Why Leading Companies Choose AWS Solutions

Speed matters when you’re bringing science from the lab into the real world. I spoke with Yannick Misteli from Roche, who leads a team of ~150 engineers across the US, EMEA, and APAC. His group supports Roche’s go-to-market domain, sales and digital marketing, with data and analytics. Their mission is to build a 360° view of customers, help teams react quickly to a very dynamic market and regulatory environment, and make interactions with the broader healthcare system more meaningful. On AWS, they’ve: *Simplified a fragmented tech landscape by standardizing on a lakehouse architecture  *Staged data in Amazon S3 with the AWS Glue Data Catalog – https://lnkd.in/dcXcyxBJ *Used Amazon Redshift as the big data engine for complex business transformations – https://lnkd.in/dF2mYUqB They measure impact by tracking adoption and usage of dashboards and tools and by watching for a real mindset shift toward data-driven decision-making across the business. #AWSPartner #AWSreInvent #AWSanalytics

Everyone is talking about AI But what’s more interesting is who is powering whom The partnership between OpenAI and Amazon isn’t just another tech collaboration it’s a strategic alignment of two massive strengths: 🧠 Advanced foundation models ☁️ Hyperscale cloud infrastructure From a tech point of view this is crucial OpenAI builds cutting edge models that require enormous compute, distributed training systems, optimized networking, and specialized hardware. Running and scaling these models globally isn’t trivial it demands resilient infrastructure, high throughput networking, storage optimization, and cost-efficient scaling That’s where Amazon comes in With AWS’s cloud capabilities high performance compute clusters, GPU/accelerator-backed instances, low-latency networking, and managed AI services large scale model training and inference become practical and enterprise ready Why this matters: 1️⃣ Scalability – Foundation models need elastic infrastructure. Cloud native scaling makes real-time inference possible for millions of users. 2️⃣ Enterprise Adoption – Companies already on AWS can integrate advanced AI capabilities directly into their existing ecosystems. 3️⃣ Cost Optimization – Training and inference are expensive. Infrastructure level optimizations reduce barrier to entry for businesses. 4️⃣ Innovation Speed – When infrastructure and AI research move in sync, iteration cycles shrink dramatically. From a developer’s perspective this means faster experimentation, managed AI integrations, better tooling, and production ready AI systems. This isn’t just about AI models. It’s about combining research excellence with infrastructure dominance. #AI #OpenAI #Amazon #AWS #CloudComputing #MachineLearning #TechLeadership

In conversations with technology and business leaders, one theme stands out: organisations that prioritise modernisation consistently deliver better customer experiences. A prime example? Amazon. Just search “Amazon Flywheel” to see how Amazon's journey has prioritised customer experiences to deliver business results. Today, leaders aren’t just asking, “Should we move to the cloud?”—they’re asking, “Will it deliver measurable business value?” They're looking for more than cost savings. They want to know: Is the juice worth the squeeze? And in many cases, it absolutely is. When companies shift from legacy infrastructure to AWS, the biggest gains go far beyond technology—they enable agility, speed, and the freedom to focus on innovation instead of upkeep. To bring this to life, we’ve created a visual snapshot showing how modernisation with AWS is driving real-world outcomes. Drawing from research by AWS, Deloitte, and 451 Research, here’s what organisations are seeing: ✅ Up to 66% increase in developer productivity, enabling teams to focus on what matters most ✅ $7.8M in annual cost savings, by moving from fixed infrastructure to agile, OPEX-based models ✅ 4x faster delivery of new features, by reducing complexity and removing bottlenecks We’ve mapped out what the journey looks like—from legacy systems to a cloud-native architecture built for speed, scale, and innovation. If you're looking to benchmark your cloud strategy or unlock more value from your technology investments, this resource provides a clear, data-driven perspective on what’s possible—and how to get there: 🔗 https://bit.ly/4kKVa66 Amazon Web Services (AWS)

Most enterprises face the same challenge: decades-old COBOL applications that are critical to operations but difficult to modernize quickly. The traditional approach takes years. The AI-powered approach is dramatically faster. Migrating to the cloud is a powerful way to future-proof your business. Most organizations take a phased approach rather than attempting everything at once. Mercedes-Benz AG faced exactly this challenge—3,000 fragmented SAP systems and legacy mainframe applications blocking innovation. They partnered with Amazon Web Services (AWS) to use agentic AI to accelerate their transformation. The business results: - Record 9-month migration of OneERP (17 systems, 40,000+ users) to AWS - "Way faster migration process" using agentic AI for COBOL-to-Java refactoring - Up to 60% application reduction planned through radical standardization - Higher developer satisfaction with AI-powered tools for testing and code generation Migration wasn't the finish line, it was the foundation for their most intense product launch program in history. What made it work: ✅ Agentic AI with AWS Transform accelerating mainframe migration ✅ AWS for RISE with SAP with latest Amazon EC2 u7i instances ✅ "Consolidation before modernization" with strict governance ✅ Strategic partnership focused on innovation, stability, and security See how Mercedes-Benz transformed their legacy infrastructure with AI-powered migration. https://go.aws/3QUR5mK

I am often asked to share customer success stories and enjoy doing so. However, the ones that I like and that I share have to include actual details, not just generalities! Today's story has tons of details. A few years ago #AWS customer New Relic decided to completely exist their on-premises data centers by migrating to the AWS cloud. This move was driven by several factors including aggressive sustainability goals and a desire for better scalability & efficiency. After an initial migration to a fleet of over 23,000 AWS Graviton servers in 2022, they continue to optimize and to adopt newer instance types. Today, with about 64% of their workloads migrated, they have reduced their carbon emissions by 99.45% and the total amount of compute (measured in Normalized Instance Hours, or NIH) by 27.29%. Read the entire blog post, "New Relic powers sustainable observability with AWS" at https://lnkd.in/grWXEEAQ

I'm excited to share this impressive AWS case study featuring our customer Lion, a leading beverage company that successfully modernized its infrastructure running SAP applications through cloud adoption. Key Highlights: - 25% reduction in TCO - 70% faster data transfer speed - 100% preservation of SAP System Integrations - Enabled innovation with easier access to AWS’s AI services We in AWS Support are impressed by how organizations like Lion leverage cloud technology to drive business value. Their success story showcases the power of well-executed cloud migration strategies and the strength of the AWS-SAP partnership! Read the full case study : https://lnkd.in/gq7dZ3sm Planning your own large-scale migration or high-stakes launch? Learn how expert support can maximize success and minimize risk with AWS Countdown Premium.