AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, there would be no Google Cloud, as they are the ones building the future on our platform. In this regular round-up, we dive into some of the exciting projects redefining businesses, shaping industries, and creating new categories.
For our latest edition, we learn how Google Cloud built an AI-powered training tool for US Ski & Snowboard; a new data approach for Vodafone and Fastweb; evaluating John Lewis Partnership’s developer platforms; the Golden State Warrior’s AI playbook; healthy, stable networks at Hackensack Meridian Health; and Ab Initio brings better context to data for AI.
Be sure to check back next year to see how more industry leaders and exciting startups are putting Google Cloud technologies to use. And if you haven’t already, please peruse our list of 1,001 real-world gen AI use cases from our customers.
Helping Team USA go bigger with AI
Who: Google Cloud set out to build an AI-powered training tool for the U.S. Ski & Snowboard team ahead of the Olympic Winter Games in Italy this February.
What they did: To help Team USA athletes like snowboarder Maddie Mastro and freeskier Alex Hall find a competitive edge, and decode the physics of their most daring tricks ahead of the Olympic Winter Games, Google Cloud has built an industry-first AI-powered video analysis platform, tapping unique models from Google DeepMind’s built on research into spatial intelligence. The tool’s goal is to help U.S. Ski & Snowboard athletes elevate their tricks — and their confidence.
Why it matters: In less time than it takes to ride the lift back to the top, users of the Google Cloud tool have a complete analysis of each trick, how it compares to past efforts, and a dizzying array of notes and pointers on what could be better next time. And while the primary reason for developing the tool was to improve athlete performance, safety came in a close second. Better body awareness can help prevent accidents and injuries — crucial for a team where dozens of its 240 athletes can be out with injuries.
Learn from us: “In the past, I’d have to call a friend and say, ‘Hey, do you have that shot of that trick from five years ago?’ and then I’d just flip back and forth between videos. This tool changes that — it lets you take a run from the past and bring it into the future. You can slow it down and see exactly where your head and body are positioned in that moment. It’s about being able to see those little things and understand them in real time.” – Shaun White, five-time U.S. Olympian and three-time gold medalist
Fastweb + Vodafone reimagined data workflows
Who: Following the acquisition of Vodafone Italy by Swisscom in 2025, these leading European telecom providers wanted to rethink how they serve customers and deliver timely, personalized experiences across mobile, broadband, and digital channels.
What they did: Both companies had already begun modernizing customer data workflows with BigQuery, but combining ecosystems exposed certain limits of the existing setup. In order to give every channel real-time access to accurate customer data, they implemented Spanner as a service and governance layer, delivering low-latency reads, horizontal scalability, high availability, and a fully managed environment with zero ops overhead. The team is also using Gemini to generate clear documentation directly from the code, which saves hours of manual work.
Why it matters: Using Spanner Graph allowed the organization to map lineage in a way that reflects how its platform actually works: which tables drive specific jobs, how transformations cascade, and where dependencies sit. Call centers now see more complete, up-to-date customer information, digital channels can rely on consistent data without custom integrations, and partners can access what they need with low latency through Apigee.
Learn from us: “Rebuilding our Customer 360 platform with Google Cloud services has already changed how Fastweb + Vodafone works. Workflow monitoring is simpler, pipelines are leaner, and real-time serving is now the norm. ” – Vincenzo Forciniti, IT AI Adoption & Platform Engineering Lead, Fastweb + Vodafone
John Lewis measures the value of its developer platform
Who: The John Lewis Partnership is a major UK retailer operating John Lewis department stores and Waitrose supermarkets. To power their digital transformation, they built the John Lewis Digital Platform (JLDP) to support dozens of product teams building high-quality software for johnlewis.com.
What they did: Moving beyond simple usage metrics, John Lewis developed a sophisticated, multi-stage approach to measuring the real value of their platform. They transitioned from initial speed-based metrics (like “Onboarding Lead Time”) to a comprehensive model using DORA metrics and subjective engineer feedback via the DX platform. This included a custom “Technical Health” feature that uses small, automated jobs to monitor more than 35 health measures — such as Kubernetes best practices, security, and operational readiness — providing teams with real-time “traffic light” indicators of their service health.
Why it matters: By focusing on value rather than just activity, John Lewis ensured the platform was actually reducing friction for developers rather than just being a mandatory tool. Their automated Technical Health checks allow product teams to manage technical debt and security vulnerabilities proactively. This approach has decoupled centralized operations teams from individual services, leading to faster incident resolution (MTTR), fewer outages, and significant cost savings.
Learn from us: “Measurement is a journey, not a destination. Start by measuring something meaningful to your stakeholders, but be prepared to adapt as your platform evolves. The things that mattered when you were proving out the platform’s viability are unlikely to be what are important several years later when your features are mature.” – Alex Moss, Principal Platform Engineer, John Lewis Partnership
Hackensack Meridian Health de-risks network migration using VPC Flow Logs
Who: Hackensack Meridian Health is a leading not-for-profit healthcare organization and the largest hospital system in New Jersey. System reliability is a cornerstone value for HMH as they manage a vast network of hospitals, urgent care centers, and physician practices.
What they did: Preparing for a large-scale migration to a new Google Cloud network design, Hackensack Meridian Health used VPC Flow Logs and Flow Analyzer to eliminate the “black box” of hybrid traffic. By enabling logs on their Cloud Interconnect VLAN attachments, they captured granular telemetry — including source/destination IPs, ports, and protocols. They then exported this data to create a visual “who-is-talking-to-what” map. This allowed them to identify critical traffic patterns between on-premises data centers and specific Google Cloud regions, VPCs, and applications.
Why it matters: In a healthcare environment, even minor network disruptions can have major consequences. By mapping traffic proactively, Hudson Meridian Health pinpointed exactly which moments in the cutover carried the highest risk. This preparation allowed them to detect a migration issue in just three minutes and resolve it within five — a process that previously could have taken hours. Beyond migration, this level of visibility enables the organization to better manage capacity planning, cost attribution, and security compliance across their hybrid infrastructure.
Learn from us: “Getting a clear picture of our interconnect traffic always felt like a black box. Enabling VPC Flow Logs and feeding it into Flow Analyzer finally gave us the map we needed. Identifying those critical traffic flows before we changed any routes was key to de-risking the entire migration.” — Randall Brokaw, Cloud Engineering Manager, Hackensack Meridian Health
The Golden State Warriors’ AI-powered back office
Who: The Golden State Warriors are one of the NBA’s most successful modern franchises. Behind their on-court wins are a specialized operations team who run what might be called organization’s “G.O.A.T.T.” (Greatest of All-Time Technologies), a data and AI platform that helps drive game-time insights, trading decisions, and fan experience enhancements.
What they did: The Warriors transitioned from a “gut-feeling” culture to an “analytics-first” strategy by building an internal “digital brain” on Google Cloud. Using BigQuery and Gemini, the team now automates complex workflows that previously took hours, such as generating pre-game scouting reports. They use machine learning to run thousands of trade simulations that prioritize “team fit” over raw individual stats and employ computer vision to track the “shot quality” of every attempt in the NBA. On the business side, they built a content recommendation engine using the Discovery API to deliver personalized digital experiences to their global fan base.
Why it matters: This AI-driven approach narrows the decision tree for leadership, allowing them to focus human expertise on the most viable options. By automating the “science” of data processing, coaches and scouts have more time for the “art” of face-to-face training, planning, and player development. This integration has not only influenced on-court strategy — like the three-point revolution — but has also improved business efficiency, with employees now proactively bringing AI-driven ideas to the IT team rather than waiting for top-down mandates.
Learn from us: “You can never reach a point where either humans or machines are making all the decisions. The sweet spot is finding that middle ground where intuition and data converge on the same conclusion. Data helps us narrow our decision tree before we even start evaluating specific options.” — Nick Manning, Senior Director of Consumer Products & Emerging Technology, Golden State Warriors
Ab Initio unlocks enterprise data for the agentic AI era
Who: Ab Initio is an enterprise software company specializing in high-volume data integration and governance. Their platform is trusted by large-scale organizations to manage complex data lifecycles across hybrid and multi-cloud environments.
What they did: To solve the challenge of grounding AI agents in accurate data, Ab Initio partnered with Google Cloud to integrate its data fabric with BigQuery, Dataplex Universal Catalog, and Gemini. They launched a suite of more than 500 metadata and data connectors that bridge the gap between legacy systems (like mainframes, COBOL, and SAS) and modern cloud environments. This integration provides field-level, end-to-end lineage, allowing Gemini to access well-documented, “AI-ready” data regardless of where it resides.
Why it matters: AI agents are only as effective as the data they can access. By using Ab Initio as a “neutral hub,” enterprises can federate data from on-premises and multi-cloud sources into a single unified layer without moving the data itself. This provides the rich semantic context and lineage needed for Gemini to perform grounded, explainable reasoning. For businesses, this means faster transition from experimental AI to production-ready agentic workflows that are auditable, compliant, and capable of making complex, automated decisions.
Learn from us: “Agentic AI requires trusted, AI-ready data and metadata. Understanding the origin, quality, and meaning of information matters as much as the data itself. Gemini serves as a key component of the agentic layer, using this context to make decisions that are explainable and auditable.” — Scott Studer, Head of Development, Ab Initio & Chai Pydimukkala, Data Governance, Sharing & Integration Product Lead, Google Cloud
