Artificial Intelligence (AI) and Machine Learning (ML) are playing an increasingly central role in pharmaceutical manufacturing and control. A position paper published by IFPMA in March 2026 outlines both the opportunities and the challenges associated with their adoption across the sector.
Enhancing efficiency and enabling new applications
According to the document, AI has the potential to accelerate decision-making processes and enhance operational efficiency, ultimately leading to more robust manufacturing processes and shorter cycle times. By automating repetitive tasks with higher accuracy, AI can also deliver significant benefits in terms of data integrity and analytical capabilities.
Several real-world applications are already emerging, including predictive and control models such as digital twins of manufacturing processes. In parallel, generative AI is being used for text-related activities, for example in the preparation of regulatory documentation.
Regulatory alignment and the need for a common framework
From a regulatory perspective, a risk-based approach to the implementation and updating of AI and ML systems has been adopted by many international authorities. The paper highlights a strong alignment between the requirements set by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA), suggesting a growing convergence at the global level.
IFPMA also points to the opportunity to develop a sector-specific AI risk framework for the pharmaceutical industry, alongside the promotion of global harmonisation of regulatory standards to support wider adoption in manufacturing.
Addressing risks and ensuring sustainable adoption
The document highlights several AI-specific risks that require careful management, including automation bias, the potential erosion of human oversight, challenges related to explainability and interpretability, autonomous learning, data quality, and lifecycle maintenance.
To address these challenges, the position paper proposes a set of actions aimed at enabling safe, effective and scalable implementation of AI and ML technologies across the pharmaceutical sector.