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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.

OpenAI AI Chip with Broadcom

OpenAI AI Chip with Broadcom
OpenAI is reportedly partnering with Broadcom to bring a custom AI accelerator into mass production next year, a move aimed at cost control, supply assurance, and tighter hardware–software integration. The reported partnership points to OpenAI deploying its own chips internally rather than selling them, following the playbooks of Google (TPU), Amazon (Trainium/Inferentia), Microsoft (Maia/Athena), and Meta (MTIA). AI training and inference costs remain stubbornly high as model sizes, context windows, and user demand surge. Custom silicon can shift the cost curve by optimizing for specific workloads, improving energy efficiency, and reducing total cost of ownership across compute, memory, and networking.

Nokia Oulu R&D campus powers AI-native 5G/6G networks

Nokia Oulu R&D campus powers AI-native 5G/6G networks
AI is shifting network design from throughput-first to data- and compute-aware architectures, where radios, baseband, and transport must expose telemetry for model training and inference at the edge. Consolidating 3GPP standardization, silicon design, radio development, lab verification, and New Product Introduction (NPI) in one campus shortens feedback loops between research, productization, and manufacturing. That is essential as the industry transitions through 5G-Advanced (3GPP Releases 18–19) to early 6G concepts such as joint communication and sensing, RAN compute offload, and AI-native control loops.

Jio Haptik WhatsApp AI Agents for SMBs from ₹10,000

Jio Haptik WhatsApp AI Agents for SMBs from ₹10,000
Reliance Jio's Haptik has launched WhatsApp and voice-based AI agents for small and midsize businesses (SMBs) starting at 10,000, signaling a step-change in how Indian firms automate customer engagement at scale. Haptik is extending its SMB platform, Interakta WhatsApp-first CRM and support suite used by over 50,000 businessesto include autonomous AI agents for chat and voice. The entry pack is priced at 10,000 and covers roughly 2,000 AI-driven conversations, putting the effective cost per interaction in the 35 range. Crucially, they support 22 Indian languages. Haptik reports that early adopters are automating up to 80% of repetitive support queries and seeing 2025% lift in lead-to-sale conversions.

O2 Telefónica Launches AI-Driven Telco Operations with Tech Mahindra and NVIDIA

O2 Telefónica Launches AI-Driven Telco Operations with Tech Mahindra and NVIDIA
O2 Telefónica Germany has deployed a Large Telco Model powered by Tech Mahindra and NVIDIA to transform its operations into a service-centric, AI-native system. By integrating telemetry, tickets, and service topology into a unified AI fabric, the model enables automated root-cause analysis, dispatch optimization, and intent-based workflows. This marks a tangible shift toward autonomous network operations, with measurable gains in operational efficiency, SLA compliance, and customer experience.

The AI Advantage: From Dream to Reality in Telecom and Finance

The AI Advantage: From Dream to Reality in Telecom and Finance
Artificial Intelligence promises to revolutionize the telecom industry, but many organizations struggle to see a return on their AI investments due to flawed implementation. The path to real AI success lies in prioritizing trust, security, and people, fostering an AI-driven culture. This involves building human oversight into every stage, developing intuitive tools, and empowering teams with training. Embracing this human-centered approach unlocks benefits like optimized network performance and enhanced customer experience.

Salesforce AI agent simulator bridges pilot-to-production gap

Salesforce AI agent simulator bridges pilot-to-production gap
Salesforce is moving to close the gap between slick AI demos and operational reality by stress-testing agents inside simulated business environments before they ever touch production. Salesforce introduced CRMArenaPro (a digital twin for enterprise workflows), an Agentic Benchmark for CRM (to compare agents across business-centric metrics), and new Account Matching capabilities (to unify records and clean underlying data). The Agentic Benchmark for CRM evaluates accuracy, cost, speed, trust and safety, and environmental sustainability. Stand up a sandbox that mirrors production and run agents through end-to-end scenarios with synthetic-but-realistic data. Tighten OAuth and third-party risk controls before expanding agent privileges

TELUS AI Platform: Enterprise-Scale Telecom Innovation

TELUS AI Platform: Enterprise-Scale Telecom Innovation
TELUS moved beyond experiments to enterprise adoption: 57,000 employees actively use gen AI, more than 13,000 custom AI solutions are in production, and 47 large-scale solutions have generated over $90 million in benefits to date. Time savings exceed 500,000 hours, driven by an average of roughly 40 minutes saved per AI interaction. The scale is notable: Fuel iX now processes on the order of 100 billion tokens per month, a signal that the platform is embedded in day-to-day work rather than isolated to innovation teams. TELUS designed for trust from the start: its Fuel iXpowered customer support tool achieved ISO 31700-1 Privacy by Design certification, a first for a gen AI solution.

The Data Analyst’s Dilemma: Reclaiming Time for Profit in the Modern Business Landscape – A Business Perspective

This article explores the challenges data analysts face due to time-consuming data wrangling, hindering strategic analysis. It highlights how fragmented data, quality issues, and compliance demands contribute to this bottleneck. The solution proposed is AI-powered automation for tasks like data extraction, cleansing, and reporting, freeing analysts. Implementing AI offers benefits such as increased efficiency, improved decision-making, and reduced risk, but requires careful planning. The article concludes that embracing AI while prioritizing data security and privacy is crucial for staying competitive.

Kyndryl’s $2.25B India Investment to Scale AI Modernization

Kyndryl’s $2.25B India Investment to Scale AI Modernization
Kyndryls' three-year, $2.25 billion plan signals an aggressive push to anchor AI-led infrastructure modernization in India's digital economy and to scale delivery across regulated industries. The $2.25 billion commitment, anchored by the Bengaluru AI lab and tied to governance and skilling programs, should accelerate enterprise-grade AI and hybrid modernization across India. Expect more co-created reference architectures, deeper public-sector engagements, and tighter integration with network and cloud partners through 2026. For telecom and large enterprises, this is a timely opportunity to industrialize AI, modernize core platforms, and raise operational resilience provided programs are governed with clear metrics, strong security, and a pragmatic path from pilot to production.

India Telecom AI Governance: TRAI and MeitY Updates

India Telecom AI Governance: TRAI and MeitY Updates
India's AI oversight for telecom is moving from recommendations to implementation, with policy review and technical workstreams running in parallel. The Telecom Regulatory Authority of India has issued recommendations on leveraging artificial intelligence and big data in telecom, including the creation of an independent statutory authority for AI governance. The proposed Artificial Intelligence and Data Authority of India (AIDAI) is envisioned to promote responsible AI development and regulate sectoral use cases. The Ministry of Electronics and Information Technology has initiated projects with research bodies and universities focused on how to ensure and test AI trustworthiness.

AI Job Loss Fears: Telecom Strategy

AI Job Loss Fears: Telecom Strategy
Fresh polling signals rising public concern that AI could upend employment, destabilize politics, and strain social and energy systems. A recent Reuters/Ipsos survey of 4,446 U.S. adults found that 71% worry AI will permanently displace too many workers. Seventy-seven percent of respondents fear AI will fuel political instability if hostile actors exploit the technology. The poll also shows broad worry about AIs indirect costs: 66% are concerned about AI companions displacing human relationships, and 61% are concerned about the technology's energy footprint. Bottom line: Public concern is high, and that increases the cost of missteps.

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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