This panel examines how modern R&D organizations are building connected laboratory environments by integrating robotics, automation platforms, scheduling tools, and data systems. Panelists will discuss practical approaches to achieving interoperability across ELN, LIMS, analytics, and cloud architectures, along with design principles for connected labs, best practices for data lineage and orchestration, and lessons learned from integrating physical and digital research infrastructure at scale.

This panel examines how organizations scale automation and robotics through enterprise-ready architectures, governance frameworks, and operating models. Panelists will share practical strategies for sustaining transformation beyond pilots and early adopters, including approaches to change management, organizational alignment, and cross-site deployment. The discussion will focus on what it takes to institutionalize automated and robotic capabilities across teams, functions, and locations.

Kick off the morning with small-group roundtable discussions designed to spark collaboration, share challenges, and exchange insights across the Bio-IT community. Attendees gather around themed tables—spanning data ecosystems, AI adoption, foundational models, intelligent labs, translational infrastructure, and emerging technologies—to compare experiences and explore practical strategies. Each roundtable seats 8–10 participants for focused, peer-driven conversation that accelerates problem-solving, strengthens connections, and surfaces cross-functional perspectives before the plenary keynote. Topics will be announced throughout the year on the Bio-IT World website as part of our 2026 theme rollout, with opportunities for attendees and partners to propose table themes. If you have a topic to suggest or would like to participate as a moderator, contact Cindy Crowninshield at ccrowninshield@healthtech.com.

This panel explores the design of modular and flexible automation infrastructure that supports multi-modal discovery and heterogeneous robotic systems. Panelists will discuss platform extensibility, orchestration strategies, and approaches to balancing standardization with scientific flexibility. The discussion will highlight architectural patterns for building future-proof robotic platforms, integrating diverse instruments, and scaling automation across modalities without compromising adaptability or research velocity.

This panel examines how high-quality data generated by automated and robotic laboratory systems enables AI-driven experiment planning, optimization, and closed-loop learning across the DMTA cycle. Panelists will discuss the technical and organizational requirements for AI-ready lab data, real-world examples of AI-enabled decision-making, and practical steps for progressing from automated execution to predictive and adaptive experimentation at scale.