• Is the current infrastructure equipped to support the modern production methods of smart factories, and if not, what are the primary challenges hindering its adaptation?
• What potential solutions can be implemented to meet the requirements of modern production approaches and overcome the obstacles in adapting the infrastructure? How can we maintain flexibility in manufacturing processes to accommodate changing demands?
• How should concepts for integrating and connecting diverse systems and processes within smart factories be approached? What strategies can be employed to ensure smooth integration and connectivity while optimizing operational efficiency?

Continental has successfully implemented a predictive quality system powered by AI and analytics to detect production anomalies before they lead to defects. By integrating machine learning into its manufacturing process, the company has improved product quality, reduced waste, and enhanced operational efficiency. This session explores how Continental brought predictive quality from concept to reality.

• How did Continental shift from reactive quality checks to predictive insights?
• What types of data were used to train and refine AI models for real-time quality forecasting?
• Which measurable improvements in quality and efficiency were achieved after implementation?

With the rapid evolution of large language models (LLMs) and intelligent automation technologies, AI agents are becoming powerful tools in the mechanical engineering sector. These agents can make autonomous decisions, analyze complex data, and interact with machines and systems in real time—transforming traditional processes across maintenance, production, sales, and service. This panel discussion explores how AI agents are being integrated into Industry 4.0, what their current capabilities are, and what ethical, technical, and operational challenges still stand in the way of widespread adoption. Are we at the dawn of a new era where humans and AI co-pilot the factory of the future?

• What distinguishes AI agents from traditional automation systems and conventional AI applications like chatbots?
• Which use cases in mechanical engineering are showing the greatest value from deploying AI agents today?
• How can AI agents be safely integrated into critical manufacturing processes without compromising reliability and control?
• What are the main challenges in training and governing industrial AI agents—especially in terms of data quality and compliance (e.g., GDPR)?
• How will the role of engineers and operators evolve as AI agents take on more autonomous tasks? Will they replace, augment, or collaborate with human expertise?