This research explores the use of Artificial Intelligence (AI) in visual inspection within the biopharmaceutical industry, focusing on the impact of false eject rates, applications of AI, AI as a solution for false ejects, and the challenges of AI adoption. The objectives of the research were to demonstrate the impact of current false eject rates on inspection efficiency, identify AI applications that can reduce these rates, and use industry expertise to evaluate the best solutions. The study has ultimately assessed whether AI implementation can improve the accuracy of visual inspections beyond the current state, offering practical, industry-relevant insights into enhancing inspection processes and reducing waste. Through thematic analysis of interviews with industry professionals, the study identifies key insights and gaps in the existing literature, offering a comprehensive view of current practices and challenges. The findings reveal that false eject rates significantly affect resources, schedules, and costs, with participants highlighting the need for improved processes to minimize these impacts. While existing literature addresses some aspects of false eject rates, this research uncovers additional implications, such as delays in product release and the perceived stability of eject rates, that are not widely discussed in prior studies. The study also explores the potential of AI in enhancing visual inspection processes, particularly through deep learning for recipe development. While participants see promise in AI, they emphasize the continued need for human expertise and raise concerns about the scalability of AI solutions in high-speed production environments. Challenges such as building large defect libraries, regulatory compliance, and the cost of AI adoption are also examined, with the research identifying areas where further guidance and innovation are needed. This research contributes to the body of knowledge by providing industry-specific insights, identifying gaps in the literature, and offering practical recommendations for integrating AI into visual inspection processes. It highlights the importance of collaboration between AI developers and industry experts and calls for future research to explore the long-term impact of AI on visual inspection and the development of supportive regulatory frameworks.