NOVARTIS
decisions while maintaining rigorous quality standards and keeping human expertise at the centre.
In clinical development, Novartis is applying AI-enabled tools to support how trials are designed and executed. Generative AI, paired with structured data systems, can help teams rapidly summarise evidence from prior studies and real-world data, draft and refine protocols more efficiently, and pressuretest key assumptions earlier. AI can also support site selection and recruitment by combining clinical, operational and real-world datasets.
Together, these tools are designed to enable faster, better-informed
AI across the value chain However, AI’ s role at Novartis extends well beyond R & D. Across the company’ s manufacturing operations, AI is being applied to improve the yield of biological drug production – a complex process in which living cells are used to generate therapeutic compounds. Higher yield means more medicine from the same production run, with direct implications for cost and supply.
For example, at Novartis manufacturing sites, AI-powered visual inspection has significantly reduced false ejection rates and improved equipment utilisation, increasing efficiency while maintaining the highest quality standards for patients. Further along the supply chain, AI helps determine the most efficient routing for finished products in a logistic network that spans the entire world.
Across the full value chain, AI becomes most powerful when it connects each step, helping teams make better decisions, reduce delays and work more efficiently from discovery through to delivery.
All of this must operate within the tightly regulated framework of Good Manufacturing Practice( GMP) – the set of quality standards mandated by regulators globally to produce pharmaceutical products.
“ In our space, quality is nonnegotiable,” Bernd says.“ Even so, AI has a role to play in quality processes
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