We are embracing the transformative power of AI digitalization to drive innovation and operational excellence. Our approach to impacting science and manufacturing is strategic and balanced: a "Goldilocks” approach—not too much, not too little. By leveraging the right tools for the right job, tools that are evolving at an unprecedented pace, we operate with agility, maintain speed, and effectively manage the important balance of compliance, precision, and innovation.

Biomedical discovery and research generate enormous volumes of knowledge, yet much of it remains locked in hard-to-find PowerPoint slides, fragmented analyses, and disconnected data systems. As teams evolve, critical context around what was done, why decisions were made, and which data and models informed them is often lost. This presentation outlines an end-to-end knowledge management capability that spans discovery and research, leveraging knowledge graphs, ontologies, and generative AI to link unstructured content with analytical models and underlying raw data. By integrating directly with workflows, collaboration tools, and decision-support systems, this approach transforms knowledge from static artifacts into a continuously accessible, reusable, and decision-ready asset.

Drug discovery requires integrating diverse biological, chemical, and genomic data, yet the benefits of robust FAIR practices are often less visible than the effort required to implement them. BioRels changes this by making FAIR both effortless and impactful. Its automated, schema-driven infrastructure standardizes incoming sources, harmonizes ontologies, captures full provenance, and enables reproducible reconstruction across billions of data points. By turning data preparation into a fast, reusable, and transparent process, BioRels removes friction and accelerates key discovery activities—from target identification and validation to mechanism exploration and AI enablement.

In a chemical registration system, the validation and standardization of submitted chemical structures are crucial for ensuring that the deposited representation of registered compounds is accurate, unambiguous, and compliant with internally defined business rules and QA criteria. This aspect of the registration process not only plays a central role in identifying registered chemicals but also significantly contributes to the quality and reliability of data available to downstream applications and users. Over time, for a system already in production, the evolution of validation criteria and standardization rules and their adaptation to evolving requirements necessitate careful consideration and a structured approach to implementation and testing. This presentation discusses recent advancements in the Small Molecule Registration (SMR) product used at Novartis Biomedical Research. It highlights the replacement of the original validation and standardization component with a newly implemented module, which was subsequently published as open-source code and contributed to the RDKit cheminformatics toolkit.

While AI dominates early-stage drug discovery discussions, pharmaceutical organizations struggle to connect these innovations to downstream clinical operations and real-world patient care. This panel tackles three critical gaps in current AI implementation: 1) integrating AI across R&D functions from target identification through clinical development, 2) leveraging digital health platforms and AI as the bridge between research insights and patient outcomes, and 3) enabling cross-team collaboration and external partnerships through shared AI infrastructure. Industry leaders from biopharma, hospitals, and digital health will debate what it really takes to break down silos between discovery, development, clinical, and commercial teams. Through candid discussion of real implementations, panelists will explore how to build communication channels that connect internal teams with external partners (hospitals, payers, and patients), creating an AI ecosystem that delivers measurable impact across the full therapeutic lifecycle.