Healthcare AI adoption: implications for learning and organization development

Purpose This paper seeks to determine how insights gained from current research on artificial intelligence (AI) in healthcare could be used to inform Learning and Development (L&D) and Organization Development (OD) practice around preparing workforces for AI implementation. Design/methodology/approach The paper draws on five recent peer-reviewed academic papers identified through a Scopus search on AI and healthcare (2025–2026), professional attitudes to AI in clinical settings, ethical AI governance, large language model adoption and platform-level AI resources, and interprets their findings from an OD/L&D perspective. Findings AI adoption is shaped by trust, perceived value and governance; hence, technical competence alone may be insufficient without trust and governance. Attitudes toward AI may vary according to exposure and motivation rather than position alone. Governance can be approached as an organizational competency, while peer communication may complement technical training in supporting trust and adoption. Research limitations/implications This is a secondary analysis of existing healthcare data of patients, clinicians and consumers, and not just one group, and therefore requires additional empirical research beyond healthcare organizations and occupations. Practical implications There are four evidence-informed practice levers to consider in sequence: diagnose, design, combine and evaluate, which means diagnose the attitude along with role; incorporate governance competency learning in the curriculum; combine technical training and peer trust; and evaluate confidence and appropriate use along with competence. Originality/value The paper offers a concise interpretation of recent healthcare AI evidence for workplace learning and OD practice.