AI in Telecom Operations

AI in Telco Won’t Scale if Legacy Stays Telcos continue to announce AI transformation roadmaps. From GenAI in customer service to AI-RAN and self-optimizing networks, the ambitions are clear. Yet across the industry, most of these initiatives remain trapped in pilot mode. The reason is not model maturity or lack of talent. It is legacy infrastructure. A recent survey by Fierce Telecom found that 32% of operators cite legacy systems as the primary barrier to AI adoption. In parallel, Accenture reports that 66% of service providers identify technical debt as the top constraint to modernization. Over half of IT Telco teams spend more than 800 hours annually maintaining aging platforms. That is time diverted from deploying automated pipelines, training models, or integrating intelligent agents into production systems. Legacy showstoppers are happening every day. In 2024, a large Telco group partnered with a top vendor to implement its cognitive SON platform. The objective was to use AI to optimize power consumption, reduce interference, and improve network efficiency by up to 30%. But the project initially failed to scale. The AI system required real-time telemetry, dynamic network configuration access, and external data streams such as energy pricing. Core telemetry data was locked inside proprietary EMS platforms that did not support open interfaces. External data integration was blocked by outdated middleware layers. Configuration workflows still require manual validation due to rigid OSS processes. The model was fully functional, but the infrastructure was not. Only after the Telco replaced key legacy OSS components and re-engineered its data architecture did the AI deployment deliver measurable impact. Across the telecom industry, legacy systems dominate BSS, OSS, provisioning, and assurance layers. These platforms were not designed to support AI inference, real-time feedback loops, or autonomous operations. They were built to enforce transactional integrity, compliance, and control. As a result, they constrain AI deployments in both speed and scope. Enterprise-wide benchmarks reinforce this structural problem. 64% of large organizations still run over a quarter of their operations on legacy systems. In telecom, that percentage is likely higher and far more critical to daily network functionality. AI in telecom cannot scale on infrastructure that was never meant to support it. Until the underlying systems are modernized, even the best-designed models will remain boxed into isolated pilots. The path forward is not just about choosing the right algorithms. It begins with the architectural will to replace what no longer supports execution.

There's one use case for AI agents not being talked about enough: volatile or seasonal industries. Think about what crypto, fintech, travel, and even retail have in common. Their surges in volume (some random, some not) and customer inquiries make it extremely challenging for traditional CX systems to keep up. But where legacy systems struggle, AI systems step up. Here's how: 1. Scalability When inquiry volumes spike, AI agents can handle the influx without missing a beat. There are no delays from hiring surplus human agents to handle more volume, making AI agents both cost- and process-efficient. 2. Consistency Whether it's 1K or 1M customer inquiries, AI agents guarantee the same level of accuracy and precision every time. Humans need downtime, AI doesn't. 3. Prioritization Customer inquiries come with varying degrees of complexity. While AI agents take care of the low-hanging fruit and repeatable tasks, human agents can focus on the high-touch cases that demand personal attention. Take Coinbase’s customer support, for example. They handle $226B in quarterly trading volume in 100+ countries. Their margin of error is slim, and CX mistakes could cost billions. Instead of leaning on human CX alone, they use AI agents to: • Handle thousands of messages per hour • Reduced customer service handling time • Improve search relevance for their help center The enterprises we work with at Decagon experience the same benefits using AI customer service agents—scalable support, no gaps in performance, and higher customer satisfaction. Just because your industry is volatile doesn't mean your CX should be.

🛎️ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭: 𝐓𝐡𝐞 𝐄𝐧𝐝 𝐨𝐟 𝐋𝐨𝐧𝐠 𝐖𝐚𝐢𝐭 𝐓𝐢𝐦𝐞𝐬? “Your call is important to us. Please stay on the line…” We’ve all heard that line—and rolled our eyes. But now, something is changing. And fast. Welcome to the era of AI-powered customer support. Companies are no longer relying only on traditional ticketing systems. They’re deploying smart AI agents that don’t just answer queries—they understand them. Here’s how: ✅ Intercom Fin — A GPT-4 powered support assistant that resolves 50%+ queries without human handoff. ✅ Forethought — Predicts intent and delivers relevant answers before customers even finish typing. ✅ ChatGPT APIs — Powering custom chatbots that respond with context, tone, and accuracy. The benefits? ⚡ Instant replies ⏱️ 24/7 support 🎯 Personalized responses 💸 Lower operational costs But here's the kicker: AI isn’t replacing human support—it’s amplifying it. → Complex cases still go to real agents. → Bots handle the repetitive stuff. → Everyone wins. As expectations rise and patience drops, the real question isn’t if companies will adopt AI in support—it’s how fast. Would you be comfortable being helped by a bot, if it resolved your issue in 60 seconds? Do let me know in the comments! 👇 #CustomerExperience #GenerativeAI #CustomerSupport #Automation #ChatGPT #AITrends #TechInnovation

“Let me explain the issue again…I was saying…” Does this sound familiar? We’ve all been there: stuck on the phone or chat, explaining the same problem to a new support agent for the third, fourth, or fifth time, feeling unheard. But customer service isn’t just about solving problems. It’s about making people feel heard. Yet, far too often, support interactions feel robotic, cold, and disconnected. You’re bounced between departments. Asked to repeat yourself again and again. Given a ticket number instead of a real solution. And the worst part? No one seems to remember your last conversation. This isn’t just inefficient; it’s deeply frustrating and exhausting, and it shows a lack of empathy. Customer service must go beyond transactions. It should tap into attentive empathy, truly listening to customers, acknowledging their frustrations and cognitive empathy, and offering relevant solutions based on past interactions and emotional context. So how do we do that at scale? OpenAI’s latest update is a step in that direction. ChatGPT can now remember past conversations across sessions. This simple upgrade unlocks a smarter, more empathetic future for customer service. Imagine this: • Your support agent already knows what you’ve been through • They pick up right where you left off • They tailor responses to your preferences and pain points This is what modern, emotionally intelligent service should feel like. And the data speaks volumes: 🔹 76% of customers say repeating themselves is their #1 frustration 🔹 81% prefer brands that personalize the experience With AI memory in play, customer service teams can now: • Offer personalized support journeys • Reduce friction in every interaction • Proactively engage based on past pain points • Build long-term trust through seamless continuity For businesses, this means: • Smarter, AI-powered systems that improve with every touchpoint • Consistent journeys that feel human even when powered by machines • Stronger retention through empathy-led engagement If you’re a forward-thinking company, here’s what to do: • Invest in AI tools with conversational memory • Redesign support flows to feel continuous, not fragmented • Train agents to collaborate with AI as empathy amplifiers • Prioritize data transparency and privacy to build lasting trust Because when customers feel understood, they don’t just stay, they advocate. #AI #ChatGPT #customerexperience #CX #KSA #SaudiArabia

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

🚦 **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

Want a great AI in customer experience case study? Look no further than AT&T. Here's what happened... AT&T just reported that its AI-driven incident-management system prevented 3.1 million unnecessary technician visits and reduced customer downtime by more than 12 million hours over the past year. These numbers are big and grab headlines for sure, but let's break this down into financials, shall we? (PS - my estimates, not anyone else's.) When I researched industry benchmarks to roll a telecom or fiber truck I came to roughly $150 to $300 in narrower field-ops estimates, with broader all-in estimates ranging from about $150 to $1,000 depending on labor, travel, repeat visits, overhead, and opportunity cost. If these numbers are still accurate, plus taking a swag at avoided contact center calls and other operational costs associated with the reported downtime number, then I would estimate savings to be between $850MM - $1.3B annually. This was a capital-grade operating system investment. AT&T started building the end-to-end incident-management system in 2017, launched it for broadband fiber in 2018, expanded it in 2019, added proactive customer notifications in 2021, added generative AI features in 2022, and added AI agents in 2025. The platform also reorganized about 10 petabytes of data, uses technologies including MongoDB, Azure, Databricks, and Snowflake, and includes more than 30 AI models for predicting and diagnosing network issues. So this likely cost $200M - $350M all-in over the life of the program, and perhaps more depending on how AT&T allocates shared platform, cloud, network-ops, and employee costs. I also modeled annual ongoing run cost at $40M to $90M. So this is likely a 10x-plus annual return after the platform is mature. This just might be the model for service AI than starting with the contact center. AT&T is using operational data to prevent the failure, identify the cause and communicate proactively, rather than waiting for a customer to complain and then automating the response. So the takeaway is that the highest-value customer-service AI may sit upstream from service. Companies should be connecting product, network, fulfillment and operational signals to customer context before spending another dollar on conversational automation. The best contact is often the one the company prevents in the firstplace. Now this begs the question, "What's the value prop of Voice AI solutions like Decagon, Sierra, and others when the customer isn't even calling in?" Feel free to challenge my numbers, but I think I've got them right. If you have a sharper pencil, let me know what you think in the comments. Thankfully, I'm an AT&T customer. Sorry, not sorry, Verizon. Can you hear me now? 😂 #customerexperience #ai #voiceai

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?

This is a follow up of my previous post on #AgenticAI. The telecommunications industry is at a breaking point. For years, the narrative has been a defensive one—how do we avoid becoming a commoditized dumb pipe? That question is now obsolete. The real question is: How do we become the intelligent, indispensable hub of our customers' digital lives? The answer is a fundamental reinvention of our operating model. It's time to move beyond being a digital company that uses AI and commit to becoming a true AI-Native Telco. This isn't just a technology upgrade; it's a new philosophy. It's a shift from being reactive to proactive, from mass-market to a #segmentofOne, from a network that simply connects, to one that anticipates, reasons, and acts. The strategic blueprint to achieve this is built on two powerful, interconnected pillars: #Pillar1: The Agentic Architecture – Building the Brain Architecting a new central nervous system for the telco. This agentic model consists of four intelligent layers: A Trusted Foundation: AI-ready sovereign infrastructure with a robust security mesh and an API-first design. A Reasoning Core: Moving from data lakes to #KnowledgeLakes, where continuous learning and reasoning chains transform raw data into strategic actionable wisdom. An Agentic Brain: A new intelligence layer where specialized AI agents orchestrate everything from network resource allocation and predictive maintenance to individual customer interactions. An Interactive Experience Layer: Smart, personalized channels that empower both customers and employees, making every interaction seamless and context aware. #Pillar2: The Value Realization Office (VRO) – The Economic Conscience Brilliant technology is useless without a direct line to business value. The biggest risk in any AI transformation is creating #islandsofinnovation in an #oceanofexpense —technically impressive projects that become multi-million-dollar write-offs. The #VRO as I wish to call it is a possible antidote. It is a dedicated, cross-functional team that acts as an economic conscience. Its sole #mandate is to ensure every single AI initiative is rigorously tied to a P&L impact. The VRO builds the business case, tracks the ROI in real-time, and has the authority to stop projects that don't deliver tangible value. It's what turns AI from a #costcenter into the company's most powerful engine for #profitable growth. This dual-pillar strategy should be THE commitment to building the future. Investing in AI is not the same as any other Capex decision; it should be for rebuilding the entire organization around it to deliver a level of service and efficiency that was previously unimaginable. The journey is ambitious, but the destination—true market leadership and unparalleled customer value—is clear. #Disclaimer: Part of the content & visuals are AI Assisted. Obviously 😊 #AINativeTelco #AIStrategy #CIO #Leadership #ROI #AgenticAI

AI is fundamentally reshaping how telecom and media companies engage with their customers, moving from reactive support to predictive, personalized experiences. In my recent interview with @Unite.ai, I discuss how Agentic AI, predictive analytics, and NLP are transforming contact centers into proactive client experience hubs. From overcoming legacy integration challenges to creating omnichannel continuity and improving operational efficiency, the opportunities are immense.   At Persistent, we combine deep expertise in AI, cloud, and data with a strong understanding of industry-specific needs to help telecom and media companies modernize customer engagement. Our recent acquisition of Starfish Associates has further strengthened our capabilities in contact center automation and unified communications.   Read the complete interview to learn how we’re helping clients harness AI to build smarter, more agile customer engagement models: https://lnkd.in/e6ViTWZr   #Telecom #Media #CustomerExperience #AgenticAI