Mixed-methods approach identifies AI opportunities in life sciences, highlighting regulatory challenges and data strategies.
Background: Artificial Intelligence (AI) adoption is accelerating in the life sciences sector, offering opportunities to enhance biopharmaceutical research, development, and commercialization. However, the sector lacks structured tools to prioritize AI initiatives across varied business domains. Objective: This study presents a framework to categorize and evaluate potential AI applications in the life sciences industry, organizing use cases along two critical dimensions: Phase of Product Lifecycle and Operational Domain. Methods: A structured mixed-methods approach was employed, including a modified Delphi consensus process with industry experts, qualitative case study review, and iterative framework refinement between August 2023 and August 2024. Results: The resulting matrix framework enables life sciences professionals to assess AI opportunities across research, clinical development, commercialization, and post-marketing activities. Key findings highlight the pervasive nature of AI impact, the emphasis on data-driven strategies, and the regulatory and ethical challenges facing biopharma firms. Conclusions: This framework provides a practical model for strategic AI adoption decisions within the life sciences sector and lays the groundwork for future research, policy development, and enterprise transformation efforts.
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Hinkel et al. (2025) studied this question.
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