AI Adoption as an Organizational Capability

This review examines artificial-intelligence adoption as an organizational capability. The organizing question is which complementary skills, processes, data, leadership, and learning routines convert tools into verifiable value. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to attributing outcomes to the model while ignoring organizational redesign and selection. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in enterprise strategy and technology management.

1. Badghish, S., & Soomro, Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864 DOI
2. Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2023). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159-182. https://doi.org/10.1002/job.2735 DOI
3. Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312 DOI
4. Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2022). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899. https://doi.org/10.1016/j.hrmr.2022.100899 DOI
5. Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, 102497. https://doi.org/10.1016/j.ijinfomgt.2022.102497 DOI
6. Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120-6145. https://doi.org/10.1080/00207543.2024.2309309 DOI
7. Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262-273. https://doi.org/10.1016/j.jbusres.2020.07.045 DOI
8. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434 DOI
9. Neumann, O., Guirguis, K., & Steiner, R. (2022). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114-141. https://doi.org/10.1080/14719037.2022.2048685 DOI
10. Zhong, K., & Song, L. (2024). Artificial intelligence adoption and corporate green innovation capability. Finance research letters, 72, 106480. https://doi.org/10.1016/j.frl.2024.106480 DOI