Empowering Manufacturing with Generative AI: Overcoming Industry Challenges with AWS

The GDS Manufacturing Summit held in New Orleans in 2024 brought manufacturers, state legislators and partners to tackle current developments in the manufacturing industry. One of the most pressing questions discussed was how Artificial intelligence (AI) and generative AI (GenAI) are redefining manufacturing.

In a survey conducted during the Summit, manufacturing technology leaders ranked the top five most challenges they face in generative GenAI adoption. Survey results align with 2024 industry trends showing manufacturers challenged with data quality issues. Challenges include ROI uncertainty, adoption problems, security concerns and legacy system integration. This data paints a compelling picture of an industry at a crossroads, caught between the transformative potential of GenAI and practical implementation challenges.

In this blog, we explore how AWS’s services help manufacturers overcome these obstacles through automated data quality management, proven ROI frameworks, and secure integration patterns. We’ll share specific examples of how manufacturers have successfully implemented GenAI using AWS services, achieving measurable improvements in operational efficiency, cost reduction, and innovation acceleration.

Challenge 1: Poor Data Quality and Standardization

Manufacturers collect data from multiple factory sources, each typically stored in separate automation systems with unique data formats and communication protocols. Integrating data from these diverse systems creates challenges, resulting in poor data quality and inconsistent standards across the organization. As a result, many customers struggle to know where to begin with their data management efforts. A global survey of 500 manufacturing leaders, conducted by Forrester Consulting found that 98% of manufacturers report at least one issue with data within their organization. This stifles innovation and impedes the roll-out of such advanced technologies like generative AI. One-way manufacturers can solve this is by implementing a modern industrial data strategy on AWS. Customers can do this with an industrial data fabric to simplify digital transformation, and enable GenAI applications.

Centralizing Data

A modern industrial data strategy is the key enabler to digital transformation and Industry 4.0. Its goal is to make operations and IT data and securely accessible in a standardized format.

Manufacturing companies can gain deep operational insights by combining their data with analytics and generative AI tools. These insights can be applied to enhance product quality, enable real-time forecasting, and reduce expenses across the board.

At the center of the Industrial Data Platform is a Data Lake to centralize data from disparate systems and applications. A Data Lake serves as centralized storage system capable of accommodating both structured and unstructured data, regardless of its volume. The core component of a Data Lake built in AWS is Amazon Simple Storage Service (Amazon S3). It’s designed to house a wide array of information from traditional relational data sourced from business applications.

It also handles non-relational data originating from platforms like mobile apps, Internet of Things (IoT) devices, enterprise resource planning software (ERP’s), and social networks. The key advantage of a Data Lake is its flexibility in data storage. Unlike traditional databases, it doesn’t require a predefined schema or structured format. This allows data to be stored in its raw, native form, exactly as it’s generated by the source. This approach eliminates the need to anticipate future query requirements, providing greater adaptability.

The versatility of Data Lakes enables a spectrum of analytical use cases. Users can perform various types of data analysis, creating interactive dashboards and visualizations. They can also process big data, run real-time analytics, implement machine learning, and handle GenAI workloads. Amazon SageMaker Lakehouse unifies data across Amazon S3 data lakes, helping you build analytics and AI/ML applications on a single dataset.

For example, Georgia-Pacific, a major manufacturer successfully leveraged a centralized data store in AWS to create a generative AI chatbot that responds to operators’ questions. This implementation led to improved knowledge management and streamlined operations. “Our collaboration has equipped our operations with a straightforward and effective solution to improve productivity and capture value,” said Roshan Shah, vice president of applied AI and products at Georgia-Pacific. This highlights the significant impact of GenAI on their business.

Data Standardization

After centralizing data, the next important tenet of a modern industrial data strategy is to standardize that data for analytics or Machine learning. To lean and standardize data on AWS, you would typically use ETL (extract, transform, load) to combine data from different sources. AWS Glue is one of the services that can help. It’s a serverless data integration service that helps to discover, prepare, move, and integrate data from multiple sources. After the data goes through this process, the data is placed into a single database or data warehouse to address various use cases.

Challenge 2: Return on Investment

Manufacturers face challenges calculating ROI for GenAI, as many benefits like better decision-making are difficult to measure. Hidden costs such as data preparation, training, and system integration make it challenging to determine actual implementation expenses. Long delays between implementation and seeing results make it hard to link improvements directly to GenAI usage, and this is especially true when running multiple projects at once. The fast-changing nature of GenAI technology, coupled with data quality issues and skill gaps creates uncertainty in ROI calculations. Therefore, companies must regularly review and update their ROI projections to maintain accuracy.

Best Practices for calculating ROI

Calculating ROI for GenAI projects in manufacturing requires a comprehensive approach that considers direct and indirect benefits while accounting for implementation and operational expenses. Before AI implementation, manufacturers should establish clear baseline metrics such as current production rates, downtime and quality control statistics, maintenance costs, and labor efficiency measurements.

Best practices for calculating GenAI ROI in manufacturing start by focusing on specific, measurable use cases such as downtime reduction, quality improvements, or production throughput increases. These metrics can be monitored using tools like Amazon Cloudwatch and Amazon SageMaker together with AWS IoT SiteWise. Financial considerations should include direct costs (licenses, infrastructure, training) and potential benefits (savings, revenue, productivity). Indirect benefits include faster onboarding, improved safety, innovation, and waste reduction.

A phased implementation approach, beginning with proof of concepts (PoCs), allows for better risk management and more accurate ROI calculations based on real-world results. Regular assessment of AI model performance through established KPIs is crucial, including tracking metrics like model accuracy, processing time, and business impact. Finally, ROI calculation must consider the broader impact, including system integration and human factors such as time saved and improved decision-making capabilities. Effective training and change management strategies are essential for its success.

For example, consider an automotive company that implemented a GenAI chatbot to assist technicians with equipment repairs. Their initial ROI projection was 24 months, based solely on direct labor savings. This calculation might significantly underestimate the technology’s true value by overlooking critical factors.

These include reduced unplanned production downtime, enabling junior technicians to work independently, and providing operators with machine adjustment recommendations for better productivity When these broader operational benefits were properly incorporated, the actual ROI period can be shortened dramatically. This illustrates how manufacturers often undervalue generative AI’s cascading impacts beyond direct labor metrics.

Challenge 3: Training and Adoption

Manufacturing organizations often face significant hurdles when upskilling their workforce for digital initiatives. The diverse technical backgrounds of employees, varying shift schedules, and the rapid pace of technological advancement can make training a hefty task. However, AWS Training and Certification offers manufacturing leaders a comprehensive solution to address these challenges.

Training, Certification, and Immersion Days

Manufacturing teams can benefit from AWS’s flexible learning approaches that accommodate diverse operational needs. Live instructor-led courses provide hands-on experience with real-world manufacturing use cases. Additionally, digital courses through AWS Skill Builder allow employees to learn at their own pace, coordinating training with their production schedules. For practical application of these skills, manufacturers can leverage AWS Immersion Days—free, guided, hands-on experiences with step-by-step instructions for building end-to-end GenAIsolutions.

Additionally, manufacturing teams can engage their AWS Account representatives to host custom AWS Immersion Days. During these, AWS experts lead workshop sessions using provided AWS accounts, creating a supportive environment for teams to learn together. For organizations looking to build foundational knowledge, AWS Educate offers hundreds of hours of free courses accessible to employees regardless of technical background.

Manufacturers can implement structured upskilling programs using AWS Learning Plans, which organize training content from foundational to advanced levels for specific manufacturing roles or solutions. These curated pathways help production teams, operations staff, and engineering personnel build relevant skills progressively. The journey culminates with AWS Certifications aligned to AI and machine learning, which are critical in smart manufacturing.

These certifications provide employees with industry-recognized credentials that validate their expertise. This structured approach to workforce development not only accelerates digital transformation initiatives. It also boosts employee retention by demonstrating a commitment to professional growth in an increasingly technology-driven manufacturing landscape.

If help is needed, you can leverage AWS Professional Services or our AWS Partner network to design, build, and deploy custom manufacturing GenAI solutions. This approach also ensures knowledge transfer to your teams for long-term success and self-sufficiency.

Challenge 4: Security and Compliance Challenges with Generative AI

Generative AI’s integration into manufacturing operations presents uncommon security challenges that extend beyond traditional cybersecurity concerns. As manufacturers increasingly adopt GenAI for optimization, maintenance, and quality control, they must safeguard AI models against adversarial attacks, data poisoning and model manipulation. These security breaches could lead to compromised production quality, equipment damage, or intellectual property theft, making it crucial for manufacturers to implement robust security frameworks.

Protecting Generative AI Applications

The security of training data and model integrity in manufacturing is improved through various AWS GenAI services. For example, Amazon Bedrock Guardrails provides comprehensive safeguards for secure generative AI applications in manufacturing with unique protective features. It employs Automated Reasoning to prevent factual errors from hallucinations to ensure logically accurate and verifiable responses. The system can block up to 88% of harmful multimodal content and filter over 75% of hallucinated responses in different use cases.

Guardrails offer customizable content filtering, sensitive data protection, and topic controls across foundation models for consistent security. This makes it particularly valuable for manufacturers who need to protect intellectual property and ensure accurate AI responses in critical operations. Using Amazon Bedrock Agents and knowledge bases, manufacturers can secure their operations while meeting AI compliance requirements.

Additionally, Manufacturers can leverage these guardrails with AWS Private Endpoints and Virtual Private Clouds configurations to ensure secure GenAI workloads within their private networks. The guardrails can be customized to filter harmful content that affect manufacturing operations, while AWS Key Management Service encryption protects model artifacts and training data. Overall, this multi-layered approach helps prevent security vulnerabilities, hallucinations and ensure compliance with GenAI Applications.

AWS infrastructure is built to satisfy the strict security requirements of high-sensitivity organizations like governments and global banks. Hence, manufacturers can run and scale their workloads on AWS with confidence, knowing their designs, software, and production processes remain secure. AWS undergoes extensive audits by multiple independent third-parties, who thoroughly evaluate the breadth and depth of AWS’s secure environment. Beyond the third-party audits, manufacturers can also closely monitor and audit their own AWS environment. AWS provides a wide range of dedicated security services, including AWS CloudTrail, Amazon CloudWatch, and Amazon GuardDuty. These tools provide real-time monitoring, anomaly detection, and automatic remediation while providing visibility into account activities.

AWS supports 143 security standards, including manufacturing-specific ones like OPC-UA and CESMII, and over 300 security, compliance and governance services and features. With these customers can meet global compliance requirements including NIST 800-171, ISO/IEC 27001:2022, and HIPAA/HITECH. Services like AWS Config, AWS Security Hub and AWS Audit Manager automate compliance monitoring to help manufacturers maintain security standards consistently.

Challenge 5: Integration with Legacy Systems

Manufacturing companies challenged to integrate GenAI applications with their legacy systems built on older, proprietary technologies. Deloitte’s 2025 Manufacturing Industry Outlook, reveals 70% of manufacturers surveyed identified challenges with data as their main barrier to AI adoption. Many manufacturers report that AI projects fall short due to infrastructure limitations and system compatibility issues.

Technical debt remains an issue, particularly in the manufacturing sector. McKinsey reported 30% of CIOs surveyed believe that 20% of their budget—dedicated to new products—is diverted to resolving issues related to technical debt. In IT, technical debt refers to the implied cost of not keeping technology components such as computers, servers, and so on. at a state where the current business and technology landscape requires them to be.

Those challenges extend to legacy protocols and lack of standardization. For instance, automation system communication protocols such as Modbus, which are widely adopted by vendors, developed in the late 1970s. Multiple connection protocols are often spread between different plants of the same manufacturer. An AWS blog, Connecting Industrial Assets and Machines to the AWS Cloud discusses these challenges faced by manufacturers. Specifically, in securely connecting and ingesting data from their industrial assets to the cloud.

AWS offers customers multiple solutions to help solve these integration issues, including:

Industrial Data Fabric (IDF)

IDF is one solution where AWS and Partners are helping manufacturing companies harness data into an asset, no matter the complexity of an environment. IDF offers multiple solutions to help customers to ingest, store, contextualize, and act on manufacturing data across the value chain. For example, Infosys faced challenges such as high operating costs due to outdated systems and limited visibility across different IT and OT applications. Infosys prevented 800+ minutes of downtime, averted six machine breakdowns, and avoided 12 critical incidents over a six-month period by leveraging IDF. Find more about their use case in this blog.

IDF lays a critical foundation for Industry 4.0 at scale, and provides economical, secure, and accessible datasets. High-quality data enables business users to streamline operations through data-driven decisions in key areas: quality control, predictive maintenance, production efficiency, inventory optimization, process improvements, and sustainable supply chain management Here are Part 1 and Part 2 on IDF solution best practices.

AWS IoT Edge Solutions

AWS edge services process, analyze, and store data close to your factory edge, deploying APIs and tools to locations outside AWS data centers. Build high-performance applications to process and store data close to where it’s generated, enabling ultra-low latency, and intelligent, real-time responsiveness. AWS IoT Greengrass is an open-source edge runtime cloud service for building, deploying, and managing IoT applications on edge devices. It enables local data processing, ML-based predictions, and autonomous reactions while maintaining secure communication with the AWS Cloud. FreeRTOS, a leading open-source real-time operating system, powers microcontrollers and small microprocessors with its responsive kernel. AWS IoT ExpressLink provides qualified hardware connectivity modules that communicate through UART interface, enabling secure access to AWS IoT Core.

Conclusion

The manufacturing industry is facing a turning point in adopting AI technology, particularly in the adoption of GenAI technologies. While GenAI can revolutionize manufacturing through process optimization, maintenance, and knowledge automation- the five challenges outlined in the blog frequently hinder its successful adoption. Manufacturers can successfully and profitably adopt GenAI by combining robust data strategies, IoT-integrated legacy systems, ROI optimization tools, workforce training, and advanced security services.

To explore how AWS enables GenAI in manufacturing, check this video on how
Generative AI is Revolutionizing Automotive and Manufacturing: Insights from AWS.

Special thanks to Naimisha Pinna and George’son Tib., Solution architects at AWS for helping write this blog.