AI in Industrial R&D: Hype vs. Reality

Artificial intelligence and machine learning are increasingly shaping industrial R&D—but many initiatives fail to deliver tangible results. The challenge is rarely the technology itself. Instead, missing foundations such as structured data, consistent context, and integrated digital lab workflows prevent AI from creating real value.

This webinar provides a practical perspective on how AI can be successfully applied in laboratory environments. It highlights the critical role of scientific data management, lab data integration, and well-designed data architectures as prerequisites for data-driven product development.

Learn where machine learning and AI for materials science truly make an impact—and where their limitations become clear. The session also outlines how lab informatics systems, including LIMS and electronic lab notebooks, contribute to scalable and reliable scientific data pipelines.

Key takeaways:

• Why AI projects in industrial R&D often fall short
• The importance of research data management and structured lab data
• How digital lab workflows enable effective AI adoption
• Where machine learning creates measurable value in materials informatics
• What it takes to turn data into decision-ready insights

A must-watch for professionals working in industrial R&D, lab digitalization, and data-driven material development.