Concrete industrial case studies of decision-making AI

Artificial Intelligence and Machine Learning (AI/ML) are transforming data analysis and process optimization across multiple industries. Their impact is already visible in areas such as predictive maintenance, quality monitoring, and operational efficiency and the potential for pharmaceutical manufacturing is significant.

In this webinar, we will present three real-world industrial AI/ML use cases from different sectors. Each example will demonstrate how advanced analytics and machine learning can be applied to improve efficiency, strengthen quality, and support data-driven decision making.

A special focus will be placed on tools and methodologies that enhance model robustness, reliability, and predictive performance. These principles are essential for organizations operating in regulated environments and provide valuable insights for pharma professionals seeking to adapt AI/ML approaches to their own manufacturing processes.

Whether you are working in pharmaceutical production, quality, or digital transformation initiatives, this session will offer practical perspectives on how AI/ML can be adopted responsibly and effectively.

Participants will learn:

- How AI/ML is used today in real industrial settings
- Which tools and methods help improve model reliability and robustness
- What practical lessons can be transferred into pharmaceutical manufacturing
- How data-driven approaches can support quality and operational excellence