AI Challenges Telecom Providers in Telecom Network Transformation

#MCExclusive | 🚨 Sterlite Tech plans Rs 1,500-crore expansion as AI data centres drive global fibre demand -STL to invest Rs 1,500 crore over 3 years. -Investment driven by AI, telecom, and broadband demand. -Record order book of over Rs 18,000 crore. Danish Khan reports:  https://lnkd.in/gNPcRrFh #AI #Telecom

🚨 Sterlite Technologies is doubling down on the global fibre upcycle. The company plans to invest ₹1,500 crore over the next three years to expand manufacturing capacity and R&D, betting that AI data centres, telecom network expansion and rural broadband projects will sustain fibre demand for years. MD Ankit Agarwal tells Moneycontrol the company is also pursuing more long-term global contracts after securing a $1-billion-plus multi-year deal, while expecting India to emerge as the next growth market as data centre investments accelerate. My latest for Moneycontrol 👇 #Telecom #AI #DataCentres #OpticalFibre #Manufacturing #DeepTech #DigitalInfrastructure #India

Elisa Claims AI Automation Cut Network Incidents By More Than 80% RCR Wireless News reported that Elisa says AI-powered automation has reduced customer-impacting network incidents by more than 80% and pushed preventive actions to 99%, while the operator still has not deployed Open RAN. Full report: https://lnkd.in/gnmuqksc #CloudComputing #AIInfrastructure #EnterpriseTech

Interesting numbers from Mordor Intelligence on agentic AI in telecom. Hitting a $4.63B market size this year with a 13.5% CAGR makes total sense. The piece that jumps out to me is the deployment data. Cloud still holds about 60% of the share, but edge infrastructure is growing at nearly 14%. It proves carriers can't just rely on central cloud pools anymore. When you're trying to block fraud or handle network orchestration in real time, routing tokens back and forth to a distant data center completely kills performance. Also, it's pretty telling that fraud and security management is the fastest-growing application area. Once you give the keys to autonomous agents that can alter configurations on the fly, traditional static security logs become useless. Telcos are realizing they need agents to hunt other rogue agents. The transition from basic copilots to true closed-loop network autonomy is happening way faster than people think. #Telecom #AgenticAI #NetworkManagement #EdgeComputing #AIOps #NetOps #TelcoSecurity https://lnkd.in/eRpNK29t

🚨 Are digital twins in telecom already falling short of expectations? Not because of the technology itself — but because of what’s missing underneath. As Daria points out, without a unified network inventory, digital twins can’t deliver their full potential. A centralized inventory is what connects physical infrastructure, logical services, and operational processes into one usable view. And that matters even more as networks get more complex and automation and AI expectations keep rising. In telecom, fragmented data and scattered systems are still major blockers to real-time decision-making and reliable twin behavior. 👉 Dive into the full article: https://okt.to/zVZaxW #Telecom #DigitalTwin #AI #NetworkAutomation #Innovation #Transformation

Elisa told RCR that AI automation has reduced network incidents by more than 80% as the operator expands 5G monetization through premium services, network slicing, FWA, and private networks. https://hubs.ly/Q04nPpBs0

#AI investment across the broader economy is forecast to grow from $174 billion in 2025 to $467 billion by 2030. The question for #telecom operators is whether their networks will evolve alongside it. In his latest RCR Wireless News article, ABI Research VP Jake Saunders explains why AI-native core architectures, network AI agents, and intent-based interfaces are becoming essential building blocks for future #6G networks. The article also explores why operators that delay architectural change risk missing the next wave of AI-driven services. Read more: https://lnkd.in/dEvpFyAJ #Telecommunications #NetworkAutomation #Telco

Telecom networks are becoming more than communication platforms. They are evolving into distributed AI infrastructure. As 5G, edge computing, and cloud-native networks mature, AI is becoming essential across the telecom ecosystem - from network planning and operations to customer experience and new service creation. AI can help telecom operators: • Predict and prevent network failures • Optimize capacity, energy use and traffic routing • Automate network operations and troubleshooting • Improve fraud detection and cybersecurity • Deliver lower-latency AI services from the network edge • Build intelligent services for enterprises, industries and smart cities AI applications need reliable connectivity, distributed compute, low latency and secure infrastructure. Telecom networks provide the foundation required to move data, connect devices and deliver AI services closer to users. The future of telecom will not be defined by connectivity alone. It will be defined by how effectively connectivity, compute, data, and intelligence work together. At Mantra Telecom, we are exploring how telecom infrastructure can support the next generation of AI-driven networks and services. #MantraTelecom #AIInfrastructure #Telecom #ArtificialIntelligence #5G #EdgeComputing #NetworkAutomation #DigitalInfrastructure

The Future of Telecommunications: AI-Native Networks, Vertical AI & Autonomous Telecom The telecommunications industry is entering its most significant transformation since the introduction of mobile broadband. The future is no longer defined solely by faster connectivity—it is about building AI-native, autonomous, intelligent, secure, and sustainable digital infrastructure. Tomorrow's telecom operators will evolve into intelligent digital infrastructure platforms, where every network, process, and customer interaction is powered by AI. Key technology pillars shaping next-generation telecom  5G Advanced and 6G Networks  Open RAN (O-RAN) and Cloud RAN  Software-Defined Networking (SDN) & Network Function Virtualization (NFV)  Edge AI and Distributed Cloud  Private 5G Networks  Satellite & Non-Terrestrial Networks (NTN)  Digital Twins for Networks  Quantum-Safe Communications The rise of Telecom Vertical AI Future telecom organizations will deploy specialized AI agents that collaborate across the enterprise, including: • Network Operations Agents • Radio Optimization Agents • Spectrum Management Agents • Predictive Maintenance Agents • Customer Experience Agents • Revenue Assurance & Billing Agents • Fraud Detection & Cybersecurity Agents • Enterprise Connectivity Agents • IoT & Edge Intelligence Agents • Autonomous Field Service Agents Powered by multi-agent collaboration, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, knowledge graphs, vector databases, and autonomous orchestration, these intelligent agents will continuously optimize networks in real time. AI-native Telecom Architecture The next-generation telecom platform is built on: • AI Experience Layer • Telecom Vertical AI Layer • Multi-Agent AI Platform • Data Fabric & Knowledge Intelligence Layer • Digital Twin Platform • Real-Time Observability • Hybrid Cloud & Edge Infrastructure • Security, Trust & Responsible AI Governance Business impact AI-native telecom enables: • Self-optimizing and self-healing networks • Predictive service assurance • Hyper-personalized customer experiences • Autonomous operations and intelligent automation • Lower operational costs and higher network efficiency • New Business models through Network-as-a-Service (NaaS), AI-as-a-Service (AIaaS), Edge AI, and industry-specific digital platforms • Sustainable and energy-efficient network operations The telecom industry is evolving from connectivity providers to intelligent digital infrastructure providers, where autonomous AI systems, edge intelligence, and industry-specific Vertical AI redefine how networks are designed, operated, secured, and monetized. The future of telecom belongs to organizations that embrace AI-native architecture, autonomous operations, and trusted, collaborative AI ecosystems. #Telecom #Telecommunications #AI #AgenticAI #VerticalAI #MultiAgentAI #5G #6G #OpenRAN #CloudRAN #EdgeAI #DigitalTwin #NetworkAutomation #TelecomInnovation #AIOps

Will AI Impact Telecom's Bottom Line? As AI growth continues to be adopted into daily activity, the big question for the telecom industry (AT&T, T-Mobile, Verizon, Ericsson, Nokia, Samsung, and others) is whether they are going to benefit at all from this growth, and if so, how will they be able to increase their revenue due to AI. As of now, most of the talk of AI in telecom is focused on increased efficiency in operating the network to reduce operating cost and increase resiliency. Telecom operators are struggling to find a way to monetize the growth of AI usage. Telecom operators will face another LTE dilemma. Significant money was spent on LTE deployment, but over-the-top companies gained all the profit from the faster connection, wider deployment, and affordable price. The only thing the operators got was a modest increase in subscription fees. As of now, AI is over-the-top. The user uses an app to send the prompt to the hosted models and then gets an answer back — no special connection or dedicated bearer for these services. When carriers implemented "all you can eat" packages, the opportunity to capitalize on the increased traffic disappeared. Enterprise AI traffic is going to be significantly more, but it will be entirely focused on connectivity to the cloud providers or the enterprise data centers. The additional traffic will demand an increase in the size of the pipes connecting the enterprise to its various destinations. Again, limited opportunity for telecom providers to capitalize on AI traffic. Can the telecom industry change this paradigm and stop being a dumb pipe? The two available options are slicing and Mobile Edge Compute (MEC). Utilizing a dedicated slice will ensure that AI traffic gets better treatment than other non-critical traffic. Deploying LLMs at the MEC will improve the response time of AI to end consumers by tens of milliseconds. As of now, most AI applications do not justify charging additional money for a dedicated slice or reduced delay that does not significantly impact the end-user experience. If the telecom industry wants to enable these features and charge extra for them, it has to promote applications that demand the lowest latency possible and a dedicated traffic channel. What are some of these potential applications? That will be discussed in the next posting. #Telecom #AI #5G #NetworkSlicing #EdgeComputing #AIinTelecom #Verizon #Att #tmobile