Professional competencies of business administrators in the age of artificial intelligence: a systematic review of academic training gaps and curricular relevance
DOI:
https://doi.org/10.62452/k60ng792Keywords:
Artificial intelligence, digital competencies, business administration, academic training, curriculum reform, higher educationAbstract
This study analyzes the gap between the competencies developed by business administration graduates and the demands of the labor market in the era of artificial intelligence. Through a systematic review of scientific literature published between 2019 and 2026, it critically examines the required competencies, deficiencies in academic training, and proposals for curricular redesign. The methodology followed PRISMA 2020 guidelines, with a Scopus search and the application of PICO criteria, resulting in the selection of 23 studies in Spanish, English, and Portuguese, whose quality was assessed using the JBI checklist adapted for social sciences. The findings reveal three main shortcomings: the lack of training in data analytics and machine learning, the predominance of traditional pedagogical approaches misaligned with dynamic digital environments, and the absence of institutional frameworks to assess digital competencies. Additionally, there is limited representation of studies from Latin America. It is concluded that there is a structural misalignment between academic training and current technological demands, requiring an urgent curricular redesign aligned with international standards and adapted to the Latin American context.
Downloads
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Alam, G. M., Rahman, M. M., Skrbinjek, V., Abdul Aziz, N. A., Bashir, K., & Kader, R. (2025). Does the digitalised era adjust the mismatch as match? A comparison of graduates from different streams to align with the digitalized banking sector. International Journal of Innovation and Learning, 37(4). https://doi.org/10.1504/IJIL.2025.147981
Aldredge, M., Rogers, C., & Smith, J. (2021). The strategic transformation of accounting into a learned profession. Industry and Higher Education, 35(4), 385–393. https://doi.org/10.1177/0950422220954319
Aljohani, N. R., Aslam, A., Khadidos, A. O., & Hassan, S.-U. (2022). Bridging the skill gap between the acquired university curriculum and the requirements of the job market: A data-driven analysis using big data. Journal of Innovation and Knowledge, 7(3), 100190. https://doi.org/10.1016/j.jik.2022.100190
Andonovikj, V., Mileva Boshkoska, B., Redek, T., & Boškoski, P. (2024). A data-driven approach to aligning academic offerings with industry needs for business and economy in Slovenia. Journal of Decision Systems, 33(3). https://doi.org/10.1080/12460125.2024.2354587
Basco, A. I., & Lavena, C. (2021). América Latina en movimiento. Competencias y habilidades para la cuarta revolución industrial en el contexto de pandemia. BID. https://publications.iadb.org/publications/spanish/document/America-Latina-en-movimiento-Competencias-y-habilidades-para-la-Cuarta-Revolucion-Industrial-en-el-contexto-de-pandemia.pdf
Benhayoun, L., & Lang, D. (2021). Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps’ identification through text mining. Human Systems Management, 40(5), 609–622. https://doi.org/10.3233/HSM-211179
Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier—Modeling the Impact of AI on the World Economy. Discussion Paper, McKinsey Global Institute.
Clayton, P. R., & Clopton, J. (2019). Business curriculum redesign: Integrating data analytics. Journal of Education for Business, 94(1), 1–7. https://doi.org/10.1080/08832323.2018.1502142
Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review Press.
García-Vera, Y. S., & Juca-Maldonado, F. X. (2026). Inteligencia artificial aplicada a la contabilidad: Flujos, buenas prácticas y control. Sophia Editions.
Gong, Y., Wang, S., & Dong, Y. (2025). Integrating artificial intelligence in entrepreneurship education: Dynamic capabilities and marketing performance. International Journal of Management Education, 23(2), 101248. https://doi.org/10.1016/j.ijme.2025.101248
Goulart, V. G., Liboni, L. B., & Cezarino, L. O. (2022). Balancing skills in the digital transformation era: The future of jobs and the role of higher education. Industry and Higher Education, 36(2), 118–127. https://doi.org/10.1177/09504222211029796
Gupta, S., & Jaiswal, R. (2024). How can we improve AI competencies for tomorrow’s leaders: Insights from multi-stakeholders’ interaction. International Journal of Management Education, 22(3), 101070. https://doi.org/10.1016/j.ijme.2024.101070
Gupta, S., & Jaiswal, R. (2025). A deep learning-based hybrid PLS-SEM-ANN approach for predicting factors improving AI-driven decision-making proficiency. Journal of International Education in Business. https://doi.org/10.1108/JIEB-05-2024-0058
Jaiswal, K., Kuzminykh, I., & Modgil, S. (2025). Understanding the skills gap between higher education and industry in the UK in artificial intelligence sector. Industry and Higher Education, 39(1). https://doi.org/10.1177/09504222241280441
Joanna Briggs Institute. (2020). JBI manual for evidence synthesis. JBI. https://synthesismanual.jbi.global
Kelly, J., Gielstra, D., Oberding, T. J., Bruno, J., & Cosentino, S. (2026). Bridging the AI skills gap: A blueprint for future-proofing the workforce by including industry advisory councils. Industry and Higher Education. https://doi.org/10.1177/09504222261417843
Mei, L., Feng, X., & Cavallaro, F. (2023). Evaluate and identify the competencies of the future workforce for digital technologies implementation in higher education. Journal of Innovation and Knowledge, 8(3), 100445. https://doi.org/10.1016/j.jik.2023.100445
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
Nyale, D., Karume, S., Kipkebut, A., & Mukudi, F. (2026). Digital skills landscape: A systematic review of current academic programs, industry demands, and the digital divide’s impact on graduate competencies. Industry and Higher Education, 40(2), 229–241. https://doi.org/10.1177/09504222251370105
Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 71. https://doi.org/10.1136/bmj.n71
Pham, M. T., Rajić, A., Greig, J. D., Sargeant, J. M., Papadopoulos, A., & McEwen, S. A. (2014). A scoping review of scoping reviews: Advancing the approach and enhancing the consistency. Research Synthesis Methods, 5(4), 371–385. https://doi.org/10.1002/jrsm.1123
Ratten, V., & Jones, P. (2023). Generative artificial intelligence (ChatGPT): Implications for management educators. International Journal of Management Education, 21(3), 100857. https://doi.org/10.1016/j.ijme.2023.100857
Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Richter, S., Giroux, M., Piven, I., Sima, H., & Dodd, P. (2025). A constructivist approach to integrating AI in marketing education: Bridging theory and practice. Journal of Marketing Education, 47(1). https://doi.org/10.1177/02734753241288876
Ritter, B. A., Gopalkrishnan, S. S., & Sockbeson, C. E. S. (2024). The next generation of pedagogy: Reflections from EdTech leaders. Journal of Management Education, 48(4). https://doi.org/10.1177/10525629231206182
Schwab, K. (2016). The fourth industrial revolution. Crown Business.
Secundo, G., Rippa, P., & Meoli, M. (2020). Digital transformation in entrepreneurship education centres: Preliminary evidence from the Italian Contamination Labs network. International Journal of Entrepreneurial Behaviour and Research, 27(5). https://doi.org/10.1108/IJEBR-11-2019-0618
Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual Review of Psychology, 70, 747–770. https://doi.org/10.1146/annurev-psych-010418-102803
Spada, I., Chiarello, F., Barandoni, S., Ruggi, G., Martini, A., & Fantoni, G. (2022). Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing education. Technological Forecasting and Social Change, 183, 121869. https://doi.org/10.1016/j.techfore.2022.121869
Traoret, R. I., Hujran, O., Al-Naymat, G., & Aldahmani, S. (2026). Bridging the skills gap: A machine learning approach to align business education with labor market needs in the UAE. Industry and Higher Education. https://doi.org/10.1177/09504222261436625
Vega Chica, M. L., & Valle Lituma, L. A. (2026). Are we learning what the market needs? A comparative study on online continuing education and workforce demands in Latin America. Industry and Higher Education. https://doi.org/10.1177/09504222261433773
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens (EUR 31006 EN). Publications Office of the European Union. https://doi.org/10.2760/115376
Wang, S., Sun, Z., Wang, H., Yang, D., & Zhang, H. (2025). Enhancing student acceptance of artificial intelligence-driven hybrid learning in business education. International Journal of Management Education, 23(1), 101184. https://doi.org/10.1016/j.ijme.2025.101184
World Economic Forum. (2023). Future of jobs report 2023. https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf
Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. International Journal of Management Education, 19(3), 100550. https://doi.org/10.1016/j.ijme.2021.100550
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Henry Espinoza-Briones, Emilio Yong-Chang, Marco Villarroel-Puma, Mariana Reyes-Bermeo (Autor/a)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish in Revista Metropolitana de Ciencias Aplicadas (REMCA), agree to the following terms:
1. Copyright
Authors retain unrestricted copyright to their work. Authors grant the journal the right of first publication. To this end, they assign the journal non-exclusive exploitation rights (reproduction, distribution, public communication, and transformation). Authors may enter into additional agreements for the non-exclusive distribution of the version of the work published in the journal, provided that acknowledgment of its initial publication in this journal is given.
© The authors.
2. License
The articles are published in the journal under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). The terms can be found at: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
This license allows:
- Sharing: Copying and redistributing the material in any medium or format.
- Adapting: Remixing, transforming, and building upon the material.
Under the following terms:
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if any changes were made. You may do this in any reasonable manner, but not in any way that suggests the licensor endorses or sponsors your use.
- NonCommercial: You may not use the material for commercial purposes.
- ShareAlike: If you remix, transform, or build upon the material, you must distribute your creation under the same license as the original work.
There are no additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
