Artificial Intelligence in Pharmaceutical Manufacturing: Applications and Implementation Challenges - Bhangale, Pankaj (Digital Repository of Theses - SSBM Geneva, 2025)
This study explored the application of “artificial intelligence” (AI) in pharmaceutical manufacturing, focusing on the challenges that may be encountered during its implementation and the potential impact on key manufacturing outcomes. Several factors, including regulatory issues, data management and integration, costs, and technological constraints, are likely to pose a challenge to the use of AI technologies in the future growth of the pharma industry as the industry relies more on AI for quality enhancement of drugs, efficiency, cost reduction, and compliance with regulatory requirements. To find out these challenges, the current research has used a survey research design and structured questionnaire survey with replies from 300 industry specialists and practitioners drawn from pharmaceutical manufacturing industries that incorporate the use of AI techniques. The data which was collected was analysed with the help of SPSS software and descriptive statistics, correlation, and regression analysis were applied to approach the expected state of AI application and its impact on the manufacturing process. The study identified both the transformative benefits and critical challenges associated with AI implementation. Key findings suggest that AI is positively impacting the sector by streamlining operations improving accuracy in drug production, and enabling real-time data analysis. However, challenges such as data quality and integration issues, regulatory restrictions, cybersecurity risks, and the need for specialized skills were noted as significant barriers to successful AI adoption. The study, therefore, finds that it will take AI to the next level of improving pharmaceutical manufacturing; AI challenges can only be overcome with multistakeholder cooperation, adoption of common data management practices, advanced cybersecurity measures and ongoing training of human capital. It also covers the need to continually adjust the regulations that govern development and use of AI technologies to the dynamic nature of the technologies to safeguard patient interest and product quality. The implications of these findings are critical for industry leaders, policymakers, and researchers. By addressing identified obstacles, pharmaceutical manufacturers can leverage AI more effectively, leading to cost savings, reduced time-to-market for new drugs, and improved drug quality.