AI in Telecom Operations

Testing and piloting AI for sales and marketing can be frustrating. That’s why Jomar Ebalida and I came up with the practical AI roadmap for marketing and GTM ops pros. This roadmap helps you figure out where to start, what to focus on, and how to scale AI initiatives in a way that’s grounded in operational reality. It’s structured in 3 phases: PREP: Evaluate your organization’s current state across data, tools, team skills, and funnel performance. PILOT: Select and test AI use cases based on your actual readiness data. (Diagram shows samples) Avoid guessing by letting the assessment drive decisions. ACTIVATE: Scale the pilots that show promise and embed them into core processes. Here are select projects worth walking through: 🔹 AI Readiness Assessment This project includes evaluating data quality, the state of your CRM, the maturity of your tech stack, and your team’s readiness to work with AI tools. It also includes a bowtie funnel analysis to help identify where your customer journey is breaking down. The outcome is a clear picture of which AI use cases are both valuable and feasible for your team to pursue. 🔹 AI SDR Agent: Outreach and Prospecting This agent is designed to support outbound sales by identifying high-potential accounts, generating personalized outreach messages, and helping SDRs scale without sacrificing relevance. It can help teams boost pipeline without overloading headcount. 🔹 AI QA and Compliance: Brand, Legal, Regulatory This workstream ensures that every piece of AI-generated content or decision logic meets the necessary internal standards. It supports brand consistency, regulatory requirements, and risk mitigation. This process should run in parallel with pilots and activations to ensure safe implementation. 🔹 AI Agents for Ops: QA Checks, Routing, and Campaign Setup This includes AI agents built to handle operational tasks such as verifying UTM links, auto-routing requests, or creating campaign templates. These agents are ideal for improving workflow speed while reducing manual errors and team bottlenecks. At the foundation of all of this is change management. Each phase of the roadmap includes a focus on enablement, training, adoption, metrics, and governance. Tools don’t generate value unless people are set up to use them properly. Which parts resonate with you? What would you change or add? PS: To learn more & access templates, subscribe for free to The Marketing Operations Leader Newsletter on Substack https://lnkd.in/g_3YC7BZ and to Jomar's newsletter at bowtiefunnel(dot)com. #marketing #martech #marketingoperations #ai #gtm

The AI-RAN Taking Shape I'm thrilled to announce our latest research contribution that fundamentally transforms how we design, deploy, and test key functionalities of cellular networks. Our new paper "ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN" represents three major industry firsts: ⚡ First-Ever Automated Scheduler Generation: We've developed LLM agents that automatically convert research papers into functional code, generating 18 different scheduling algorithms directly from academic literature using OCR and AI. No more months of manual implementation in ns-3 or Matlab! Automatically generated schedulers are automatically deployed in a live network as dApps through a CI/CD pipeline - without the need to change a single line of code in the gNodeB implementation (CU or DU);  ⚡ Intent-Based Scheduling: Network operators can now express high-level requirements in natural language ("prioritize users with bursty traffic") and ALLSTaR automatically translates these into optimized scheduling policies according to operator’s intent. ⚡ World's First O-RAN Compliant AI-RAN Testbed: All validation conducted on X5G with AutoRAN, production-grade, multi-vendor 5G infrastructure with GPU acceleration, AI-for-RAN and AI-and-RAN capabilities, demonstrating real-world viability at scale. This work also introduces a methodological paradigm shift: instead of implementing one algorithm at a time, we can now systematically evaluate a vast body of scheduling literature in production-like environments. We're moving from manual, months-long integration processes to automated, intent-driven networks that adapt in real-time. This is the Open RAN and the AI-RAN vision - and a pathway toward 6G that builds on our national strengths and open ecosystem. Full paper: https://lnkd.in/eTNWPNRR Open6G www.open6g.us #ORAN #AIRan #OpenRAN #5G #WirelessResearch #AI #MachineLearning #Telecommunications #Research Our brilliant team: Maxime Elkael Michele Polese Reshma Prasad Stefano Maxenti Office of the Under Secretary of Defense for Research and Engineering NSF AI-EDGE Institute National Telecommunications and Information Administration (NTIA) Qualcomm

NVIDIA and Infosys focus on telecom... The blog post discusses how Infosys leveraged NVIDIA's NeMo Retriever and NIM (Neural Inference Microservices) to enhance the efficiency and accuracy of telecom Network Operations Centers (NOCs) through generative AI. Infosys developed a smart NOC solution that uses AI-powered chatbots for network troubleshooting, reducing downtime, and improving customer service. The solution involved creating a vector database of network-specific documents, optimizing embeddings, and reranking for accurate and fast responses. The implementation of NVIDIA's technology significantly reduced latency by 61% and improved accuracy by 22%, enhancing the overall performance and reliability of the NOC systems. #nvidia #telecom #infosys https://lnkd.in/gp85zTUa

Struggling to hit sales targets with a lean ops team and tighter budgets? There's a smarter way to drive conversions. For lean sales ops teams, every dollar and every minute count. Scaling sales with constrained resources demands strategic focus.  Relying solely on manual processes or guesswork leaves significant revenue untapped, especially when competing with larger teams. This is where AI becomes the ultimate force multiplier. Modern AI tools are transforming how sales ops maximize efficiency and conversion without needing massive headcount. AI empowers focused efforts through three key areas. ✔ Predictive analytics for lead scoring ensures teams target the highest-potential prospects. ✔ Personalized outreach automation enables hyper-relevant communication at scale. ✔ Workflow optimization automates administrative tasks, freeing sales reps to sell. At Software Finder, our own sales ops embodies this approach. We leverage an intelligent lead scoring model that processes historical conversion data and engagement signals. This ensures our team prioritizes the warmest leads with surgical precision, leading to significantly higher conversion rates and a more efficient sales cycle. This demonstrates how smart technology consistently outperforms sheer size. For leaders, this approach unlocks a pathway to consistent revenue growth, even with slow resource scaling. It elevates the sales focus from manual effort to strategic intelligence, ensuring every action contributes directly to conversion. This is precisely how lean teams outmaneuver competitors in today's market. What AI strategies are you deploying to maximize your sales ops conversions with a limited budget? Share your insights.

💡 Application of AIML for the New Radio air interface 3GPP TR 38.843 technical report explores how AI/ML can be integrated into 5G networks to enhance various aspects of the air interface, including: - Channel Estimation: Improving the accuracy and efficiency of channel estimation processes. - Beam Management: Enhancing beamforming and beam-tracking techniques to ensure reliable communication. - Interference Management: Utilizing AI/ML to predict and mitigate interference in the network. - Resource Allocation: Optimizing the allocation of network resources to improve overall performance and efficiency. Specifically for beam management, AI/ML have advantages like: - Deep Learning - adaptively learns the features of the channel in support of reliable beam-management - Parameters of Deep Learning models capture the high-dimensional features of the propagation scenario, such as blockage locations and shapes in support of reliable beam-management - Deep Learning can be utilized to extract the nonlinear features inherent in the angular domain for implementing super-resolution beam-prediction - Deep Learning is capable of modeling complex nonlinear factors for beam management whereas mathematical models usually ignore these factors for simplicity. #AIRAN #AI #5G

There is a lot of chatter today about what isn’t working anymore in outbound sales. But what about what *is*? What are the channels and strategies that are bearing fruit that could be executed even better using AI? I’ll start with dialing. HubSpot’s recent Sales Trends Report shows phone calls to be the most effective channel for cold outreach. Yet, connect rates are below where they had been historically. It’s likely because buyers' BS filters are at all-time highs, while reps struggle with the disjointed calling workflow. So how do we take a strategy that is working, but improve the approach so it performs even better for our teams? Here are three core areas where the dialing experience and workflow can be improved thanks to genAI and AI voice: 1- Reaching the right prospects: connect with more phone-ready leads by having AI sort through intent and engagement signals to ensure outreach happens at the right time, to the right person. 2- Leveraging the stacking effect: use AI Agents to warm up lead lists through pre-call email and social engagement, so that way your dials are more likely to land with an audience who already has some familiarity with you and your brand. 3- Maintaining relevance at scale: use generative AI to create custom call scripts using unique data on each of your leads, while leveraging AI voice to make tailored voicemail drops in the voice of your reps. Both establish relevancy without sucking up bandwidth. What other channels and strategies are you seeing improved by introducing AI?

Transforming Network Management: The Power of Marvis In a recent discussion, Shirley Wu Sr. Director of Software Engineering leading the Marvis data engineering team since 2018 and Jessica G., Sr. Consulting Sales Engineer, delved deep into the world of Marvis, @Juniper Networks' #AINative virtual network assistant. Their conversation offers a comprehensive exploration of Marvis, from its evolution to the collaborative efforts across firmware, hardware and customer support teams, and the groundbreaking application of AI and ML in network management. Shirley sheds light on the developmental journey of Marvis, from its initial role of answering simple queries to its advanced capability of proactively identifying and troubleshooting network issues, all thanks to its sophisticated AI and ML capabilities. The discussion also highlights the collaborative synergy across data science, customer support, cloud infrastructure, firmware, and hardware, showcasing the multidisciplinary approach powering the success of Marvis. In my recent blog, I share more on how Marvis harnesses deep learning and natural language processing to help IT teams deploy and operate networks on par with human IT domain experts.  This transformative technology not only reduces support tickets improving customer experience, but accelerates troubleshooting to maximize operational efficiency Through evidence of transformative impact on network management, Marvis stands as a testament to Juniper's commitment to changing the paradigm from just managing network elements to managing the end to end client to cloud user experience. To explore further, read the full article here:  https://juni.pr/3X5nrvM #AIInNetworking #JuniperNetworks #Marvis