Manufacturing AI Deployments

Blue Origin, a leading aerospace company, accelerated lunar hardware development by leveraging AI agents and advanced AWS services.,The company built BlueGPT platform with over 2700 AI agents using Amazon Bedrock, Amazon Bedrock AgentCore, Amazon EKS, Amazon EC2, Amazon OpenSearch, and Strands Agents SDK.,BlueGPT enables autonomous iterative design loops, complex GPU-accelerated physics simulations, and hierarchical AI agent orchestration, drastically reducing hardware development time from years to days.,This AI-powered approach democratized AI use across 70% of employees, improving productivity and enabling the delivery of the world’s first AI agent-designed lunar hardware ready for Moon deployment.

ARUM designed a machining center that leverages LLMs so workers can operate the tool through natural conversations.,The solution features AI character KAYA and integrates Microsoft AI services on Azure to support chat, voice communication, summarization, database search, and fallback agent behavior.,KAYA enables novice workers to perform high-precision machining and helps automate NC program creation in a manufacturing environment facing skilled labor shortages.

Schneider Electric, a global leader in energy and industrial automation, faced growing operational complexity and rising energy demands in AI-driven industrial environments. To maintain its leadership and advance sustainability, Schneider Electric developed an AI-native ecosystem centered on its EcoStruxure platform and powered by Microsoft Azure AI Foundry and Azure OpenAI. Strategic alliances with Microsoft and NVIDIA enabled the integration of AI throughout energy, automation, and sustainability applications. Schneider Electric now delivers industrial AI copilots, end-to-end AI-ready infrastructure for high-density data centers (including liquid cooling with Motivair), predictive maintenance, and a data-driven 'self-healing' supply chain. The architecture enables seamless connection from sensors and hardware to cloud AI services, driving outcomes like lower costs, accelerated delivery, and massive reductions in energy and carbon footprint. Schneider Electric has achieved a €130M+ supply chain value, reduced inventory/delivery times, and scaled recurring AI software revenues. Its open ecosystem and vertical integration make it a dominant industrial AI partner globally.

Novus Hi-Tech enhanced FleetGPT to improve fleet safety operations as its deployments expanded.,The system assesses unsafe events in context, determines next-best actions, automates routine follow-up, and escalates higher-risk cases while human teams remain the validation layer.,Amazon Bedrock powers the reasoning layer across video, telemetry, and driver-behavior data; AWS IoT Core, Amazon Kinesis Video Streams, Amazon S3, and AWS Step Functions support ingestion, storage, and workflow orchestration.

RSM deploys advanced Copilot Agents in manufacturing to reduce quality risks and manage product recalls.,AI-powered agents analyze real-time data, identify affected product batches, pinpoint root causes, and automate recall notifications—all with seamless integration to existing Quality Management Systems (QMS), ERPs, and CRM tools.,Their technical architecture includes layering data sources, Copilot intelligence (Copilot Studio, Power Platform, Power Automate), and user interfaces in Teams, Outlook, or Dynamics 365.,AI models and Copilot Studio guide agents through user prompts, system actions, and automated event triggers. Security is ensured via Entra ID authentication, data loss prevention, and role-based access for generative AI.,Retrieval-Augmented Generation (RAG) capabilities allow accurate processing for both structured (quality orders, ERP records) and unstructured (documents, feedback) data.,Governance processes retain humans-in-the-loop for decisions with validation, filters for sensitive topics, and performance monitoring, ensuring compliance and reliability.,These innovations accelerate investigations and recalls, improve regulatory alignment, and protect corporate brands in a highly regulated environment.,RSM offers custom Copilot solutions for manufacturers seeking operational transformation and competitive advantage.

Siemens and Google Cloud created Knowledge Fabric to help modernize large industrial software codebases and the applications that run on them.,The system ingests the software ecosystem into an intelligent agentic workflow that can reason across code, Jira, Confluence, and PDF documentation while preserving explainability and traceability.

On the opening day of the 2026 Beijing Auto Show, leading Chinese automakers including BYD, Geely, Li Auto, Changan Automobile, Dongfeng Motor, BAIC, Great Wall Motor, SAIC Volkswagen, and SAIC IM Motors announced integration of Qwen into their intelligent vehicle systems.,Select models from these manufacturers will offer Qwen-powered AI services directly within the cabin, enabling users to book hotels, purchase attraction tickets, order food delivery, track parcels, and more.,Alibaba Cloud describes an edge + cloud collaborative architecture for smart cockpits: Qwen-Omni runs on the edge to perceive and interpret the physical environment, while cloud-side Qwen agentic AI capabilities understand natural-language commands, decompose intents, plan multi-step workflows, and orchestrate scenario-specific agents for seamless execution.,The article also notes an earlier FAW Hongqi 'Lingxi Cockpit' implementation that integrated Qwen agentic AI into an intelligent system on the Hongqi HS6 PHEV for ambiguous voice recognition and complex multi-step task planning.,Alibaba says it is also adapting Qwen-Omni to run on NVIDIA DRIVE AGX Thor for in-cabin human-machine interaction.

Litmus, a leading Industrial Data Operations provider based in Germany, formed a strategic partnership with Microsoft to deliver a seamless edge-to-cloud solution for industrial companies.,The integration leverages Litmus Edge with Microsoft Azure IoT Operations, enabling real-time data collection, contextualization, and processing directly from industrial edge devices.,Azure IoT Operations, complemented by Azure Arc and Entra ID, provides adaptive cloud management, device discoverability, observability, and secure, scalable data orchestration.,The Akri Litmus Connector facilitates connectivity and automatic discovery, streamlining edge-to-cloud deployments and simplifying industrial device management.,This solution empowers industrial customers to scale AI-driven applications like predictive maintenance and quality control with rapid data acquisition and analysis.,By unifying data pipelines, companies gain real-time operational visibility and can efficiently deploy AI models for improved production efficiency.

Toyota Motor Europe (TME) built a proof of concept with Deloitte and the AWS Generative AI Innovation Center to automatically generate documentation from legacy NCL source code.,The solution produces technical YAML documentation, business HTML reports, and Mermaid process-flow diagrams from a legacy warranty-handling application, using Amazon Bedrock, Strands Agents SDK, Amazon Bedrock AgentCore, and an Amazon Bedrock Knowledge Base.,The workflow uses agentic orchestration, retrieval-augmented generation, and bottom-up diagram composition to overcome context-window limits and preserve embedded business logic for modernization.

UNACEM, a Peru-based industrial group operating in cement, aggregates, concrete and power generation, created an agentic AI logistics assistant for its Lima plant to reduce truck-driver waiting time during cement order picking and preparation.,The assistant is exposed through WhatsApp and web chat and uses IBM watsonx Orchestrate as the orchestration layer, watsonx.ai for LLMs and embeddings, IBM Code Engine microservices, pgvector on IBM Databases for PostgreSQL, and IBM Cloud Object Storage for grounded retrieval over manuals and operational content.

Poloplast modernized a legacy AS/400 ERP-based planning process by integrating demand planning, business performance planning, automation, and AI agent capabilities across finance and operations.,The company uses Microsoft Dynamics 365 Supply Chain Management, Dynamics 365 Finance, Power Platform, and Microsoft Copilot Studio to improve forecasting, budgeting, reporting, and employee access to knowledge.

WalkingTree leverages Azure AI and cyber-physical systems to improve quality control in manufacturing industries including electronics, automotive, and food processing.,By deploying AI-powered visual inspection, predictive analytics, and CPS-enabled real-time dashboards, WalkingTree addresses persistent challenges in manufacturing: defect reduction, compliance, and downtime minimization.,Electronics manufacturers using the system saw a 25% reduction in PCB failure rates, while automotive clients saw a 40% decrease in unplanned downtime.,Food processing companies improved compliance and quality assurance via real-time metrics and actionable AI insights.,The solution harnesses IoT sensors, predictive maintenance, and data-rich dashboards to optimize productivity and preserve production integrity.,WalkingTree provides bespoke implementation and support tailored to each manufacturing vertical.

BMW Group built a 3D App Store on AWS that gives employees anytime, anywhere access to high-fidelity car visualizations. Using Amazon S3, Amazon EC2 GPU instances, and Amazon Bedrock, the team delivered a globally scalable XR platform that democratizes spatial computing across the organization.

Elanco rebuilt its IT ecosystem after separating from its parent company and adopted Google Cloud to support secure, scalable data analytics and AI across the business.,The company developed Elanco.ai, a private and secure generative AI framework that uses Gemini models and Google Cloud Cortex Framework to ground responses in enterprise data.,Elanco.ai supports routine employee tasks, pharmacovigilance case documentation and translation, SAP order-data lookup, and compliance-document processing across thousands of policy documents.

FAW Group Import & Export Co., Ltd. operates Hongqi Overseas’ remote maintenance support system for global vehicle service. The team needed to turn large volumes of maintenance manuals, work orders, images, and video into reusable knowledge assets so dealers and technical experts could resolve issues faster across regions.,Using AWS generative AI services, Hongqi built a multimodal RAG-based knowledge system to support natural-language Q&A, cross-language understanding, source-attributed retrieval, and automated maintenance report generation for after-repair knowledge accumulation.

Satellite giant EchoStar needed more efficient operations to deliver content to businesses and consumers globally. Its Hughes division wanted to increase employee efficiency and streamline daily business processes.,Using Microsoft Azure AI Foundry, Hughes developed 12 new production apps, from automated sales call auditing and customer retention analysis to field services process automation support and more.,The solutions currently in production are expected to save Hughes more than 35,000 work hours annually and boost workforce productivity by at least 25%.,EchoStar delivers entertainment, communication, and connection to millions of businesses and consumers around the world through leading satellite-powered brands, including Hughes Network Systems, DISH, Sling, and Boost Mobile.,The company aims to provide these services reliably and expand on its mission to offer satellite coverage in hard-to-reach rural areas. That’s why EchoStar and its brands take a technology-forward approach to solving complicated operational, productivity, and customer experience inefficiencies.,An avid and early AI adopter, Hughes Network Systems understood the power of generative AI to address challenges in speech, vision, text, and structured data for a wide range of work productivity challenges. It needed a way to relieve sales call auditors from listening to hours of conversations to ensure quality communications, a priority customer experience objective at Hughes.,The company knew it was an area in which it could significantly improve cost, productivity, and ROI by using the right technology.,Hughes chose Microsoft Azure AI Foundry because of their longstanding partnership with Microsoft and its deep technical knowledge. They relied on Microsoft’s approach to responsible AI and data privacy, security, and governance options.,Hughes developed an AI-driven, automated speech-to-text system, delivering higher-value interactions and advanced call insights and agent directives across calls, exponentially boosting productivity.,They also created a large language model (LLM) operations framework using Azure AI Foundry to evaluate and ensure the quality and safety of AI-generated outputs. This helped accelerate moving from pilot to production.,Multiple AI applications enhance employee efficiency and customer service at Hughes, saving over 30,000 hours annually summarizing calls and 8,000 hours through field services process automation.,AI-enhanced computer vision accelerates image generation and annotation for faster training and higher accuracy of traditional vision models.,Retrieval-augmented generation (RAG) improves information accessibility for employees and field installers.,AI analyzes customer journey data for quality assurance and churn reduction, boosting overall productivity by 25%.,Hughes plans to expand agentic AI deployment using Azure AI Foundry and Microsoft Copilot Studio across verticals to continue AI innovation.

Michelin aimed to optimize its global profit margin, enhance supply chain resilience, and improve manufacturing operational efficiency under complex global market conditions.

Cox Automotive deployed autonomous AI agents using Amazon Bedrock AgentCore to automate and scale vehicle lifecycle workflows, including fleet services, auctions, dealerships, and consumer experiences.,AgentCore enabled conversation context management, multi-agent orchestration, security with role-based permissions, observability, and cost tracking.,Within one year, Cox deployed 17 AI agent solutions, reducing fleet repair estimate times from hours to minutes, increasing consumer engagement 3x, saving 17,000 work hours, and cutting technical debt by 50%.,The architecture includes Amazon Bedrock, AgentCore memory, Guardrails, and integration with Strands Agents Framework for multi-agent coordination.

Georgia-Pacific, a leading global manufacturer of pulp and paper products, faced challenges with scattered knowledge across many facilities, leading to production inefficiencies and risk of knowledge loss from retiring employees.,The company partnered with AWS and AWS Professional Services to develop ChatGP, a generative AI chatbot using Amazon Bedrock's Anthropic Claude large language model, integrated with IoT sensor data via Amazon Kinesis.,ChatGP provides machine operators centralized, contextualized, and real-time troubleshooting guidance and knowledge access tailored to specific equipment and processes across 140+ facilities.,Impact includes improved machine production, reduced quality defects, minimized downtime, accelerated troubleshooting, preservation of expert knowledge, and estimated multimillion-dollar annual savings across operations.

Audi AG migrated its car configurator backend to AWS using Amazon EKS, Karpenter, and AWS Lambda to improve scalability and reduce costs.,Developed a serverless e-commerce platform on AWS Lambda enabling rapid online vehicle reservations during COVID-19, reducing costs by 70%.,Deployed generative AI chatbots powered by Amazon Bedrock to enhance internal knowledge retrieval and customer engagement.,Solutions enabled faster innovation, reduced compute costs by up to 63%, and improved time-to-market from months to weeks for digital services globally.