AWS for Industries

The Transformative Impact of Generative AI in Manufacturing at Hannover Messe 2024

Generative artificial intelligence (AI) is rapidly becoming a cornerstone technology, driving significant advancements in manufacturing through the creation of synthetic data and images, optimized designs, process simulations, insights from operational data, and more. Recent research from Capgemini indicates that a substantial majority of manufacturers are not just curious about generative AI; 55% are actively exploring its potential and another 45% have moved to pilot projects. In just the past year, thousands of Amazon Web Services (AWS) customers have collaborated with AWS to delve into generative AI projects, identifying over 80 practical applications across the automotive and manufacturing sectors. This growing momentum highlights the critical role of generative AI in boosting operational efficiencies and prompting innovation.

At Hannover Messe 2024, visitors to the AWS booth experienced firsthand the capabilities of generative AI in optimizing operations and accelerating innovation, including transforming product design, enhancing production processes, and refining supply chain management. AWS and AWS Partners made it simple for manufacturers to understand how to build and scale generative AI-based applications effectively with over 25 demonstrations using services such as Amazon Bedrock, Amazon CodeWhisperer, and Amazon Q in combination with AWS for Industrial purpose-built services such as AWS IoT SiteWise, Amazon Lookout for Vision, and AWS Supply Chain. These live demos provided attendees with a window into the future of industrial transformation, showcasing how AWS cloud technology and partner solutions enable manufacturers to harness the benefits of generative AI.

This blog post dives deep into the latest trends in generative AI within the manufacturing industry as well as observations from Hannover Messe 2024. It explores real use cases and shares compelling success stories from leading manufacturers who have leveraged AWS’s services and AWS Partner solutions. These stories illustrate how generative AI facilitates rapid troubleshooting and decision-making on the shop floor, dramatically reducing Mean Time to Resolve (MTTR) for equipment failures, as well as drives unprecedented gains in productivity and product quality.

Trends in generative AI adoption in industrial manufacturing

The landscape of manufacturing is rapidly transforming as generative AI gains traction, reshaping operational efficiencies and innovation. Hannover Messe 2024 showcased the growing influence of generative AI across various sectors, with successful customer case studies and emerging use cases highlighting its role in driving concrete business outcomes for manufacturing. The event underscored the integration of generative AI with existing technologies like industrial IoT and digital twins, aiming to manage complex industrial challenges more effectively. While the potential was widely recognized, discussions also covered the challenges of implementing these technologies at scale, such as the need for industry-specific and proprietary training data and integration with existing IT infrastructures. Despite these hurdles, generative AI’s impact was seen as a transformative force across the entire value chain of industrial operations, from design and planning to manufacturing and engineering.

However, one theme continues to emerge: harnessing the full power of generative AI requires a robust modern data strategy. According to IDC, a notable 83% of IT leaders agree that utilizing business data to fine-tune generative AI models offers a significant competitive advantage, yet only 30% of organizations have started developing the necessary modern industrial data architecture. AWS facilitates this integration by hosting generative AI in the cloud where manufacturers’ data is already managed, allowing easy access to powerful AI tools that can dramatically enhance operational agility and decision-making processes.

Security and privacy concerns remain paramount as manufacturers delve deeper into generative AI. According to recent research, worries about data control, intellectual property, brand impact, regulatory challenges, and high infrastructure costs make some hesitant to fully embrace these technologies. AWS addresses these concerns by providing a secure cloud environment, recognized for protecting sensitive data and reducing infrastructure overheads, making it an ideal platform for developing and scaling generative AI applications. This security assurance enables manufacturers to explore and implement generative AI solutions with confidence, driving forward industrial innovation while safeguarding critical business assets. With AWS, customers can access leading foundation models, customize with their own data, and use the leading security, access control, and features they trust from AWS.

Industry-specific generative AI use cases showcased at Hannover Messe

AWS’s main showcase at Hannover Messe demonstrated several impactful applications of generative AI across various aspects of manufacturing, providing innovative solutions to longstanding industry challenges:

  • Interactive Inventory Trends Analysis: In the main showcase at the AWS booth, the “e-Bike Smart Factory“, AWS demonstrated how manufacturers can utilize generative AI to empower supply chain specialists to perform complex inventory analyses using intuitive natural language commands. This tool powered by Amazon Q in AWS Supply Chain facilitates quicker, data-driven decision-making in supply chain management.
  • Assisted Diagnosis and Troubleshooting: In the “e-Bike Smart Factory”, AWS also leveraged Amazon Bedrock and AWS IoT SiteWise to showcase how a fictitious e-bike manufacturer enhanced shop floor productivity with a generative AI assistant for quick diagnosis and resolution of equipment issues. This system used natural language processing to parse complex technical documentation such as manuals and Standard Operating Procedures (SOPs) as well as real-time IoT data streams and other system data, enabling technicians to easily identify and address problems.
  • Advanced Defect Detection: Using Amazon SageMaker Studio, the e-Bike Smart Factory showcased how manufacturers can implement sophisticated image synthesis techniques to train machine learning models in Amazon Lookout for Vision for defect detection. This generative AI application creates a robust dataset of synthetic images, which improves the model’s ability to detect and classify manufacturing defects accurately.
  • Maintenance and Operator Work Instructions: In addition, AWS had a dedicated kiosk showcasing generative AI use cases in manufacturing. The first use case illustrated how manufacturers can leverage Large Language Models (LLMs) for Retrieval Augmented Generation (RAG) in scenarios like maintenance and operator work instructions, as well as using LLMs to document programmable logic controller (PLC) programs and write structured text. Given security and cost considerations for deploying generative AI workloads, this demo utilized Amazon SageMaker Canvas to highlight performance vs. accuracy differences across various LLMs.
  • Product Lifecycle Visibility: The second use case in the AWS generative AI kiosk highlighted an intelligent digital thread using knowledge graphs and generative AI technologies, aimed at addressing the challenge of dispersed knowledge across the product lifecycle in manufacturing organizations, which often leads to limited visibility, knowledge gaps, and difficulties in continuous improvement. This demo demonstrated how to integrate disparate data sources across enterprise systems using a combination of knowledge graphs and generative AI – driving traceability, accessibility, collaboration, and agility throughout the product lifecycle.

Video of “e-Bike Smart Factory” generative AI assistant using AWS IoT SiteWise and Amazon Bedrock to enhance shop floor productivity through rapid equipment issue diagnosis and resolution.

In addition, numerous AWS partners showcased innovative applications of generative AI across manufacturing, demonstrating how this technology can drive efficiency, visibility, and optimization throughout industrial operations.

Tulip demonstrated their frontline operations platform combining no-code apps, AI, and edge connectivity in a composable suite to digitally transform processes faster through guided operations, production tracking, and real-time operational visibility. At Hannover Messe 2024, Tulip also announced a strategic collaboration agreement with AWS to build generative AI capabilities leveraging services like Amazon Bedrock that empower frontline workers. This will allow Tulip’s platform to leverage LLMs and other generative AI technologies to provide contextualized instructions, automate documentation, analyze issues, and streamline knowledge sharing across frontline operations. By integrating AWS’s advanced AI/ML capabilities, Tulip aims to further augment human workforces with AI co-pilots that enhance productivity, quality, and workforce engagement on the plant floor.

Bosch Digital Twin Industries showcased their solution for optimizing critical asset performance through multi-sensor data, physics-based AI algorithms, and prescriptive asset management combining sensing, insights and automation. Their generative AI chatbot leverages user manuals, operating manuals, and natural language processing to provide site and maintenance engineers with relevant information on faults, causes, remedies, and troubleshooting guidance. The chatbot creates a knowledge graph of connected components and their relationships, built using Claude 3’s Sonnet model, Amazon Bedrock’s knowledge base, Amazon Textract for information extraction from manuals, and OpenSearch as the vector database. When users enter a prompt, the system retrieves relevant information chunks through similarity searches, ranks them, fetches related component connections, invokes the LLM via Bedrock, and responds with insights like maintaining optimal operating conditions, remedies for failures, root cause analysis, interdependent component checks, disassembly/assembly steps, and shutdown precautions.

Bosch Digital Twin helps maintenance engineers optimize asset performance by using natural language to interact with a generative AI-powered chatbot that leverages multi-sensor IoT data, operating manuals, and user manuals to provide prescriptive maintenance guidance and troubleshooting insights.

Matterport showcased their Digital Twin Platform that turns buildings into comprehensive digital models. Their demo highlighted how generative AI-powered digital twins can optimize factory operations by providing a centralized visualization of real-time IoT data insights and predictive maintenance capabilities. This “single-pane-of-glass” approach consolidates telemetry from various sensors and displays it in spatial context with insights from knowledge bases and historical information. This can reduce manual effort, simplify failure resolution, and enhance predictive maintenance programs. The demo exemplified contextual predictive maintenance with Amazon Monitron, utilizing a 3D spatial visualization of telemetry mapped to a Matterport scene. It also illustrated how generative AI and services, like Amazon Bedrock, can access pertinent information stored across diverse knowledge bases, enabling more comprehensive and intelligent insights.

MongoDB showcased a demo combining real-time telemetry data collection, vector search for acoustic diagnosis, and generative AI using Amazon Bedrock to provide users with real-time natural language reports on device status. The solution leverages vector search to analyze acoustic data and detect issues, then uses generative AI to generate reports explaining the current status, troubleshooting guidance, and best practices. These reports can be augmented with a retrieval-augmented generation (RAG) architecture to incorporate operator notes, PDF manuals, proprietary standard operating procedures (SOPs), and other proprietary data sources. This enables MongoDB to deliver highly contextualized and rich reports tailored to each customer’s data, speeding up operations on the shop floor.

Real-time acoustic diagnosis and natural language reporting powered by MongoDB’s vector search and generative AI capabilities using Amazon Bedrock.

Mendix, a Siemens business, demonstrated their generative AI application powered by Amazon Bedrock. This demo highlighted the potential for businesses to streamline processes and enhance customer satisfaction through the integration of advanced AI capabilities using the Mendix Amazon Bedrock Connector in the Mendix Marketplace, which eliminates traditional complexities and streamlines the integration of generative AI within our ecosystem.

This video explores how customers can able to enable Mendix UI setups through intuitive AI-powered dialogs.

The diverse range of generative AI use cases and demonstrations at Hannover Messe 2024 emphasized the transformative potential this technology holds for industrial manufacturing operations. From interactive inventory analysis and advanced defect detection to intelligent digital threads and predictive maintenance, the applications spanned the entire manufacturing value chain. The demonstrations highlighted how generative AI empowers manufacturers to gain visibility, make data-driven decisions, optimize processes, and drive innovation like never before. By integrating AWS for Industrial capabilities across generative AI, ML, and Industrial IoT, as well as proprietary data in the cloud, manufacturers can unlock new levels of operational efficiency, productivity, and agility. As these real-world examples illustrated, pioneering manufacturers are already realizing concrete business outcomes by harnessing the power of cloud-based generative AI solutions to solve long-standing industrial challenges.

How generative AI can drive concrete business outcomes for Manufacturing

During a focused session on the main stage at Hannover Messe 2024, Danny Smith, Principal Strategist for Artificial Intelligence at AWS, explored how generative AI technologies are integrated into the manufacturing sector, driving significant business outcomes. He emphasized how building a strong data foundation in the cloud can help manufacturers leapfrog their competition in the age of advanced artificial intelligence techniques. Generative AI is top-of-mind for executives, with a potential value of $2.6 trillion to $4.4 trillion annually, according to recent research by McKinsey & Company. Despite the potential, manufacturers are grappling with ongoing challenges of identifying and breaking data silos, making data actionable, and using data to drive growth and profitability.

In the session, the industry expert shared some of the opportunities to address these challenges that generative AI offers through improved customer experiences (e.g., chatbots, personalization), enhanced employee productivity (e.g., code generation, content creation), and optimized business processes (e.g., cybersecurity, data to insights). Several active generative AI projects in the sector include guided maintenance, generative styling, production planning meetings, and on-product assistants, showcasing the technology’s versatile applications. A notable case study illustrated how repair technicians could be accelerated using generative AI tools, streamlining knowledge retrieval, improving repair accuracy, and reducing downtime.

By bringing generative AI to the cloud, where their data already resides, manufacturers can leverage centralized data storage and real-time processing capabilities – facilitating immediate operational adjustments and decision-making. The cloud also provides robust security measures and compliance with international standards, critical for protecting sensitive data and ensuring regulatory adherence. This security, combined with the reduced costs of cloud infrastructure compared to traditional on-premise setups, makes generative AI more accessible and financially viable for manufacturers of all sizes.

 Active generative AI projects AWS customers are currently pursuing as shared by Danny Smith, Principal Strategist for Artificial Intelligence at AWS, during a session on the main stage at Hannover Messe 2024.

Danny emphasized aligning AI initiatives with business outcomes and balancing trade-offs between quality, performance, and cost. AWS provides a comprehensive generative AI technology stack, including Amazon Bedrock, Amazon SageMaker, and specialized hardware like AWS Inferentia. He also encouraged manufacturers to start with Amazon Q Developer for immediate productivity gains and promote experimentation through hackathons. For long-term strategy, the AWS Generative AI Innovation Center offers scoping workshops to identify use cases and guide manufacturers through proof-of-concept development.

This video shows customers how they can use generative AI to start building applications on AWS.

By transitioning generative AI to the cloud, manufacturers unlock powerful capabilities to innovate faster, streamline operations, and maintain competitive advantages in a rapidly evolving industry. This strategic integration not only simplifies the management of generative AI applications but also maximizes their impact, driving significant improvements in efficiency and operational agility.

Customer stories highlighting real-world applications

Several leading global companies have already implemented these generative AI solutions, illustrating the practical benefits and transformative potential of AWS’s technologies:

KONE uses Amazon Bedrock to empower field technicians with a generative AI-driven Assistant, leveraging vast libraries of technical documentation to enhance customer service. This tool significantly speeds up the time to diagnose and resolve issues, improving overall customer satisfaction and operational efficiency.

“Amazon Bedrock enabled us to quickly innovate on industry use cases with generative AI. AWS’s generative AI capabilities are the platform for our future Technician AI Assistant, which will leverage complex technical documentation and case libraries to speed customer service in the field.” – Amy Chen, CIO, KONE

BMW Group uses Amazon QuickSight’s Q-powered authoring experience to enhance regional supply chain management. This tool allows non-technical users to generate sophisticated analytics and visualizations swiftly, improving responsiveness to market changes and aiding in strategic decision-making.

“The new authoring experience of Amazon QuickSight powered by Amazon Q is a huge time saver to create calculations without stopping for reference and build visuals fast, and then refine the visual presentation for a precise experience, all with natural language. The regional specialists can impress our business users with a quick turnaround, and they can make critical decisions more quickly.” – Christoph Albrecht, data engineering and analytics expert, BMW Group

Merck & Co. applies generative AI to reduce false rejects in pharmaceutical manufacturing by over 50%. By generating synthetic defect data, Merck enhances its ML models’ ability to identify genuine defects, thus optimizing the manufacturing process and ensuring the availability of life-saving medications.

“We use generative AI approaches and generative models like GANs [Generative Adversarial Networks] and Variational Autoencoders to develop synthetic defect image data for complex defects where we have limited training data. The insights gained have helped us to understand root causes of rejects, optimize processes, and reduce overall false rejects across various product lines by more than 50%.” – Nitin Kaul, Associate Director, IT Architecture, Merck & Co.

Vivix Vidros Planos leverages a Virtual Engineer powered by Mendix and Amazon Bedrock to accelerate the onboarding and training process for new technicians. This AI assistant provides real-time, personalized problem-solving instructions, significantly reducing training time and enhancing operational efficiency.

“With generative AI, we’ve been able to compress our technician training process from years to just a few months. This is allowing us to expand our maintenance practices efficiently even as we bring in many new hires.” – Aristotle Third Neto, Industrial Transformation Manager, Vivix Vidros


Generative AI is undeniably transforming industrial manufacturing, offering innovative solutions that streamline operations, enhance product quality, and improve supply chain management. At Hannover Messe 2024, AWS and AWS Partners showcased how manufacturers can harness the power of generative AI to gain unprecedented insights, optimize processes, and drive innovation. With tools like Amazon Bedrock, Amazon SageMaker, Amazon Q, and AWS Inferentia, AWS provides a comprehensive technology stack that makes integrating generative AI applications into existing operations more accessible than ever.

However, realizing the full potential of generative AI requires a robust data strategy, seamless integration with cloud technology, and a commitment to aligning AI initiatives with business outcomes. By leveraging AWS’s secure cloud environment and purpose-built industrial solutions, manufacturers can confidently explore and implement generative AI applications that deliver tangible business results.

By embracing generative AI with AWS, you can unlock powerful new capabilities that will keep your business ahead of the curve in this rapidly evolving industry. To get started, visit the AWS Generative AI Innovation Center to discover how AWS can help you identify, implement, and scale generative AI solutions tailored to your unique industrial challenges. You can also connect with AWS Generative AI Competency Partners to discover specialized generative AI solutions that can enhance productivity, quality, and workforce engagement on your shop floor.

Sophie Pagalday

Sophie Pagalday

Sophie Pagalday is the Sr. Product Marketing Lead for the AWS for Industrial & Manufacturing growing portfolio of purpose-built services. She's spent most of her product marketing career in the industrial automation, logistics, and supply chain space, focused on technology ranging from enterprise work management systems to robotics. As an advocate for our customers, Sophie is relentless about learning about the challenges they face and how to best communicate how our services can help them achieve their goals.

Dimitrios Spiliopoulos

Dimitrios Spiliopoulos

Dimitrios Spiliopoulos is a Worldwide Principal IIoT GTM Specialist in AWS. He is a LinkedIn Top Voice as well as regular author and speaker about Industrial IoT and Smart Manufacturing, working with global industrial customers and partners. He has been in AWS for 3,5 years across various roles related to IoT and manufacturing. He has received multiple awards for his work in the IoT space and in the manufacturing sector, like the Top 100 Manufacturing Sector Advocate award from and Who is Who in IoT by Onalytica, as well as he is adjunct professor for IoT at IE Business School since 2018. He loves sharing insights about Edge, IoT, Digital Twins, AI, Sustainability and Industry 4.0. Feel free to follow him or connect on LinkedIn: