Abstract 

Digital transformation in chemistry, manufacturing and controls (CMC) is advancing rapidly through technologies such as artificial intelligence (AI), machine learning, modelling and simulation. However, regulatory frameworks and expertise struggle to evolve at the same pace. The absence of harmonised terminology, evaluation methods and credibility standards creates uncertainty for industry and regulators, limiting the use of digital tools in regulated environments. Early regulatory engagement, consistent approaches to data provenance, and clear criteria for assessing model risk and reliability are needed to ensure confidence in digital methods. The Digital CMC Centre of Excellence in Regulatory Science and Innovation (CERSI) is addressing these gaps through the development of a practical framework, case studies and supporting tools to guide regulatory use of predictive models.  

This article aims to raise awareness of how realising the benefits of digital transformation in CMC depends on early alignment between innovators and regulators, underpinned by shared language and credible, risk-proportionate frameworks for evaluating predictive models across their lifecycle. It describes how the Digital CMC CERSI, through a harmonised framework, case studies, a sandbox and training, is establishing a practical, science-based approach to increase confidence and accelerate the safe regulatory adoption of digital tools.