How Telecom Companies Are Adapting to AI

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

Telecom didn't adopt AI by choice. Networks simply outgrew human capability. For decades, Telecom networks were powered by rules, scripts, and highly skilled NOC engineers. And for a long time, that worked. But today's networks have reached a scale where human expertise alone is no longer enough. A single 5G cell site can generate thousands of performance counters every minute. Multiply that across thousands of sites in a metro network, and you're dealing with millions of events every hour. No operations team , no matter how experienced can analyse, correlate, and respond to that volume in real time. That's where AI stopped being optional. The Evolution of AI in Telecom :- ✅ Rule-Based Automation 🔹 If-this-then-that logic 🔹 Static thresholds 🔹 Automated scripts 🔹 Reliable, but limited to predefined scenarios ✅ Machine Learning 🔹 Learns patterns from historical data 🔹 Detects anomalies 🔹 Predicts traffic demand 🔹 Forecasts network KPIs ✅ Deep Learning 🔹 Understands complex, high-dimensional data 🔹 RF fingerprinting 🔹 Image-based tower inspections 🔹 Sequence modeling for network intelligence ✅ Generative & Agentic AI 🔹 Understands natural language and operational context 🔹 Generates configurations and Root Cause Analysis (RCA) 🔹 Assists engineers with troubleshooting 🔹 Progressively moves toward autonomous decision-making with human oversight 💡 Where Is Telecom Heading? Each wave builds on the previous one. The destination remains the same: ✅ Self-configuring networks ✅ Self-healing networks ✅ Self-optimizing networks The vision is no longer just automation. It's autonomous network operations. AI isn't another feature being added to telecom. It is rapidly becoming the operating system that will power the next generation of intelligent networks. #Telecom #AI #ArtificialIntelligence #AgenticAI #GenerativeAI #5G #NetworkAutomation #DeepLearning #MachineLearning #Telecommunications #DigitalTransformation #FutureOfWork #NetworkEngineering #Innovation

The telecom industry has spent two decades hunting for a new revenue silver bullet, only to find itself relegated to the "dumb pipe" status. While the buzz at Mobile World Congress centered on foldable phones and AI-branded handsets, the real shift is happening in the network architecture itself—turning idle infrastructure into a high-margin compute marketplace. The latest episode of "The Week with Roger" reveals why the "AI RAN" isn't just a technical upgrade, but the first credible business model for carriers to monetize their massive capital investments. 1. Monetizing the "Fallow" Compute Networks are built for peak capacity—the morning commute or the evening streaming rush. For the rest of the day, that massive processing power sits idle. By shifting from traditional fixed ASICs to NVIDIA-based GPUs, carriers can now resell this "fallow" compute as AI tokens. T-Mobile is already proving the model by running live translation services on its own base stations, keeping the revenue rather than paying a third party for the compute. 2. From Data Centers to John Saw's Kinetic Tokens The old "edge compute" model failed because it tried to sell data center space to end users. The AI RAN model is different: it creates a fungible currency of AI tokens. Whether it is real-time translation or local AI processing, the network becomes a distributed computer that initiates physical outcomes in real time. 3. The 6G Equipment Cycle Advantage This transition requires a complete hardware rethink. Because T-Mobile entered the 5G cycle earlier than its peers, they are positioned to hit the 6G equipment refresh five years ahead of the competition. Their joint 6G lab with Deutsche Telekom and Qualcomm is already targeting prototypes by 2029, specifically designed to handle these AI workloads at the edge. 4. The $480 Billion Revenue Drain While carriers look for new income, they are leaking existing revenue at a staggering rate. The GSMA now pegs the impact of global fraud at nearly half a trillion dollars annually. The move toward AI-integrated networks isn't just about selling tokens; it is about using that same on-site compute to identify and kill fraud in milliseconds before it hits the bottom line. 5. The European Cautionary Tale European carriers, burdened by low returns and a lack of investment, are falling behind in this compute race. With the U.S. spending five to six times more on capex, American networks are becoming the testing ground for this new "compute-reseller" model, while European infrastructure risks crumbling into coverage holes and 2G fallbacks. Is the telecom industry prepared to become a global compute provider, or will carriers remain the pipes that others use to transport AI value? Listen to the full analysis on "The Week with Roger" to hear how these shifts will redefine the market by 2029. https://bit.ly/40OuZUy

For decades, CSPs poured billions into 4G, 5G, spectrum, and radio networks — yet much of the digital value was captured above the network. Why? Because the value is not in infrastructure alone but also in the intelligence layer. In 2024, the top 100 operators generated $1.75T in revenue — but spent $1.38T in OPEX. That’s a massive opportunity to unlock profits. And it won’t be solved by more spectrum or faster radios. It will be solved by AI, automation, and a rethink of OSS/BSS and IT — from back-office systems into engines of growth, monetization, and customer experience. Legacy silos across billing, CRM, and network data are holding back innovation. Modern data platforms harnessing graph datasets and agentic AI will help change the trajectory — turning raw data into real-time intelligence that can act, orchestrate, and monetize. We’re already seeing it happen: > AI-driven anomaly detection and traffic prediction > Digital twins optimizing network energy use > Intent-based automation cutting order-to-cash cycles > GenAI agents accelerating catalog migration and product design Suppliers like Ericsson are embedding AI and automation across OSS/BSS to help CSPs reclaim control of key revenue levers. The winners will be those who shift investment toward the intelligence layer — building platforms that activate data, scale automation, and create new revenue streams. The question isn’t who builds the fastest network anymore. It’s who builds the smartest platform. For more on how this is being applied check out: https://lnkd.in/ehZhAk3h #Telecom #AI #Automation #OSS #BSS #AgenticAI #5G #NetworkAutomation #DigitalTransformation #AppledoreResearch #InnovatorsDilemma

AI agents are moving into telecom operations, but how autonomous are they really? For the latest TeckNexus LinkedIn Newsletter, we independently reviewed AI agent deployments across 50 global telecom operators to assess what is actually deployed, how much autonomy agents have, and how strong the supporting evidence is. Key findings: • No operator has fully removed humans from the loop • 54% still require a person to execute every recommended action • 42% allow agents to complete routine work independently, with human oversight for exceptions • Customer experience leads adoption, but most deployments remain closely supervised • Network operations show a different maturity curve • Back-office and IT AI agents are far more widespread than industry coverage suggests Deutsche Telekom currently has the broadest evidenced AI agent program across customer experience, network operations, RAN automation, and back-office functions. The research also found that Google Cloud is the most frequently named AI partner across publicly documented operator programs, ahead of #OpenAI, NVIDIA, Microsoft, and the major telecom equipment vendors. The newsletter examines: • Which operators lead, and why • Where AI agents are being deployed • How autonomy differs by use case • Regional strategies across APAC, Europe, North America, MEA, and Latin America • What operators and vendors should prioritize next #Telecom #AIAgents #AgenticAI #NetworkAutomation

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

🚦 **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

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

Video accounts for more than 70% of global internet traffic, and telecom operators have built their entire CapEx and RoI models on the assumption that this demand will keep exploding. More video → more bandwidth → more spectrum → more cells But here’s the disruption: NVIDIA Maxine. Instead of transmitting raw video frames,#Maxine sends only key facial points and uses AI on the receiver side to reconstruct the video in real time. The result: up to 10× less bandwidth compared to traditional codecs like H.264. The same video call that needed 2 Mbps now works at 200 Kbps—with no loss in perceived quality. In other words, we’ll move from pixel transmission to "Semantic Transmission", where just a compact representation is sent and AI regenerates the full experience locally. If 70% of today’s internet usage can be compressed by 90% or more, the very foundation of telco growth models collapses. #MNOs risk overbuilding networks for traffic that may never materialize. #5G and #6G won’t disappear, they bring ultra-low latency, massive #IoT, and industrial automation, but the assumption that video growth will fund RoI on spectrum auctions and network rollout is now shaky. The “data tsunami” may be an illusion. Telcos must adapt quickly: Shift from pipes to platforms, offering AI-native services and edge compute. Partner with AI players rather than being sidelined as commodity bandwidth providers. Bandwidth was king in the MPEG era. In the AI era, intelligence is king. Watch this short video to find what Jensen Huang, CEO of Nvidia, has to say: https://lnkd.in/dmXPF-Xz #AI #NVIDIA #Maxine #VideoCompression #AICompression #Telecom #5G #6G #MNO #Telco #Bandwidth #CapEx #RoI #DigitalTransformation #EdgeAI #FutureOfConnectivity