SERVIER and development and discovery processes, while the remainder address manufacturing, commercial and patient service functions.
The pharmaceutical industry has used machine learning techniques for drug discovery and development for years. However, generative AI capabilities have created new opportunities for automation and analysis across business functions.
AI applications in pharmaceutical research include molecular design, compound optimisation, clinical trial design and regulatory document preparation. Commercial applications encompass sales forecasting, market analysis and patient engagement personalisation. The company takes a measured approach to AI adoption, evaluating applications carefully rather than pursuing technology implementations without clear business justification. This methodology reflects industry recognition that AI capabilities require careful assessment to deliver measurable value.
Current market conditions suggest the pharmaceutical industry may be experiencing inflated expectations around AI capabilities. Companies that maintain rigorous evaluation processes can distinguish between practical applications and technology hype.
Servier maintains assessment processes for AI use cases that evaluate technical feasibility, business impact and implementation requirements. This approach helps ensure technology investments support specific business objectives rather than general AI adoption.
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