Gen AI Application for Industrial Automation

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Webinar details

Generative AI is transforming industrial automation by enabling intelligent systems that not only optimize existing processes but also generate adaptive solutions in real time. By integrating large language models (LLMs) and multi-modal AI frameworks with industrial control systems, organizations can automate complex decision-making, enhance predictive maintenance, and improve production efficiency. Applications include intelligent process optimization, automated report generation, failure prediction, and contextual response systems that adapt to real-time data from sensors, machines, and enterprise systems. These capabilities reduce downtime, lower operational costs, and support continuous process improvement across manufacturing and industrial operations.

The webinar will explore practical implementations of Gen AI in industrial automation, focusing on how technologies such as Retrieval-Augmented Generation (RAG), reinforcement learning, and model distillation are deployed to build scalable, efficient, and interpretable AI solutions. Emphasis will be placed on use cases such as real-time monitoring, automated quality control through computer vision, and dynamic workflow optimization. Technical considerations including data integration, edge deployment, model reliability, and safety in automated environments will also be addressed. This session aims to provide a clear understanding of how Gen AI can drive innovation and operational excellence in industrial settings.

• The webinar will be recorded and will be sent out to registered attendees afterwards.
• A certificate of attendance will be provided to attendees who request one near the end of the live webinar session.
• Please note: the time stated on this event is in UTC. You will need to convert this to your own time zone.

Key takeaways from this webinar

• Understand how Generative AI technologies can be applied to optimize and automate industrial processes in real-time.
• Learn about the integration of large language models, sensor data, and edge computing for predictive maintenance and dynamic workflow management.
• Gain insights into deploying scalable and interpretable Gen AI solutions within existing industrial automation infrastructure.

Related courses

This webinar/topic relates to our school of Industrial Automation, Instrumentation and Process Control and is particularly found in the following courses:

• 52886WA Advanced Diploma of Industrial Automation Engineering
• 52872WA Advanced Diploma of Robotics and Mechatronics Engineering
• Online – Bachelor of Science (Industrial Automation Engineering)
• Graduate Certificate in Industrial Automation and Machine Learning
• Graduate Diploma of Engineering (Industrial Automation)
• Online – Master of Engineering (Industrial Automation

To learn more about tuition fees, please click here.

About the presenter

Dr. Krutika Shahabadkar, EIT Lecturer, Swinburne Lecturer and Tutor and Gen AI Research Engineer in Studiosity

Dr. Krutika Shahabadkar is an AI Engineer and Researcher with a strong foundation in developing end-to-end machine learning and Generative AI systems across healthcare, industrial domains, and academic research. Her work centers on integrating large language models (LLMs), Retrieval-Augmented Generation (RAG), and real-time sensor data to create intelligent, scalable solutions that support enhanced decision-making in complex and dynamic environments.

Dr. Shahabadkar brings expertise in fine-tuning LLMs, applying reinforcement learning with human feedback (RLHF), and deploying AI models across both cloud and edge infrastructures. She is dedicated to designing systems that are not only technically sound but also aligned with real-world needs, with a particular focus on low-resource and high-impact settings.

Beyond her research and engineering contributions, Dr. Shahabadkar has taught postgraduate courses in Data Analytics, Machine Learning, and Artificial Intelligence. She has mentored students in both theoretical and applied aspects of AI, fostering the next generation of data scientists and engineers. Her collaborative work spans interdisciplinary teams including clinicians, engineers, and policymakers, with an emphasis on delivering ethical and impactful AI solutions.

Her technical skill set includes proficiency with tools and platforms such as AWS SageMaker, Azure, TensorFlow, PyTorch, Hugging Face, Docker, and CI/CD pipelines. Passionate about responsible AI, Dr. Shahabadkar is committed to building systems that deliver measurable value while advancing equitable access to technology.