Amazon Web Services (AWS) is powering the future of telecommunications. Leading communications service providers (CSPs) run more workloads on AWS than any other cloud provider. By partnering with AWS, CSPs not only accelerate their data center consolidation and migration to the cloud, but monetize their path to 5G by offering customers next-generation capabilities in mobile edge computing and IoT. With a rich catalog of cloud-deployable partner solutions, AWS enhances the customer experience through machine learning and AI and accelerate business process automation to drive operational efficiency. Now, more than ever, CSPs can leverage the most advanced technologies on the market to create new revenue streams and focus on what sets their business apart.
Benefits
Accelerate digital transformation and data center consolidation
Accelerate digital transformation and data center consolidation to optimize performance, lower IT costs, strengthen security posture, and free up investment capital.
Monetize the path to 5G with mobile edge computing and IoT
Leverage 5G and mobile edge computing to bring next-generation capabilities to smart devices and networks, enabling monetization of various IoT applications.
Enhance the customer experience with machine learning and AI
Improve the customer experience by providing exceptional service and personalized customer care enabled by machine learning/AI and predictive analytics.
Automate business processes to drive efficiency
Engage a rich catalog of technology partner solutions to automate critical business processes and drive operational efficiency.
Explore use cases
Cloud Migration
Secure migration to the cloud is a crucial first step in digital transformation. With the increasing number of mergers between CSPs, acquisitions of media and technology companies, and OTT providers cutting into profit margins, telecom providers are dealing with consolidation on a massive scale and need solutions to accelerate this transition and free up capital to invest in innovation.
Internet of Things (IoT)
Telecommunications providers must deliver robust network solutions to enterprises and consumers alike that enable a seamless experience such as connected homes, autonomous vehicles, robotic surgery and advanced gaming at the mobile edge. To do this, CSPs are investing heavily in IoT and 5G edge use cases to promote their network as the best 5G experience on the market.
Machine Learning and Artificial Intelligence
Network outages can be extremely costly to a CSP and minimizing them is the key to success. In addition, predicting downtime, optimizing bandwidth usage, and resolving issues is more critical than ever. Machine learning and AI can help prevent issues, improve efficiency, create personalized experiences, and drive call center excellence.
Business Applications
CSPs must navigate a complex technology landscape while migrating to the cloud. Many must maintain legacy systems to support current customers while preparing for the rollout of 5G, as well as optimizing business processes to fund innovation. Systems such as billing, provisioning, and network operations are critical to business continuity and customer satisfaction.
Case studies
See how leading telecommunications companies are partnering with AWS to accelerate innovation, enhance customer experience, and monetize their business.
Videos & Webinars
Geeta Chaudhary, Director, Telecom Professional Services does a year in review that walks through regional successes from 2020 supporting Telecom strategic pillars. It will be a journey through various case studies and digital transformations inclusive of AI/ML, IoT, Edge Cloud, Network evolution and our GTM partnerships with major Telecoms to monetize 5G.
AWS On Air host Nick Walsh, Sr Developer Advocate and Heather Nolis, AI Development Lead, T-Mobile discuss how the best AI/ML applications of machine learning occur when algorithms augment human activities instead of replacing them. That’s the case with T-Mobile, which has deployed machine learning to help contact center agents better serve customers. Nolis and her team built machine learning models that sit between a care agent and the customer, scanning previous customer interactions and serving up relevant information to the care agent to help them quickly address the customer’s issue.