Artificial Intelligence in Telecommunication Networks

🚀 Reflections from #GTC26: The New Era of AI-Native Telecommunications Just wrapped up an incredible week at NVIDIA GTC 2026, and the takeaway is clear: The telecommunications industry is no longer just the "pipe"—it is becoming the backbone of global AI infrastructure. Here are the 3 shifts that redefined the landscape for me: 1️⃣ The "AI Grid" is the New Revenue Engine The NVIDIA AI Grid Reference Design is a game-changer for operators. By transforming existing physical footprints—cell sites, switching offices, and regional hubs—into distributed AI infrastructure, telcos can monetize their edge like never before. It’s no longer just about coverage; it’s about providing high-performance compute exactly where the data is born. 2️⃣ AI-RAN: The Connective Tissue for Physical AI We’ve moved beyond chatbots. Physical AI—systems that perceive, reason, and interact with the physical world—requires near-zero latency to function at scale. AI-RAN is the critical enabler here, providing the low-latency connectivity needed for these autonomous systems to "breathe." In short: If Physical AI is the muscle, AI-RAN is the nervous system. 3️⃣ 6G: Born in the Simulation The road to 6G is being paved in simulation. With the NVIDIA Aerial Omniverse Digital Twin (AODT), we are witnessing the first wireless generation born in a digital environment. Telcos can now architect and validate complex AI-native networks in a risk-free virtual world before a single radio wave hits the live spectrum. #NVIDIAGTC #6G #AIGRID #AIRAN #DigitalTwin #PhysicalAI

Bringing AI Agents to the Heart of 6G Networks! Our latest work: MX-AI – the first end-to-end, multi-agent system that can observe and control a live 5G network through natural language. Future 6G networks will connect billions of devices, demand ultra-low latency, and run on tight energy budgets. Managing this complexity manually is simply impossible. 🤖 MX-AI introduces Large Language Model (LLM)-powered agents that understand operator requests in plain English, monitor the network in real time, and take actions automatically — from diagnosing problems to reconfiguring network slices. What’s unique: ✅ Works on a real 5G Open RAN testbed (not just simulations) ✅ Uses a team of cooperating AI agents (planner, monitor, validator, executor) to turn high-level intentions into precise network commands ✅ Achieves human-expert-level decision accuracy with just seconds of latency ✅ Fully open-sourced to accelerate research on AI-native RANs The result? Operators can ask: “Guarantee 10 Mbps for the emergency services slice from 6–7 pm” …and MX-AI will figure out how to make it happen, implement the change, and confirm it worked. This is a step toward truly intent-driven, AI-native 6G networks — where humans set the goals, and intelligent agents handle the complexity. 📄 Read the paper: https://lnkd.in/dz3muvcN A huge thank you to my amazing co-authors and collaborators: Ilias Chatzistefanidis, Andrea Leone, Ali Yaghoubian, Mikel Irazabal, Sehad Nassim, Lina Bariah, Navid Nikaein. And to our institutions for their support: @EURECOM, @BubbleRAN, @AaltoUniversity, @KhalifaUniversity. #6G #OpenRAN #AI #TelecomInnovation #LLM #MXAI #AgenticAI

Every industry is being reshaped by #AI. But #telecoms? Many still write it off as too slow, too legacy, too regulated. That view is increasingly outdated. Following #MWC2026, I mapped Ericsson's AI deployments across the standard AI stack: Infrastructure, Model, Platform and Application. The picture is more complete than most people realize. This is not a roadmap. Most of it is live today. Here is what stands out: 📡 Infrastructure (Device & RAN) AI embedded at the physical layer. On-device inference, Vehicle-to-Everything (V2X) in connected cars, analytics inside IoT sensors. In the RAN, spectrum optimized continuously, energy cut dynamically, beamforming improved in real time. The network hardware is becoming intelligent. 🧠 Model (Multi-access Edge Computing (MEC)) Where AI models actually run, close to the source with single-digit millisecond latency. Autonomous fault detection, real-time inference, industrial automation, live network simulation. From reactive operations to self-healing behavior. 🏛 Platform / Tooling (5G Core) Orchestration, slicing, policy and APIs all AI-driven. Operators declare intent. AI configures the rest. The role of the network engineer shifts from manual configuration to oversight. ☁️ Application (Cloud & Operations Support Systems (OSS)) AI running operations end-to-end. Predicting failures, automating planning, moving humans to oversight. Federated learning and an AI model marketplace are next. Ericsson is not adding AI to the network. They are rebuilding the network around it. 🔭 Looking ahead Most AI transformations sit on top of infrastructure. In telecoms, it is happening inside it. Near term, cross-layer AI and open Network APIs turn the telecoms stack into a platform others build on. By 2030, 6G makes AI-nativeness a design requirement, not a retrofit. The network stops carrying intelligence and becomes the intelligence layer itself. Telecoms is not catching up to the AI wave. It is becoming the infrastructure the AI wave runs on. Proud to be part of building exactly that at Ericsson. 💡 Which layer of the stack surprises you most? #Telecoms #AI #5G #MWC #Ericsson #NetworkIntelligence #AIStack #6G

Artificial intelligence is changing telecom infrastructure in a very practical way: networks can no longer behave like passive transport systems. They are becoming active, programmable platforms that must support workloads with very different performance profiles. That shift matters because AI traffic is not uniform. Pre-training is mostly about scale. It needs high-capacity, symmetrical links, large data movement, and stable “always-on” connectivity that can absorb massive bursts without degrading performance, often within the same campus or data center environment. Inferencing is different. It depends less on raw volume and more on predictability. Latency, jitter, and deterministic behavior become critical, especially when AI is embedded into real-time applications, automated decisions, or agentic workflows. This means network topology has to match the workload. One of the biggest changes is the rise of East-West traffic: data moving between data centers, clouds, edge nodes, and metro locations. This is starting to matter more than the traditional North-South client-server model. Large AI models often need to replicate data, coordinate compute, enforce policies, and move workloads across multiple locations. That requires a different network architecture. The risk of getting this wrong is not just higher cost. It can directly affect application reliability and performance. For example, if an agentic AI workflow depends on deterministic API responses and the network cannot deliver consistent latency or jitter, the workflow becomes unreliable. The strategic implication is clear: enterprises need more modular, programmable, and cloud-service-based network platforms. Network-as-a-Service becomes relevant not because it is a new commercial wrapper, but because AI workloads need infrastructure that can adapt dynamically. The future network will not be judged only by capacity. It will be judged by how intelligently, predictably, and efficiently it supports each AI workload.

Over the last 7 years, IBM has been quietly building something quite deliberate. Not a single product. Not a one off platform. But a set of capabilities that, taken together, form the operating backbone for enterprise AI. You can see the pattern when you step back: Foundation: Red Hat Performance: IBM Instana and IBM Turbonomic Governance: Apptio, an IBM Company and HashiCorp Integration and data: Webmethods and DataStax Flow: now strengthened with Confluent Individually, each of these solves a specific problem. Together, they start to look more like a system. For telecom operators, that matters. Telcos are not short of data. They are not short of platforms. What they are often dealing with is fragmentation, latency between systems, and the challenge of turning insight into action at scale. AI only works in that environment if a few things are true: Data moves in real time Systems are observable Resources are optimised continuously Governance is built in, not bolted on That is where this kind of architecture becomes relevant. Not as a “data fabric” concept, but as a way of running complex, distributed environments where decisions need to be made inside the operational loop, not after the fact. In telecoms, that translates into very practical outcomes: Better network performance Faster issue resolution More efficient use of infrastructure Lower cost to serve The interesting question now is not whether the components exist. It’s whether operators bring them together in a way that actually changes how their business runs. Because in telecoms, AI will be judged by how deeply it is embedded into the operating model and how it influences performance, efficiency and outcomes over time, not how impressive it looks in isolation. #Telecoms #AI #DataStreaming #Observability #FinOps #Cloud #TelcoTransformation Alison Clegg James Stewart Kash Hussain Callum Simpson Alexander Verdi Elke Kunde Begüm Daşkaya Gökhan Yılmaz Chantelle Govender Titus Masike

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

Telecom has always evolved in waves. Each one reshaping not just networks, but the way leaders think. Before GSM, we operated siloed systems until GSM MoU asked a bold question: what if we aligned? That decision unlocked 3GPP, LTE surge, and ultimately 5G NR - transforming telecom from infrastructure into the digital backbone of economies. Now we’re at the next wave. Only this time, the catalyst is AI. And with AI the data is clear: 50%+ of CXOs already see noticeable gains in decision speed, foresight, and creativity through AI, and active use is expected to more than double within three years. At the same time, only 1% expect AI to make autonomous strategic decisions - a reminder that leadership judgment still sets the direction. Three things matter now: 1. AI doesn’t replace decisions, it improves the right ones. From capacity planning to churn prediction and service design, AI elevates decision quality while leaving human‑led calls where stakes are reputational. 2. Human AI chemistry becomes a leadership differentiator. Just as GSM succeeded through alignment, today’s leaders must learn to think with AI. Tools will standardize; judgment and collaboration won’t. 3. Governance is the accelerator. With 71% of CXOs citing legal and security risks, trust, explainability, and responsible data practices are what enable AI to scale safely and confidently. We’ve been here before - GSM, 3GPP, LTE, 5G, NTN. Each leap required leaders to rethink how decisions get made. AI is simply the next leap. And the leaders who lean in now will shape what comes next. https://lnkd.in/gKBNB_cu

Nokia is Doubling Down on the "AI-Native" Future I’ve said it before: AI is the only path forward for a telecom industry that has spent years struggling to move beyond being a "dumb pipe." At MWC this year, the conversation shifted from the maturation of 5G to the "seamless path" toward 6G. But here’s the reality—you don’t get to 6G or "Agentic Networking" without a complete architectural overhaul. That’s why today’s news from Nokia is important. Nokia is not just adding AI as a software "overlay" but rather weaving intelligence directly into the silicon, the RAN, and the Core. Nokia is bringing in some heavy hitters: Udayan Mukherjee (formerly of Intel) is taking the helm for the RAN and Core CTO team. Udayan’s expertise in vRAN is exactly what’s needed to bridge the gap between traditional telco hardware and the flexible, programmable, AI-native architectures of tomorrow. Pavan Kurapati and Oguz Sunay are joining to spearhead Datacenter Networking and AI Research. If you want to win in AI-RAN, you have to master the intersection of the edge cloud and wireless networking. This is where the "Complexity Tax" gets paid or eliminated. The "Secret Sauce": The Technical Advisory Board What really caught my eye, though, is the establishment of a Technical Advisory Board (TAB). Nokia is bringing in Nick McKeown and Raj Yavatkar as founding members. The telecom industry has a "value capture" problem. We’ve spent 20 years talking about AI-powered services while sticking to legacy billing. To break that cycle, the network must become intelligent and programmable from day one. By aggressively hiring top-tier talent from the silicon and cloud worlds, Nokia is proving they understand that the future of networking isn't just about "faster speeds"—it’s about building the AI Factory of the future. Great to see the team expanding, Pallavi Mahajan. This is how you move from "AI Curiosity" to "AI Urgency." Mark Provost #AI #Telecom #Networking #6G #Nokia #AIRAN #Innovation #CloudNative

The next telecom battleground may not be phone plans. It may be physical AI. T-Mobile is making a bold argument: its 5G Advanced network, standalone 5G foundation, uplink capacity, and edge compute potential position it to support real-world AI workloads better than rivals. That is a serious strategic claim. The signal is clear. AI is moving from screens and chatbots into machines, robots, cameras, vehicles, factories, hospitals, logistics networks, and field operations. Those use cases do not just need cloud intelligence. They need low latency, reliable connectivity, uplink performance, distributed compute, and operational trust. That is where telecom operators may get a second chance to matter in enterprise transformation, but the hype should be separated from reality. “Fallow compute” at the network edge is not automatically a business model. Physical AI is not yet a massive carrier revenue stream, and enterprises will not buy the story unless the use cases are measurable, secure, supportable, and economically justified. Still, this is the right conversation. The future of telecom will not be won by selling connectivity alone. It will be won by proving that networks can become intelligent infrastructure for the physical world. What would make enterprises trust carriers with mission-critical AI workloads? #AI #Telecom #5G #5GAdvanced #PhysicalAI #EdgeComputing #EnterpriseAI #DigitalTransformation #NetworkStrategy

The global tech race is no longer just about AI. It’s about who builds the infrastructure that AI depends on. In the US, AT&T is committing over $250B to modernize telecom infrastructure for the AI era. At global platforms like MWC 2026, leaders like NVIDIA, Ericsson, and Qualcomm are pushing toward AI-native, autonomous networks where compute, connectivity, and intelligence operate as one system. And beyond the US — a new force is emerging: 👉 HUMAIN is building hyperscale AI infrastructure backed by billion-dollar investments, targeting global AI compute demand and cloud-to-edge ecosystems. 💡 Here’s the shift most people are missing: We are moving from: ➡️ Cloud-first architectures ➡️ To AI-first infrastructure ecosystems Where: • Networks are no longer just carriers of data • Data centers are no longer isolated • Edge is no longer optional Everything is becoming one intelligent, synchronized system ⚡ And this is where real complexity begins: • Machine-to-machine traffic is exploding • AI workloads are redefining network behavior • Real-time systems demand precision across compute, network, and timing This is not a scaling problem anymore. This is an architecture problem. 🔍 From my journey working across Verizon, AT&T, T-Mobile, and Comcast I’ve seen firsthand: • How network design is evolving to support AI-native workloads • How edge + cloud integration is reshaping service delivery • And how synchronization across systems is becoming mission-critical 🚀 What excites me today: Not just building networks… But shaping intelligent infrastructure ecosystems Where: • AI drives decision-making • Networks adapt in real-time • And infrastructure becomes predictive, not reactive The next wave of innovation won’t come from isolated breakthroughs. It will come from those who can connect AI, networks, and infrastructure into one seamless architecture. That’s exactly the kind of challenge I’m focused on solving. #AI #5G #Telecom #NetworkArchitecture #EdgeComputing #Cloud #Innovation #Verizon #ATT #TMobile #Comcast #NVIDIA #Ericsson #Qualcomm #SaudiVision2030