Current drug development is costly and lengthy, with an estimated average cost of USD 2.6 billion and a 12-year timeline, due to extensive regulatory requirements and high attrition rates. The emergence of precision medicine and gene therapies, often priced in the millions per patient, currently strains health budgets. A major challenge for health systems in the forthcoming years will be to control expenses in the context of a coming wave of innovative drugs fueled by AI. While AI promises to enhance drug development efficiency and reduce associated costs, ensuring these innovative treatments are accessible to patients and affordable for health systems remains crucial. The determination of fair drug pricing is a complex issue that balances societal access, budgetary constraints, and innovation incentives. AI’s role will be pivotal in reducing drug development costs, enhancing clinical trial success, and enabling value-based pricing by analyzing real-world performance data. Other savings could come from a better use of health data, the development of telemedicine, the digital transformation of hospitals, the efficient management of patients, as well as a transition toward preventive medicine.