How Telcos Use AI for Business Growth

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? 👇

🚀 Google Cloud Next ’25: Why CMT & IT Services Firms Can’t Afford to Wait on AI Just witnessed the future at #GoogleCloudNext—and for Communications, Media, Technology, and IT Services, the implications are massive. Here’s what matters: 📡 For Telecom & Technology: - AI-powered network optimization (hello, Ironwood TPUs!) means smarter traffic routing, reduced latency, and predictive maintenance at scale. - Agentic AI (Agentspace) can automate customer service resoluton—think self-healing networks + hyper-personalized subscriber experiences. - Google’s new Open WAN architecture redefines global connectivity—delivering carrier-grade reliability, AI-driven traffic optimization, and seamless multi-cloud integration for enterprises scaling distributed workloads. 🎬 For Media & Entertainment, EdTech & Publishing: - Gemini 2.5’s multimodal prowess = faster content tagging, AI-driven editing, and even synthetic media creation (with ethical guardrails). - Vertex AI’s new tools can slash costs for metadata generation, localization, and personalized content delivery. - Vue’s multimodal AI can revolutionize content generation —automating video summarization, dynamic ad copy variants, and even real-time multilingual localization at scale—all while maintaining brand guardrails. 💻 For IT Services & Tech Firms: - Developer productivity just leveled up: Gemini Code Assist reduces legacy modernization time, while Agent2Agent protocols enable autonomous DevOps. - $75B infra investment = a scalable foundation for building and managing AI-first solutions for clients. The Bottom Line: AI isn’t just a feature—it’s the new operational backbone for our industry. The winners will be those who: 1. Drive tangible business value with Agenti AI. Use Agentspace not just for efficiency or process automation, but to transform Service Pools to Software. 2. Embed AI into all major functions (e.g., revenue acceleration, sales productivity, churn prediction, media asset mgmt, content creation). 3. Upskill teams on Google’s AI stack—now. Thoughts? How’s your org preparing? 👇 #AgenticAI #CloudComputing Cognizant #CMT Ravi Kumar S Surya Gummadi Ganesh Ayyar Prasad Sankaran Annadurai Elango Brian DeLarber Sandra Notardonato Thea Hayden Antonella Bonanni Vijay Anand Ankur Singh Deepak Sharma Anand Srinivasan Narendar Prabhakaran Neeraj Deuskar

What if everything we knew about telco marketing was wrong? Last month, Totogi worked with a brave Tier-1 operator that agreed to test a radical hypothesis: Could it outperform decades of traditional marketing wisdom? The results shocked everyone—including us: Not the usual 1-2% improvements that make for nice quarterly reports. We're talking about a 10% revenue jump in 12 weeks. A 60% surge in offer acceptance. Data usage more than doubled. The secret? We eliminated the biggest bottleneck in telco marketing: human meddling. Think about it. Right now, your marketing teams are building campaigns the same way they did in 2010. Analyzing data. Designing offers. Running them through approval chains. By the time these carefully crafted campaigns reach your subscribers, the opportunity has evolved or vanished. But what if your marketing could move at the speed of your network? That's what we proved is possible. Our AI system creates and deploys personalized offers in minutes, not months. It's like giving each of your millions of subscribers their own personal marketing team, working 24/7 to find the perfect offer at the perfect moment. Here's the fascinating part: The more we got humans to trust the AI, the better the results became. Not because humans aren't brilliant at marketing—they are. But because even the best human marketers can't analyze millions of behavior patterns in real-time. I'm curious: What's holding your team back from embracing AI-driven marketing? Is it trust? Technology? Or something else entirely?

This is pretty cool. Anyone looking for the much-anticipated (but less often observed) gen AI productivity boost should read about our work with Deutsche Telekom, where an AI-powered “personal trainer” helped thousands of employees improve customer experience, resolution rates, and more. Deutsche Telekom is a powerhouse, with millions of customers and thousands of call center and field service agents around Germany. But as they grew, the company found it hard to maintain the high bar they set for their employees, with ripple effects across customer experience, operational efficiency, and employee satisfaction. Traditional learning programs weren’t cutting it—the question was, how to customize learning at scale? Enter gen AI. By teaming up with Deutsche Telekom—from the c-suite to the call centers—our colleagues helped build and launch a capability-building engine that could individually upskill service agents and better meet customer needs. Leveraging our in-house frontline AI product, we used millions of pieces of call data, field service notes, customer feedback and more to identify skill gaps and provide tailored learning interventions. The results so far are clear—a 14 point increase in customer satisfaction and a 10 percent increase in first-time resolution rates year over year, with more I’m sure to come. But the real lesson? The technology is essential, but not enough. It’s the people and organization that will determine success. You can find the whole story here: https://lnkd.in/gYsymvXj Huge congrats to Deutsche Telekom, and thanks to Julian Raabe, Nicolai von Bismarck, and all the colleagues who helped bring this work to life.

I am a huge proponent of telecom operators leveraging AI not just for internal use but also for creating and supporting so-called "AI factories." Here's what I mean by "AI factories"... Telecom carriers can leverage their extensive connectivity, edge computing, and cloud infrastructure to power AI-driven services at scale. Integrating AI with their 5G and fiber networks allows them to process vast amounts of real-time data at the edge, enabling low-latency AI applications such as autonomous systems, smart cities, and industrial automation. These AI factories would combine network slicing, private 5G, and multi-access edge computing (MEC) to provide enterprises with dedicated, high-performance AI processing capabilities. Additionally, operators can monetize their AI platforms by offering AI-as-a-Service (AIaaS), facilitating model training, inference, and automation across various industries. This effectively transforms their networks into intelligent AI hubs. While some operators await the next big 5G breakthrough to fuel growth, AI is already poised for advancement—ready for visionary telecom executives to harness their networks' capabilities and drive its next evolution. #AI #5G #Telecom #EdgeComputing #DigitalTransformation