While AI adoption offers significant potential, the industry is still transitioning from traditional approaches to digital manufacturing. This shift presents challenges related to regulatory compliance, organizational change, workforce skill gaps, and data infrastructure limitations. 

In this webinar, we explore how AI-driven analytics enhance product and process knowledge across the full lifecycle, from process development using Quality by Design (QbD) to process validation and continued process verification (CPV). This end-to-end approach supports process robustness, continuous improvement, and consistent product quality. 

During the process performance qualification (PPQ) phase, we demonstrate how large language models (LLMs) can be used to generate accurate validation documentation, reducing both time and operational effort. 

Additionally, we examine a real-world digital twin implementation, highlighting how AI enables process control through real-time data acquisition, advanced analytics, predictive modeling, and process troubleshooting.