Document Processing · Real-time Analytics · Risk Management
Novartis R&D needs to turn large volumes of scientific, clinical, operational, and real-world data into faster, higher-quality decisions across drug discovery and development, reducing cycle times, surfacing safety risks earlier, and improving trial planning in a domain where success rates are low and delays are costly.
Reported impact
Novartis aims to reduce drug discovery and development timelines, improve decision quality, surface risks earlier, shorten iterative cycles, and help teams bring therapies to patients faster with a higher likelihood of success.
Novartis describes an in-house, AI-enabled R&D decision-support approach that spans research, preclinical work, clinical development, and documentation. Data is ingested from human genetic and multi-omic datasets, imaging, clinical trial results, decades of published research, real-world evidence, protein information, prior chemistry, and clinical and operational study data. AI models are used to integrate these sources for target identification and validation, while generative chemistry methods explore molecular design spaces and optimize multiple properties in parallel. In development, Generative AI paired with knowledge graphs summarizes prior studies and evidence to support protocol drafting, and trial computational twins simulate what-if clinical design scenarios. AI-enhanced analytics monitor supply, recruitment, and study risks and recommend next best actions. The article does not name a storage platform, but it does describe approved-source workflows, privacy and quality controls, and expert human review before outputs are used in regulated R&D decisions.
Novartis feeds genetic, multi-omic, imaging, trial, literature, real-world, protein, chemistry, clinical, and operational data into AI models and analytics. Those models generate target insights, molecule proposals, safety signals, protocol summaries, trial simulations, and study-risk recommendations, which are then reviewed by Novartis scientists and clinicians to guide R&D, trial design, and regulatory documentation.
"AI models can integrate evidence from human genetic and multi-omic datasets, imaging, clinical trial results, and decades of published research to surface previously underappreciated disease drivers"
"AI-enabled tools such as Generative AI, paired with knowledge graphs, can rapidly summarize what has worked—and what hasn’t—across prior studies and real-world evidence"
"Generative AI can create well-structured first drafts using approved sources, which Novartis experts then review and refine"
"Novartis R&D is harnessing digital technologies to make smart decisions faster, aiming to reduce the time it takes to bring new options to patients"
"Novartis teams are using advanced, AI-enhanced analytics to surface early risk signals across supply, monitoring, and recruitment, and to recommend ‘next best actions’ when risks are identified"
"Simulations using trial computational twins can also help predict the operational implications of clinical design elements"
"With generative approaches, teams can propose new structures informed by protein information and prior chemistry, explore large, molecular-design spaces digitally, and optimize for multiple chemical properties in parallel"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
In-house Sample Feb 24, 2026 Sanofi Sanofi needed a faster, more objective way to decide which R&D programs to advance, accelerate, or stop while weighing costs, clinical trial enrollment timelines, unmet need, competition, and each program's probability… more than one billion data points across Sanofi Aily Labs · Plai In-house Sample Jun 19, 2025 Sanofi Sanofi needed to replace siloed, manual manufacturing and supply workflows with a connected, data-driven operating model that could improve medicine quality and safety, prevent supply disruptions, speed product… 3,500 annual Product Quality Reports Modulus · advanced analytics · generative AI 
AWS Sample Sep 11, 2024 Georgia-Pacific The lack of a centralized, readily accessible knowledge base often leads to reduced machine productivity, prolonged machine downtime, and higher troubleshooting and repair costs. Making phone calls to experts and… Accelerated operator onboarding Amazon Bedrock · Amazon Kinesis 