This study examines workforce readiness for ethical AI adoption in Oman’s healthcare system and identifies human resource development strategies needed to support implementation. A qualitative interpretivist design was used. Semi-structured interviews were conducted with 20 stakeholders from government, academia, industry, and civil society, selected through purposive and snowball sampling. The data were analysed thematically and interpreted through the Ability–Motivation–Opportunity (AMO) framework. The findings reveal four interconnected workforce challenges. First, ability deficits included limited AI literacy, insufficient data and digital skills, weak ethical literacy, and inadequate exposure to AI-supported clinical decision-making. Second, motivational barriers included fear of job displacement, low trust, concerns about professional autonomy, and uncertainty regarding accountability for AI-assisted decisions. Third, opportunity constraints included limited structured training, weak interdisciplinary learning, insufficient employee involvement in AI implementation, and unequal access to digital resources between urban and rural facilities. Fourth, dependence on foreign consultants restricted knowledge retention and the development of sustainable local capability. The study proposes an AMO-aligned human resource development agenda comprising competency-based training, scenario-based ethics education, participatory implementation, multidisciplinary AI teams, leadership development, and enforceable knowledge-transfer arrangements.
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20, Article 310. https://doi.org/10.1186/s12911-020-01332-6
Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. L. (2000). Manufacturing advantage: Why high-performance work systems pay off. Cornell University Press.
Boxall, P., & Purcell, J. (2016). Strategy and human resource management (4th ed.). Palgrave Macmillan.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Elgin, C. Y., & Elgin, C. (2024). Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: A qualitative study of healthcare professionals' perspectives. BMC Medical Ethics, 25, Article 148. https://doi.org/10.1186/s12910-024-01151-8
Jiang, K., Lepak, D. P., Hu, J., & Baer, J. C. (2012). How does human resource management influence organizational outcomes? A meta-analytic investigation of mediating mechanisms. Academy of Management Journal, 55(6), 1264–1294. https://doi.org/10.5465/amj.2011.0088
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, Article 195. https://doi.org/10.1186/s12916-019-1426-2
Kim, J. Y., Hasan, A., Kueper, J., Tang, T., Hayes, C., Fine, B., Balu, S., & Sendak, M. (2025). Establishing organizational AI governance in healthcare: A case study in Canada. npj Digital Medicine, 8, Article 522. https://doi.org/10.1038/s41746-025-01909-3
Marwaha, J. S., Yuan, W., Poddar, M., Elsamadisi, P., & Brat, G. A. (2025). The algorithmic consultant: A new era of clinical AI calls for a new workforce of physician-algorithm specialists. npj Digital Medicine, 8, Article 552. https://doi.org/10.1038/s41746-025-01960-0
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. Journal of Innovation & Knowledge, 9(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
Razai, M. S., Kooner, P., Majeed, A., & Esmail, A. (2024). Implementation challenges of artificial intelligence in primary care: A qualitative study of general practitioners' perspectives. PLOS ONE, 19(11), e0314196. https://doi.org/10.1371/journal.pone.0314196
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. https://www.who.int/publications/i/item/9789240029200
Rumaidhi, S. A. M. Al, & Saimy, I. S. (2026). Preparing the Healthcare Workforce for Ethical Artificial Intelligence Adoption in Oman: An Ability–Motivation–Opportunity Perspective. International Journal of Academic Research in Business and Social Sciences, 16(7), 1275–1288.
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