Northeastern University research argued that AI can add predictive control to cell and gene therapy manufacturing—moving from reactive troubleshooting to batch-failure prediction, real-time monitoring, and “digital twin” simulation. The team emphasized that CGT processes are intrinsically variable because every manufacturing step—starting material sourcing through analytical testing, storage, and delivery—can shift outcomes. The article also flagged adoption barriers: successful AI in CGT requires high-quality, curated datasets, digital manufacturing infrastructure, and multidisciplinary governance that regulators can trust. In other words, model performance depends as much on data ecosystems as on algorithms. For the CGT sector, the message is practical: AI can identify critical process attributes, optimize parameters, and strengthen quality systems, but it is not plug-and-play. Organizations must invest in workflow integration and data governance to avoid building models that cannot generalize across lots. The work positions AI as a manufacturing control technology, not just a research tool—particularly relevant for sponsors trying to scale advanced therapies reliably.