The advent of Generative AI in the form of foundation models has reshaped the AI landscape, offering broad out-of-the-box capabilities. As many foundation model applications focus on processing and generating text, and a significant amount of information both for engineering projects (e.g. standards, specifications) and operations (e.g. shift book entries and work orders) is recorded as text, a number of value-adding applications become possible – specifically in the area of knowledge management. Learn about how Evonik has deployed the technology to support both engineers and operations staff in their daily work. In this session you will learn:

• the core distinctions between Generative AI and Traditional AI, and why these differences matter for production
• how Evonik is successfully applying Generative AI and Traditional AI to support both engineering and operations
• a look behind the curtain of the architecture behind the developed applications

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?

• How do we define Knowledge Management in Engineering and Operations?
• What use cases require optimized Knowledge Management in Engineering and Operations?
• How does Generative AI differ fundamentally from Traditional AI, and what does this mean for Knowledge Management?
• What are the most common examples of cases, in which the use of Generative AI for Knowledge Management has led to unexpected outcomes?
• What specific challenges do companies face when implementing Generative AI approaches in Engineering and Operations, and how can they overcome them?

The moderators will present the results of the World Café Round Tables and all the most important questions, challenges and solutions arising from the event will be discussed.