"At Sanofi, AI has shifted from experimentation to becoming a vital part of our infrastructure. It now powers our R&D decisions, our supply chain and manufacturing processes, and most importantly how we discover and develop medicines. All businesses that have implemented AI in an impactful way face challenges, such as skills gaps and uncertainty, but you move beyond that by embedding AI deeply into teams and systems. This enables AI to become a key, reliable source of sustained productivity and innovation."
"The critical factor in 2026 and beyond will be enterprise-scale implementation, shifting from experimenting with AI to operationalizing it at the core of how companies work. This will be the tipping point where AI speculation ends, and where it becomes a fundamental driver of growth. As more organizations reach this stage, debates about bubbles have already given way to evidence of durable, long-term value in areas including new drugs discovered by AI, optimized supply chain and manufacturing and preventative medicine powered by new technologies."
"According to a Boston Consulting Group report, generative AI has the potential to accelerate early-stage drug breakthroughs, reducing timelines by 25% or more. At Sanofi, we are already seeing this materialize with dramatic results. Combining machine learning and data integration with lab research has helped us discover 10 completely news drug targets in just one year. AI is no longer just assisting R&D efforts, it is actively shaping decision-making. Our drug development committee meetings begin with an AI agent's assessment"
At Davos this year, AI emerged as a central force driving innovation and growth across sectors. Companies are moving from speculative experimentation to embedding AI at the core of operations to deliver sustained productivity and innovation. At Sanofi, AI now powers R&D decisions, supply chain and manufacturing processes, and drug discovery workflows, enabling the identification of multiple new drug targets in a single year. Generative AI can shorten early-stage drug timelines by roughly 25% or more. Enterprise-scale implementation and deep integration into teams and systems will be critical in 2026 and beyond to realize durable, long-term value and overcome skills gaps and uncertainty.
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