At Davos this year, AI was a key pillar of discussion. Increasingly, people recognize the impact it is already having on innovation and growth. I believe that we will see in the months ahead companies in all sectors proving that AI is not a speculative moment in time but a durable engine of transformation that’s fundamentally reshaping how we work.
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.
The New Era of AI-Driven Drug Development
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 of whether a drug should advance to the next trial phase. Crucially, the agent does not simply give a yes or no answer but fully contextualizes each decision. It compares the asset’s prospects against others in development and assesses its opportunity cost relative to alternative uses of Sanofi’s capital. This is a powerful example of how AI is making drug development not only faster, but smarter.
This transformation doesn’t stop in the lab. AI is also addressing one of the most persistent obstacles in drug development: clinical trial recruitment. AI-powered patient recruitment tools improve clinical trial enrollment rates by 65%. Through AI we can now automate patient eligibility through scanning electronic health records, clinical notes and lab results. This enables higher accuracy in matching patients to complex criteria in trials. We can also determine in real time if a clinical trial site is not enrolling as expected and move our efforts to sites that are progressing more rapidly. Trials that once required months to recruit now find the right patients in days or weeks.
Beyond accelerated target discovery and clinical trial recruitment processes, AI-driven tools are transforming the economics of R&D. Its accelerating early-stage drug discovery and generating scientific insights that can reduce costs by an estimated 50%. These shifts are poised to reshape what kinds of medicines become viable. In the year ahead, we can expect key breakthroughs, particularly in precision medicine, to become more scalable and accessible. Recent reviews underscore that thanks to AI, multi-omics and an unprecedented data infrastructure precision medicine is moving from bespoke targeting to large-scale, population-level care models.
Building the Next Generation of Supply Chains & Manufacturing
AI’s ability to detect vulnerabilities in supply chains is unparalleled. Through enhanced visibility and real-time tracking, AI systems provide unprecedented insights into inventory levels and product movement.
At Sanofi, AI-driven supply chain management has enabled the company to avoid $300 million in revenue risk and predict 80% of low inventory risks before they occur. By expanding access to data and increasing transparency across functions, organizations can make better informed decisions in a timely manner. The impact is tangible: 68% of supply chain organizations have already integrated AI to enhance traceability and visibility, resulting in a substantial 22% increase in operational efficiency. These gains will only accelerate over the next few years as AI becomes further embedded in global supply networks.
Beyond visibility and forecasting in supply chain operations, AI is reshaping the way we approach the manufacturing of medicines. AI is providing end-to-end support in manufacturing through enhancing efficiency, product quality and safety and enabling real-time monitoring that ensures consistent, reliable output. According to McKinsey, AI-driven analytics can significantly maximize yield which not only ensures patients are receiving critical medicines faster but also improves the cost and sustainability of manufacturing operations.
Advancing Preventative and Predictive Care
The next frontier in prevention is early intervention supported by remote patient monitoring and digital tools. One study of more than 1,100-plus patient encounters found that remote patient monitoring cut hospitalizations by nearly 60%. For chronic diseases, the combination of wearables, symptom-tracking apps and environmental sensors is proving to be transformative. In COPD patients, a digital program integrating these tools achieved 94% sensitivity and 90.4% specificity in predicting exacerbations enabling clinicians to intervene before crises occur. These types of integrated digital therapeutic models are reducing emergency visits and admissions for vulnerable populations. As AI accelerates timelines across the pharma and healthcare industry, our ability to discover, predict and intervene earlier will only expand.
The future of prevention will be defined by the convergence of vaccines, therapeutics, data and intelligent monitoring. By pairing scientific innovation with AI-driven insights, we have the opportunity to shift the healthcare approach from reactive treatment to proactive, continuous protection. The result will be improved outcomes, reduced costs and ultimately transformation of patient lives at scale.
We are in a new era powered by AI across business functions. 2026 promises accelerated momentum of AI-powered transformation in the pharmaceutical industry as we continue to pursue the discovery of life-changing medicines for patients. As AI capabilities continue to scale, we’ve already seen the conversation shift away from concerns about a bubble and toward the evidence of durable, enterprise-driven value. What is likely to remain is not hype but sustained growth and meaningful transformation.
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