| Sumario: | This survey conducted by the authors examined factors of Roger's diffusion of innovation theory related to adopting machine learning (ML) and artificial intelligence (AI) in the bio/pharmaceutical manufacturing industry. The sample included industry leaders and bio/pharmaceutical professionals with experience in regulatory affairs, product development, and commercial operations at FDA-regulated bio/pharmaceutical companies. Participants responded to a survey administered with a direct link for confidentiality. Respondents were asked 44 questions on the adoption challenges of ML and AI practices in bio/pharma manufacturing. According to the results, creating ML and AI solutions catering to manufacturing segments can quickly generate a customer base. The survey also found that the first areas to be affected by AI are likely to be those related to process efficiency because decision making related to manufacturing efficiency tends to be less regulated compared to quality decision making. In addition, the greatest opportunity for return on investment (ROI) in ML and AI implementation in bio/pharma manufacturing maybe waste reduction, batch yields, and faster batch completion, which are well-aligned with existing lean manufacturing principles. In the current scenario, it is essential to provide training courses for bio/pharma professionals to collaborate in identifying opportunities and developing and implementing ML and AI projects. This will help professionals adapt to the digital revolution and stay ahead in the rapidly changing bio/pharma manufacturing industry.
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