Organizational factors influencing the adoption of AI technologies in preventive healthcare

Purpose Artificial intelligence (AI) has the potential to revolutionize preventive healthcare by enabling early risk detection, patient stratification and support for clinical decision making. However, its implementation in real-world settings remains inconsistent, primarily due to ongoing organizational challenges. Guided by the population, intervention, comparison and outcome (PICO) and preferred reporting items for systematic reviews and meta-analyses (PRISMA) frameworks, this study aims to identify the organizational factors that influence the successful adoption of AI technologies in preventive care. Design/methodology/approach This study adopts the PRISMA flow and reviews 536 peer-reviewed articles that met the PICO-defined criteria: healthcare organizations (population), adoption or use of AI technologies (intervention), no comparator required (comparison) and organizational adoption factors (outcomes). Using an AI-assisted screening process combined with thematic analysis, we categorized organizational influences into 12 key topics. These were analyzed through the lens of the organizational readiness for change (ORC) framework. Findings The findings highlight that adoption success hinges on strategic leadership, workforce preparedness, effective data governance, seamless workflow integration and robust ethical oversight. Persistent challenges include fragmented infrastructures, limited trust, and regulatory uncertainty. Originality/value This study advances the field in three respects: it focuses specifically on preventive healthcare as an organizational context; it applies the ORC framework as an integrating theoretical lens across a large, synthesized corpus, and it employs a transparent human–AI-assisted screening process that supports large-scale evidence synthesis while preserving author oversight.