AI in Telecom Operations

I'm excited to announce the launch of AI Refinery for Sovereign and Agentic AI, a groundbreaking platform that deepens our partnership with NVIDIA. This first-of-its-kind platform champions data sovereignty and operational resilience through physical AI, paving the way for enhanced competitiveness in the journey toward agentic AI.   As I've mentioned before, I firmly believe that AI presents a unique opportunity for Europe to reinvent its economy, drive productivity, resilience, and competitiveness, and support future growth. I'm incredibly proud to see the momentum our clients are gaining, including Public Power Corporation, Roche, Kion Group, Noli, and Nestlé.   Nestlé, for instance, is launching a new AI-powered in-house service that will generate high-quality product content at scale for eCommerce and digital media channels. This initiative exemplifies the transformative potential of AI in driving business efficiency and innovation. The expansion of our AI Refinery platform is particularly significant for European organizations, enabling them to accelerate the deployment of AI agents while addressing their sovereignty concerns. This is especially crucial for the public sector and critical infrastructure industries, such as energy, telecommunications, and defense.   We continue to support our clients in maintaining control over their critical data and leveraging innovative AI solutions through this expanded AI Refinery platform. More details here: https://lnkd.in/dvekqfB6 #Noli #Nestle #PublicPowerCorporation #KionGroup #Roche #AgenticAI #AI #Accenture

Nvidia just invested $1 Billion in Nokia. For AI networking. This isn't just another tech deal. This is the future of telecom being written. 💰 WHAT HAPPENED: The Deal: → Nvidia: $1B into Nokia → Focus: AI-powered network infrastructure → Signal: AI + Telecom convergence is REAL Why it matters: Biggest validation yet that edge AI needs telecom networks. 🤔 WHY THIS MAKES SENSE: Nvidia's need: → Dominates AI chips ($2T valuation) → But AI must move from cloud to EDGE → Edge = telecom networks → Nvidia doesn't do telecom Nokia's assets: → O-RAN technology leader → 5G/6G infrastructure → Global operator relationships Together: → Nvidia GPUs at cell towers → Real-time edge intelligence → $100B+ market unlocked 🚀 WHAT THIS ENABLES: 1. AI-Powered Networks → Self-optimizing in real-time → 40-50% efficiency gains → Zero-touch operations 2. Edge AI at Scale → AI processing at 100K+ cell sites → <10ms latency → Autonomous vehicles, robotics, AR/VR 3. 6G Foundation → AI-native architecture from day 1 → Being built NOW for 2030 launch 📊 THE BIGGER RACE: Partnerships forming: → Nvidia + Nokia ✅ → AWS + Ericsson → Google + Samsung → Microsoft + ??? The pattern: Hyperscalers + Telecom vendors = New normal Why NOW: → O-RAN deployments accelerating → AI workloads moving to edge → 6G standards starting → Enterprise private networks exploding 💡 INDUSTRY IMPACT: Operators: ✅ Better network optimization ✅ Edge computing platform ✅ New revenue (AI inference) ⚠️ Risk: Becoming "dumb pipes" Nokia: ✅ $1B + Nvidia partnership ✅ AI credibility boost ⚠️ Risk: Execution challenges Nvidia: ✅ 100K+ new edge locations ✅ Beyond data centers ⚠️ Risk: Telecom is slow/complex Competitors (Ericsson, Huawei, Samsung): 🚨 Need hyperscaler partnerships NOW 🚨 Can't compete on AI chips alone 🎯 THE 3 BIG SHIFTS: 1. Cell Towers = AI Nodes → Every site becomes edge compute → Mainstream by 2026-2028 2. Telecom = Platform → Not selling connectivity → Selling "AI inference as a service" 3. 6G = Different Game → Chip makers + cloud + AI companies involved → Not just traditional telecom vendors ⚠️ THE UNCOMFORTABLE QUESTION: If Nvidia gets deep into networks... Learns the business... Has the AI chips... The operator relationships... Could they bypass operators entirely? Nokia got $1B today. But did operators just let Nvidia inside the castle? THE BOTTOM LINE: This $1B isn't about networking equipment. It's about control of the AI edge infrastructure. The companies that control where AI runs Will control the next $1 Trillion market. Nvidia just made their move. Who's next? Your take? → 💪 Smart move by both companies? → 🚨 Threat to traditional telecom? → 🤔 Too early to tell? Drop your thoughts 👇 Join my Free 5G/6G Learning Free whatsapp Channel : https://lnkd.in/gerTY-kr ♻️ Repost this to help your network get started ➕ Follow Nitin Gupta for more

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

Last week in AI revealed a structural shift that many people are missing. For the past two years, the conversation has centered on one question: who has the best model? But the latest announcements suggest something deeper is happening. AI is becoming infrastructure. OpenAI: -- released GPT-5.4, its newest frontier model designed for complex professional tasks. On benchmarks, it scored 57.7% on SWE-Bench Pro for software engineering, 82.7% on BrowseComp, and 92.8% on the GPQA Diamond science benchmark. -- The company also introduced Codex Security, an AI agent designed to detect software vulnerabilities, and -- launched ChatGPT for Excel, which allows users to analyze spreadsheets using natural language while connecting to financial data providers like FactSet and Moody’s. At the same time, the competition is accelerating on efficiency and cost. Google released Gemini 3.1 Flash Lite, designed to deliver responses about 2.5× faster and generate output roughly 45% faster than earlier Gemini models, with pricing starting at $0.25 per million input tokens. Alibaba also released Qwen 3.5 small models ranging from 0.8B to 9B parameters. In some benchmarks, the 9B model reportedly outperformed systems with more than 120B parameters, highlighting how efficiency is becoming a competitive frontier. But the biggest signals this week came from infrastructure. Nvidia introduced AI models designed to monitor and manage telecom networks, helping detect failures and automate network operations. Huawei announced a new AI-native network architecture that includes agent layers capable of automating telecom management, with forecasts suggesting that up to 15% of network decisions could be handled autonomously by AI agents by 2028. Governments are now responding as well. The White House announced a Ratepayer Protection Pledge signed by Microsoft, Google, Amazon, Meta, and OpenAI. Under the pledge, companies building large AI data centers must pay for electricity grid expansions instead of passing those costs to residential ratepayers. Meanwhile, research shows AI is already reshaping work: -- A study cited by Scientific American found that developers using AI coding tools produced 27% more merged code changes and nearly 20% more after-hours commits. -- A separate survey of nearly 5,000 developers reported that more than 90% now use AI tools, and over 80% say they improve productivity, though many also reported increased debugging after releases. Adoption globally is still uneven. Japan is investing ¥340 billion in subsidies to accelerate AI adoption as it prepares for a projected labor shortage of 11 million workers by 2040. Yet today only about 8.4% of workers in Japan report using AI at work. The AI race is no longer just about building better models. It’s about controlling the infrastructure around them: energy systems, developer ecosystems, enterprise workflows, and the industries where AI actually runs.

🚦 **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

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

When does it make sense for AI to answer the phone instead of a human? At Fertitta Entertainment, a 40-50% call abandonment rate was a clear signal. The company was receiving 40,000 calls per month for its restaurants. Most were simple inquiries—people who wanted to make a reservation or ask a question. Stuff that most of us handle online, but this customer base wanted to do over the phone. Brian Jeppesen, Fertitta's Director of Contact Center Operations, told me staffing for this many calls was a huge challenge. The solution was a AI voice agent from PolyAI. On day 1, AI handled 87% of these calls. The AI voice agent answered immediately, spoke clearly, and understood most customers. Abandon rates dropped to less than 10%. Revenue capture went up. The customer experience immediately improved. Jeppesen explained customers could still talk to a human if they wanted to or if the AI agent encountered something it couldn't handle. This was a benefit for his customer service reps. Handling the simple, repetitive contacts freed up human agents to focus on calls that needed a human touch. Agent satisfaction increased. Attrition went down, easing the staffing challenge. This strikes me as a great way to blend AI and humans. AI can handle the simple, repetitive contacts that are tedious for agents and drive up staffing costs. Humans are freed to handle contacts that require real human skills like warmth, empathy, and advocacy. *** 💡Do you have a story like this to share? I'm looking for case studies for my newest keynote, Humanizing HELP: Human customer service in an age of AI. Leave a comment or direct message me.

How AI Teaches Telecom Networks to Fix Themselves (Before You Even Notice!) What if your mobile network could predict congestion and fix it - before your video call starts buffering? With machine learning, it's today's reality. Let me break down how telecom giants are using AI to turn reactive networks into proactive problem-solvers: 🔍 Step 1: The Network's Memory Every cell tower constantly logs: → Regional usage patterns → City-wide traffic flows → Individual site performance This creates a "normal" baseline - the foundation for all AI predictions. 🚨 Step 2: Spidey-Sense for Anomalies When a concert suddenly overloads a downtown tower, ML spots the irregularity instantly. Unlike traditional threshold alerts, it understands context - distinguishing between a festival and a DDoS attack. ⚡ Step 3: Instant Prescriptions The AI doesn't just diagnose - it treats: ✓ Dynamically shifts bandwidth ✓ Spins up virtual network functions ✓ Optimizes antenna tilt angles Some systems implement changes automatically - no coffee break needed for engineers! 📈 Step 4: The Learning Loop Every prediction gets scored: ✔ Was the forecast accurate? ✔ Did the fix work? ✔ How to improve next time? This turns each event into a masterclass for the AI. Why This Matters: • Fewer service disruptions • More efficient resource use • Customers who never know there was a problem to begin with The future? Networks that don't just adapt - but anticipate. To learn about AI & 5G, visit - https://lnkd.in/eT-ZZyrP #TelecomInnovation #AI #MachineLearning #Networks #TechTransformation

Telecom Sector Update: October 2025 - Rapid Transformation: The global telecom industry is experiencing a dynamic shift, with AI, automation, and cloud-native networks driving innovation and operational efficiency. The move to 5G and even early steps towards 6G are enabling new business models, especially with private networks for enterprises and advanced IoT deployments. - Market Headlines: Telecom companies worldwide are reporting revenue growth (4.3% to $1.14 trillion globally), with India standing out for network expansion and rural connectivity efforts. Notably, India has reached 75% of its "100% telecom saturation" mission, consolidating leadership through massive investments in infrastructure. - Financial Trends: Operators are under pressure to raise mobile tariffs as investment in network technology outpaces revenue in highly competitive markets. Yet, telecom stocks remain attractive due to their stable, recurring income bolstered by fiber and 5G rollouts. - Leading Indicators:     - Subscriber Base: India remains the world's second-largest telecom market with over 1.2 billion subscribers, and nearly 996 million broadband users as of September 2025.   - Data Trends: Monthly data usage per user leads globally, powered by surging demands for video, gaming, AR/VR, and AI-driven services.   - Network Expansion: Accelerated rollout of 4G densification, fiberization for 5G backhaul, and new broadband growth in tier-2/3 towns are significant.   - Policy Developments: New cybersecurity rules, spectrum auctions, and Digital India policy pushes are shaping the regulatory landscape. - Tech and Business Evolution:     - AI Adoption: Over half of telecom companies have implemented AI at scale, with another 37% actively scaling up. Generative AI is cited as a long-term growth engine by 65% of Indian CXOs.   - Cloud and Edge: Cloud-native networks are the new normal, boosting agility, service assurance, and digital transformation for enterprise customers.   - Sustainability: Green networks and sustainable business practices are coming to the forefront, as the sector aligns with global environmental goals. - Risks & Outlook: Key risks for 2025 include regulatory shifts, cybersecurity threats, and adapting to new business models and spectrum management. Market analysts expect telecom's robust performance to continue fueling a bull run in Indian equities. Conclusion:   The telecom sector is at a crossroads—technology, investment, and sustainability are shaping its future. Markets like India, Turkey, Europe, and North America stand out for innovation and growth. Forward-looking indicators such as rural adoption, ARPU increases, swift 5G rollout, fiber penetration, and strategic AI deployment will point the way ahead. #TelecomTrends #5G #6G #AIinTelecom #DigitalIndia #TelecomNews #IndustryInsights #Connectivity #NetworkInnovation