Senthil Nathan Rajendran, Co-founder & CEO, Tenzai Systems

Senthil is the Founder & CEO of Tenzai Systems, an award-winning AI and Data Science consulting firm that is focused on democratizing AI and making it accessible, scalable, and responsible for enterprises. He is a highly accomplished thought leader, innovator, and technologist with over 17 years of experience in the fields of Artificial Intelligence and Data Science. He has consulted large Fortune 1000 clients across multiple verticals, including Retail, Consumer Goods, Travel, Manufacturing, and Financial Services. In addition to his consulting work, Senthil has filed 7 patents in areas such as Augmented Analytics, Generative AI, Natural Language Processing, and AutoML. He is the founder of Tenzai Systems, an award-winning AI and Data Science consulting firm that is focused on democratizing AI and making it accessible, scalable, and responsible for enterprises.


Generative AI is quietly reshaping the manufacturing landscape, one innovation at a time. A recent McKinsey report highlights the potential of generative AI to contribute an astounding USD 13.5 trillion in additional economic activity by 2030, equivalent to 14 percent of global GDP. Among the sectors, the manufacturing sector is poised to reap substantial benefits, with a potential value capture of USD 270-460 billion. Major manufacturers from the automotive, aerospace, pharmaceutical, and semiconductor industries have already embraced Generative AI, focusing on key areas such as Personalization, Design, Automation, and Optimization.

Generative AI Use Cases in Manufacturing & Production

In this article, we’ll explore some interesting generative AI applications for manufacturing & production. We’ll also showcase real-world case studies to illustrate how manufacturers are leveraging Generative AI to transform their production processes.

  • Virtual assistants for the Production team

Manufacturing and production engineers spend 40% of their time on non-value-added tasks such as information search and data checks. Generative AI offers a solution by enabling manufacturing firms to develop virtual assistants for engineers. These AI assistants can assist in information retrieval and data analysis through natural language conversations.

  • Automated machine data analysis

A McKinsey study finds that just 0.5% of machine data gets analyzed, citing challenges in data quality, data volume, complexity, and analytics expertise. Generative AI offers a solution by automating data processing, uncovering patterns, and detecting anomalies in machine data. This helps manufacturing firms prevent equipment failures, improve quality control, and boost employee productivity.

  • Automated Production reports

According to Gartner, employees spend an average of 10 hours per week on production report creation. This time investment varies based on factors such as process complexity, factory size, data automation, and reporting tools. Generative AI has the potential to revolutionize this process by automating data analysis and report generation. It can directly connect to the production systems, process large volumes of data rapidly, and generate comprehensive reports with minimal human intervention.

  • Gen AI-based production planning & scheduling

Over 70% of manufacturers grapple with production planning and scheduling challenges, leading to lost sales, elevated costs, and customer dissatisfaction. Key hurdles include data complexity, diverse systems, and manual processes. Generative AI emerges as a transformative solution, automating scenario planning and scheduling. By incorporating scenario simulation, complex optimization, real-time adaptation, and continuous improvement capabilities, manufacturers can enhance planning quality and overcome these challenges effectively.

  • Synthetic Machine data generation

Predictive maintenance models require access to large amounts of data to create AI/ML models. This data can be difficult to obtain, especially for older assets that have not been equipped with sensors. AI-generated synthetic data can be used to supplement real data when there is not enough data available to train a predictive maintenance model. This can be the case for older assets that don’t have adequate sensors, or for assets that are operating in harsh environments where data collection is difficult.

Generative AI: Real World Case Studies in Manufacturing 

Discover how manufacturers are reshaping production with successful Gen AI implementations

  • M&M deployed Gen AI-powered Virtual assistants for the production team to reduce downtime

Mahindra & Mahindra (M&M), a leading automotive manufacturer, has leveraged generative AI to empower its workforce. By aggregating technical data and maintenance records of industrial robots, M&M developed a Virtual Assistant using LLMs. This tool equips production teams to swiftly address technical issues by querying them with error codes. The Virtual Assistant then provides step-by-step instructions, reducing machine downtime and expediting issue resolution.

  • BMW utilizes Generative AI to optimize manufacturing operations

BMW Group has implemented a Quantum-Inspired Generative Model, known as generator-enhanced optimization (GEO) for plant scheduling. In this novel approach, classic optimization solvers first analyze data and generate schedules. Then, GEO’s quantum-inspired generative models refine these results. Notably, GEO often surpasses traditional solvers by minimizing assembly line idle time while meeting monthly vehicle production targets.

  • Merck creates synthetic images using generative AI for defect detection

Merck, a global pharmaceutical leader, has harnessed generative AI to tackle the common challenge of limited training data for AI/ML models. Facing the shortage of defect images for inspections worldwide, Merck’s AI Engineers leveraged generative AI techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders. They created synthetic defect image data for complex defects. These synthetic images played a critical role in training defect detection models, resulting in remarkable accuracy improvements and a significant reduction in time-to-market for new models.

  • Hitachi leverages Generative AI to train new workers in maintenance & manufacturing.

Hitachi, a major equipment manufacturer taps into generative artificial intelligence to transfer expert skills in maintenance and manufacturing to younger workers to mitigate the impact of mass retirements. The AI system generates training videos depicting challenges and accidents in sectors like railways and power stations by simulating real-life scenarios on screens in a 10-square-meter room, providing immersive training experiences for their employees.

  • Sereact is giving vision to warehouse robots through Generative AI

Sereact is a leading provider of AI-based robotic solutions provider based out of Germany. Sereact revolutionizes warehouse operations by enabling robots to autonomously perform “pick-and-pack” tasks, which typically account for 55% of warehouse costs. Unlike competitors, Sereact employs a transformer model, trained on billions of simulated images. This image-trained model also interacts with a language model, which allows the operator to type in text commands to a chat interface, which will automatically create a plan of action for the robot to complete its task.


Generative AI is not just a fleeting trend; it’s going to be a permanent fixture in the manufacturing landscape. Its potential spans R&D, Innovation, Production, Quality, Sales and commercial, IT, and Customer Experience. However, there’s no one-size-fits-all formula for choosing generative AI use cases. Success lies in identifying the right applications based on your AI maturity level. Begin with small POCs and less complex use cases, then scale up. Prioritize assessing business impact, AI readiness, and data readiness before embarking on Gen AI endeavors. By following these principles, manufacturers can chart a path to innovation and excellence in the era of AI-driven manufacturing.

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