AI in Telecom Operations

As an AI engineer, this is the kind of application of AI I find most interesting. Not in apps. But in infrastructure. Most telecom networks are still designed in a static way. Capacity is planned in advance, which works until real-world demand suddenly spikes, like at a railway station during peak hours or a stadium during a match. That is where Vodafone Idea Limited approach stands out. They are using an AI-powered Self-Optimizing Network that dynamically shifts capacity in real time to high-demand areas. And with Vi’s 5G footprint set to expand from 43 cities to 133 cities nationwide by May 2026, this is not just a concept on paper, but something being rolled out at scale. From a systems perspective, this is similar to autoscaling in distributed systems, but applied to telecom infrastructure. Instead of over-provisioning everything, the network keeps observing usage and reallocating resources where needed. We often talk about AI as a feature. But this is AI acting as a control system, quietly optimizing things in the background. And honestly, that is where AI creates the most impact, when it becomes invisible. #Vi5G #5GIndia #Vi #VodafoneIdea

Telcos, Welcome to Your New Customers: AI Agents The iPhone marked a before and after in telecom. Networks engineered for voice collapsed under video demand. Operators spent billions on spectrum, radios, and fibre backhaul, but ARPU sank from $22.39 in 2009 to $13.56 by 2019 and another 20 percent by 2023. The value was captured by Apple, Google, and digital platforms, not the carriers who carried the load. A second shock is arriving with AI agents. These are not IoT devices with dumb SIMs but autonomous pieces of software, often cloud-based, that authenticate, negotiate, and transact thousands of times per second. Their arrival reshapes every part of the telco business. Networks shift from managing downstream video streams to orchestrating upstream biometric data, inference payloads, and relentless bursts of signalling. Edge compute becomes the new backbone, replacing CDNs as the critical layer of performance. Operations and BSS no longer revolve around monthly bundles but around real-time billing, event-based charging, and automatic SLA credits. The customer journey breaks apart: the “user” is no longer a human who can be persuaded by advertising or loyalty points, but an algorithm that selects providers based only on latency, trust, and price. Commercial logic pivots from ARPU to RPI, revenue per thousand verified interactions, with identity and determinism becoming the true products. Even the ecosystem map shifts: just as Apple and Google seized the interface in the smartphone era, hyperscalers are already racing to build agent marketplaces. SoftBank has announced plans to deploy one billion AI agents across its companies, and forecasts put the telecom opportunity at $188 billion by 2034. Nobody willl invite Telcos to the party. We will need to claim our role this time, or once again build the infrastructure while someone else takes the economics. Full analysis here: https://lnkd.in/gvkTKqzx

What does it take for Telco AI to succeed? 🚀 Here are the key actions Telcos need to take to unlock the full potential of AI and ensure industry-wide transformation: 1️⃣ Comprehensive Use Case Prioritization ➤ AI for Networks: Optimize operations with fault prediction and proactive maintenance. ➤ Network for AI: Offer services like fraud detection and anti-phishing, driving ROI for B2B/B2C customers. ➤ Innovations like multi-agent LLM orchestration for RAG require technical readiness and strong business validation. 2️⃣ Scalable AI Infrastructure with Multi-Tenancy ➤ AI at scale demands multi-tenant architectures with heterogeneous environments: ➤ GPUs (i.e. Nvidia, AMD), AI accelerators (Intel Gaudi, Habana), FPGAs, and DPUs. ➤ Preemptive scheduling and QoS-aware orchestration for dynamic resource allocation. 3️⃣ Distributed LLM Deployment and Optimization ➤ Harness distributed inference to minimize latency: ➤ Lightweight models for edge tasks. ➤ Centralized models for complex reasoning and multimodal processing (text, video, audio). 4️⃣ Low-Latency, High-Bandwidth Orchestration ➤ Build AI-ready networks to deliver: ➤ Latency guarantees (<10ms) and high throughput. ➤ Intelligent routing and token-level guarantees for predictable inference times. 5️⃣ Unified Control Plane ➤ Centralize AI and network coordination: ➤ Resource discovery for available LLM nodes. ➤ API layers for secure, seamless inter-agent communication. 6️⃣ Data Accessibility and Interoperability ➤ Streamline access to datasets with: ➤ Standardized data formats for portability. ➤ Secure, sovereign-compliant pipelines for real-time ingestion and third-party integration. 7️⃣ Ecosystem Collaboration and Standardized Blueprints ➤ Telcos thrive through partnerships: ➤ Work with OEMs, tech providers, and system integrators to create blueprints and scalable POCs. ➤ Validate these frameworks collaboratively to drive adoption across the industry. Telco AI success is about more than technology—it’s about harmonizing infrastructure, prioritizing impactful use cases, and collaborating across the ecosystem. What would you prioritize first? 👇

At MWC Barcelona this year, we launched the GSMA Open-Telco LLM Benchmarks to unite a community tackling the unique challenges of telecom AI. The first results were clear: out-of-the-box AI models simply aren’t fit for telco-specific needs. Now, with version 2.0, this effort has evolved into a thriving, open-source collaboration. The findings point to a hybrid architecture as the most effective path forward - combining the broad reasoning of foundation models with the precision of specialised components. In addition to providing clear direction for AI in telecom, what’s really exciting is the unprecedented level of industry collaboration. Operators including AT&T, China Telecom Global, Deutsche Telekom, du, KDDI Corporation, KPN, Liberty Global, Orange, Telefónica, Turkcell, Swisscom, and Vodafone are joined by research and technology partners - Adaptive AI, Datumo, Huawei GTS, Hugging Face, The Linux Foundation, Khalifa University, NetoAI, Universitat Pompeu Fabra - Barcelona (UPF), The University of Texas at Dallas and Queen's University - to build a shared ecosystem for experimentation, validation, and learning. Read more in our latest blog: https://lnkd.in/eTDH5PBX

🚦 **Reflections from NVIDIA GTC Washington, D.C 2025.** Last week’s GTC made one thing clear; AI-native infrastructure is evolving fast, and telecom is being invited to the table. But amid the excitement, it’s worth taking a balanced look at what’s real today versus what’s aspirational. 📡 Telecom in the Spotlight - **Nokia and NVIDIA** announced work on *AI-native 6G RAN nodes* using the Aerial/ARC-Pro platform, a promising signal of how compute and connectivity are converging. - Huang emphasized that *telecom is the nervous system of the economy*, calling for greater technology independence and domestic innovation. - Panels on “AI for Telecommunications” showcased prototypes of intelligent RAN optimization, edge analytics, and network planning powered by machine learning. ⚖️ Signals vs. Substance - **Early days**: Many of these initiatives are still in the *proof-of-concept* phase. Integrating AI models into live RAN environments will require years of testing, spectrum-policy clarity, and vendor alignment. - **Cost and complexity**: Embedding GPUs and AI accelerators into network nodes could shift the economics of telecom infrastructure, it’s a good idea, but not a trivial retrofit. Also, we have been there before with the whole MEC concept (which failed). - **Governance**: As sovereign-tech conversations grow louder, telcos will need to navigate new compliance, data-sovereignty, and security frameworks before large-scale deployment. 💭 My Take AI-enabled wireless is an exciting frontier, it promises smarter, more adaptive networks. .....But for now, the prudent path is **experimentation with guardrails**: pilot at the edge, validate the economics, and align architecture standards before scaling. If you’re in telecom or enterprise network architecture, this is a space to watch closely and approach "thoughtfully". #NVIDIAGTC #Telecom #AI #6G #RAN #EdgeComputing #NetworkTransformation

Legacy on-premise IT systems have a stranglehold on telco innovation. The AI-first future demands speed and agility that traditional software systems simply can't deliver. In the latest Telco in 20 podcast episode, Vodafone's Dr. Lester Thomas and I dive into how a radical new approach to IT is breaking down the barriers that have stalled telecom progress. While most operators debate whether cloud-native transformation is realistic, Vodafone is demonstrating not only is it doable — it's absolutely critical. We cover: • How Vodafone moved 17 petabytes of data from 600 Hadoop servers into Google Cloud to create their foundation for AI adoption • The company’s strict "cloud native" definitions have resulted in 80-90% of digital workloads being truly cloud native • The three principles Vodafone's Open Digital Architecture is based on: machine-readable standards, open-source collaboration, and proof-of-concept testing • Why AI is forcing complete software redesign at Vodafone, and how their AI Booster platform democratizes access while maintaining governance The operators who thrive won't be the ones doing IT the way it’s been done over the last 20 years. They'll be the ones bold enough to do the heavy lifting of truly becoming cloud-native and work to create a data platform that’s usable by AI so they are able to push the boundaries of what's possible in telecom. This is THE conversation to watch before you head to TM Forum’s DTW Ignite event in Copenhagen! If you missed the LinkedIn Live event you can watch the conversation on demand or listen to the audio only version on your favorite podcast player! Links in the comments. #Vodafone #telecommunications #cloudnative #AI #digitaltransformation

What if you could listen to every customer interaction—at scale? For years, contact center leaders have struggled with limited visibility. Most QA teams review only 2-5% of calls, leaving critical insights buried in recordings that never see the light of day. AI-powered Conversation Intelligence changes that. Instead of relying on outdated keyword spotting or manually scoring a fraction of interactions, AI can analyze 100% of your customer conversations, extracting call drivers, sentiment trends, and agent performance insights in real time. Imagine what you could do with that level of clarity. Identify trends before they become problems—spot surges in customer complaints and act before they escalate. Coach agents with precision—understand exactly where improvements are needed, without listening to hours of calls. Optimize automation strategies—pinpoint high-volume, repetitive workflows that are ripe for AI-driven automation. When every conversation becomes a source of insight, your contact center stops flying blind and starts making proactive, data-driven decisions. How would that change your CX strategy?

Let’s say your support center is getting hammered with repeat calls about a new product feature. Historically, the team would escalate, create a task force, and maybe update a knowledge base weeks later. With the tech available today, you should be able to unify signals from tickets, chat logs, and social mentions instead. This helps you quickly interpret the root cause. Perhaps in this case it's a confusing update screen that’s triggering the same questions. Instead of just sharing the feedback with the task force that'll take weeks to deliver something, galvanize leaders and use your tech stack to orchestrate a fix in real time. Don't have orchestration in that stack? Start looking into this asap. An orchestration engine canauto-suggest a targeted in-app message for affected users, trigger a proactive email campaign with step-by-step guidance, and update your chatbot’s responses that same day. Reps get nudges on how to resolve the issue faster, and managers can watch repeat contacts drop by a measurable percentage in real time. But the impact isn’t limited to operations. You energize the business by sharing these results in a company-wide standup and spotlighting how different teams contributed to the OUTCOME. Marketing sees reduced churn, operations sees lower cost-to-serve, and leadership sees a team aligned around outcomes instead of activities. If you want your AI investments to move the needle, focus on unified signals, real-time orchestration, and getting the whole business excited about customer outcomes....not just actions. Remember: Outcomes > Actions #customerexperience #ai #cxleaders #outcomesoveraction

"🌐📡 RIC in 5G: Optimizing Networks with AI & ML 🤖💡" Certainly! The Radio Intelligent Controller (RIC) is a pivotal component in the advanced 5G network architecture. 1. Overview 🌐   - RIC, part of the Open Radio Access Network (O-RAN) architecture, is essential for optimizing Radio Access Network (RAN) performance.   - It uses artificial intelligence (AI) and machine learning (ML) to dynamically manage and optimize RAN resources. 2. Types of RIC 🔄   - **Near-Real-Time RIC (Near-RT RIC)**: Operates within 10ms to 1s. It focuses on quick response tasks in RAN, like handover control and load balancing.   - **Non-Real-Time RIC (Non-RT RIC)**: Functions on a timescale greater than 1s. It's involved in broader network planning, policy guidance, and RAN optimization strategies. 3. Functions 🔧   - **Resource Management**: Efficiently allocates radio resources (e.g., spectrum, power) to enhance network efficiency.   - **Interference Management**: Reduces interference between cells to improve signal quality.   - **Load Balancing**: Distributes network load evenly to maintain performance during peak times. 4. Real-Time Examples 🌟   - **Adaptive Traffic Steering**: For instance, during a sports event, Near-RT RIC can dynamically redirect data traffic from an overloaded cell to neighboring cells with more capacity, ensuring smooth streaming for users.   - **Energy Saving**: Non-RT RIC can predict low traffic periods and turn off certain network elements to save energy, resuming full operation when traffic picks up.   - **Predictive Maintenance**: By analyzing patterns, RIC can predict and prevent potential network failures, scheduling maintenance before issues impact users. 5. Benefits ✨   - **Enhanced Network Efficiency**: Optimizes RAN performance for better network efficiency.   - **Cost-Effective**: Automates network management, reducing operational costs.   - **Improved User Experience**: Ensures high-quality service for end-users through intelligent network management. In essence, RIC plays a crucial role in 5G networks, utilizing AI and ML for intelligent, real-time network management, significantly enhancing performance, efficiency, and user experience. 1. #5GInnovation 📶 2. #RICtechnology 🌐 3. #SmartNetworking 💡 4. #AIin5G 🤖 5. #MLforRAN 📡 6. #ORANFuture 🌍 7. #NetworkOptimization 🔧 8. #5GEvolution 🚀 9. #IntelligentRAN 🧠 10. #NextGenTelecom 📱

The AI-RAN Taking Shape I'm thrilled to announce our latest research contribution that fundamentally transforms how we design, deploy, and test key functionalities of cellular networks. Our new paper "ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN" represents three major industry firsts: ⚡ First-Ever Automated Scheduler Generation: We've developed LLM agents that automatically convert research papers into functional code, generating 18 different scheduling algorithms directly from academic literature using OCR and AI. No more months of manual implementation in ns-3 or Matlab! Automatically generated schedulers are automatically deployed in a live network as dApps through a CI/CD pipeline - without the need to change a single line of code in the gNodeB implementation (CU or DU);  ⚡ Intent-Based Scheduling: Network operators can now express high-level requirements in natural language ("prioritize users with bursty traffic") and ALLSTaR automatically translates these into optimized scheduling policies according to operator’s intent. ⚡ World's First O-RAN Compliant AI-RAN Testbed: All validation conducted on X5G with AutoRAN, production-grade, multi-vendor 5G infrastructure with GPU acceleration, AI-for-RAN and AI-and-RAN capabilities, demonstrating real-world viability at scale. This work also introduces a methodological paradigm shift: instead of implementing one algorithm at a time, we can now systematically evaluate a vast body of scheduling literature in production-like environments. We're moving from manual, months-long integration processes to automated, intent-driven networks that adapt in real-time. This is the Open RAN and the AI-RAN vision - and a pathway toward 6G that builds on our national strengths and open ecosystem. Full paper: https://lnkd.in/eTNWPNRR Open6G www.open6g.us #ORAN #AIRan #OpenRAN #5G #WirelessResearch #AI #MachineLearning #Telecommunications #Research Our brilliant team: Maxime Elkael Michele Polese Reshma Prasad Stefano Maxenti Office of the Under Secretary of Defense for Research and Engineering NSF AI-EDGE Institute National Telecommunications and Information Administration (NTIA) Qualcomm