No More Boxes on Slides.
Examples of Workflow Orchestration and Their Impact from R&D to Production on Demand

This webinar is co-hosted by SciY (a Bruker company) and Novalix, who have jointly developed and will deliver the content of the session.

“Confused” (or perhaps overwhelmed) when hearing semantics, ontologies, schema, knowledge graphs and digital twins? This webinar is different. As experimental scientists, Julien Marin (Novalix) and Anna Codina (SciY) will focus on how automation and AI-powered experimentation are applied in day-to-day experimental settings, from discovery to manufacturing.

Through concrete examples, we will show how labs are moving beyond isolated automation toward connected, end‑to‑end workflows: integrating data directly from instruments, enriching it with scientific context, applying automated analysis, and using AI to guide the next experimental step.

We will walk through practical scenarios such as reaction screening, structure verification, and closed loop experimentation, as well as their extension into AI piloted self-contained distributed manufacturing. The emphasis is not on theory, but on what works in practice: what has been built, what has been tested, and what delivers value today.

Attendees will come away with a clear understanding of how to move from fragmented tools and manual workflows to connected experimentation systems where data, automation, and AI are combined to accelerate decisions.

Thursday, July 16th, 2026, 11:00 AM EDT | 05:00 PM CEST

• What real workflow automation looks like beyond concept diagrams
• How to connect instruments, data, and systems into end-to-end experimental workflows
• How experimental data is captured, structured, and reused to enable automation and AI
• How closed loop experimentation works in practice
• How AI can move from post data analysis to actively guiding experiments and processes
• How these approaches extend from R&D into process optimisation and continuous manufacturing

This webinar is ideal for professionals who want to move from concepts to real implementation:

• Scientists and lab leads working with automation or high throughput experimentation
• R&D and digital lab leaders looking to turn strategy into execution
• Automation, robotics, and workflow engineering teams
• Data, informatics, and AI teams supporting experimental workflows
• Process development, PAT, and manufacturing innovation teams
• CRO/CDMO leaders interested in scalable, connected experimental platforms