SHEIKH SAMEER’s Post

Telecom and AI in 2025 🔮 In his latest article with The Fast Mode, OXIO CTO Adil Belihomji explores how AI is set to transform telecom infrastructure in 2025. From boosting network intelligence to enabling personalized customer experiences, Adil highlights the immense potential of AI to optimize efficiency and redefine telecom as we know it. Explore the article to learn how AI-driven innovation is unlocking the next era of Telecom-as-a-Service: https://bit.ly/4hm0b3D #TaaS #AI #Telecom #ThoughtLeader #OXIO

While many focus on AI's role in customer service chatbots or network monitoring capabilities, the underlying changes run deeper: AI will help alter how telecom services are…

Curious about how AI is reshaping the telecom landscape? Check out this #amazing SDxCentral article by Taryn Plumb, where Group President of Technology and Head of Strategy at Amdocs, Anthony Goonetilleke, shares insights about customer service, and network optimization, despite industry challenges. Learn all about the rapid cadence of #AI's evolution, and the paradigm shift that must happen as we embrace these technologies! > https://lnkd.in/ea8zS3Gp #AI #Telecommunications #TechInnovation #GenerativeAI #GenAI

AI is the next frontier for business, including telecom. And it’s so new, it’s important to map out the challenges to use it successfully. Read on to find out how: https://hubs.ly/Q02MJmXV0

Creating AI-based value in the telecoms/ICT sector has become a necessity not only to assert a competitive edge, but also to retain a customer base that no longer wants to wait on the sidelines of technological evolution.  With an Internet adoption rate of over 64% worldwide (April 2023), according to Statista, this ecosystem is mature enough to leverage AI across both operators/ISPs and end-customers.

While many focus on #AI's role in customer service chatbots or network monitoring capabilities, the underlying changes run deeper: AI will help alter how telecom services are… Read the full article by OXIO's Adil Belihomji below: #trends2025 #generativeai #ai #autonomousnetworks #mvno #ml #chatbot

GenAI applications are redefining how data flows, demanding networks that can handle asymmetrical, high-volume, multimodal traffic. CSPs have the opportunity to not only upgrade their own infrastructure but also collaborate with enterprises to design networks optimized for edge AI. CSPs and enterprises can redefine connectivity with VeloCloud SD-WAN as the intelligent overlay that brings it all together. https://brcm.tech/3VVxNNx

Say goodbye to network bottlenecks and hello to smarter resource allocation. Discover how AI can revolutionise telecom network operations. Here are sample ML algorithms for capacity planning that can proactively manage peak loads, minimise downtime, and enhance operational agility. #AI #Telecommunications #CapacityPlanning

AI Adoption in Telecoms and Why it’s Hard (Hint: It’s based on specific needs💡) Back in May Paul and Azita Arvani, together with Monica Paolini discussed the potential opportunities and challenges of using AI in telcos. It was a treasure trove of insights, so here’s a takeaway to share with you. Monica: Cost is a top consideration in any assessment. But how do you assess cost tradeoffs? How do you do a cost-benefit analysis to decide what is the best model for you? Paul: The choice may depend on the use case. There are many facets in each network. To reduce energy consumption in the RAN, you may want to use AI to predict traffic and allocate resources. There is a saving opportunity but also a cost. You need to initially select and train a model—you want to avoid continuously training it. Do you need to have the right compute infrastructure to train that model? Or are you happy to train it in a public cloud and accept that you will have to move data from your deployment to a data center outside your network? Operators will have different perspectives. Some will train their models in the central cloud, while others are skeptical. Some have regulatory constraints to meet. The public cloud is quicker and does not require the purchase of super-expensive hardware to train a model only for a limited time. The size of the deployment matters, too. It is a numbers game. The bigger the deployment, the more energy you can save, but also the more models you need to train, and there are costs associated with that. If you have a small operation, the saving margins from optimization are low, especially if you need to use GPUs to train the model. We need to be careful when assessing these factors because the tradeoffs are complex. 🤯 Watch the full discussion here: https://lnkd.in/e_SKDQVA #SparringPartners #AI #Telecoms