolves with the tech.

AI Leaders Double Revenue and Cut Costs 40%

AI Leaders Double Revenue and Cut Costs 40%
The AI value gap is widening—and it’s now a strategy problem, not a tooling problem. Fresh research shows a small cohort of “future-built” companies converting AI into material P&L impact while most firms lag despite sizable spend. BCG’s 2025 assessment of 1,250 senior executives finds only 5% of companies have the capabilities to consistently generate outsized AI value, with 35% scaling and beginning to see benefits, and a full 60% reporting little to no financial impact to date.

India Joins Telecom Equipment Manufacturing Elite with Indigenous 4G

India Joins Telecom Equipment Manufacturing Elite with Indigenous 4G
India’s nationwide launch of BSNL’s “Swadeshi” 4G stack moves the country from a services-first model to domestic production of core telecom equipment at national scale. India formally launched an indigenous 4G stack for state-run BSNL, alongside more than 97,500 towers announced from Jharsuguda, Odisha. Officials highlighted early reach metrics, noting that roughly 92,000 sites are active and connecting an estimated 22 million users. Telecom equipment sovereignty has become a board-level issue as operators de-risk supply chains, comply with trusted source mandates, and balance costs amid rising traffic and spectrum refarming needs.

Why 95% of GenAI Pilots Fail to Scale Across Industries, Including Telecom

Is AI Underperforming in Telecom? Challenges, Use Cases & the Road Ahead
AI is everywhere in telecom, yet most pilots never make it into production because the industry’s data, tooling, and operating models are not ready for scaled automation. Recent industry research suggests that about 95% of AI pilots in telecom fail to scale beyond proofs of concept. Leaders are moving from pilots to platforms by embedding AI in the systems that run the business and anchoring every initiative to measurable outcomes. Telecom AI will not scale through pilots alone; it scales when embedded in the systems that run revenue, experience, and 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.
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