The New Digital Divide: The New Digital Divide: A Perspective of AI Literacy as Workplace Capital and Organizational Inequality

Keywords: Artificial Intelligence Literacy, Workplace Capital, Digital Divide

Abstract

In the business world, workplace relationships, employee work performance, and business decision-making are all currently being transformed by the broad diffusion of generative Artificial Intelligence (AI). This explosion focuses on productivity effects; however, it is unaccompanied by studies on whether divergent degrees of AI expertise can produce novel labour inequities. This perspective relied on prior work in the fields of AI literacy, the digital divide, human capital, organizational justice, and human-AI teams. This article conceptualizes the AI Literacy Divide. It argues that literacy in AI is becoming a type of workplace capital that mediates whether an individual can translate access to AI to enhance productivity, work quality and career progression. High-AI literacy workers could be relatively privileged recipients of a variety of rewards associated with AI-enabled work, while low-AI literacy workers could be relatively excluded or disadvantaged relative to their work counterparts possessing equivalent levels of non-AI-based work knowledge. We presented a framework on the origins and effects of the AI Literacy Divide and discussed the practical implications for HR management, human capital management, organisational equity, and future research.

Author Biography

Prof Sam Bodunrin, Algoma University
Dr Samuel Bodunrin, PhD, CHRL, SHRM-SCP, GPHR, CPHR, PMP, PMI-ACP, CBAP, is a Professor of Management and Human Resources, researcher and HR professional with a diverse career that includes significant academic, industry and professional governance experience. His research expertise is in artificial intelligence and work, digital transformation, strategic HR, OB, workforce development and the future of work. Dr. Bodunrin is an author of many academic research papers and professional books in human resource management and a leader in many professional and academic bodies in Canada and globally.

References

Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659. https://doi.org/10.1111/1748-8583.12524

Call, M. L., Jiang, K., & Idso, C. (2026). Star advantage: Employee value creation and capture in the age of artificial intelligence. Human Resource Management, 65(1), 151–167. https://doi.org/10.1002/hrm.70023

Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2024). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management, 71, pp. 810–823. https://doi.org/10.1109/TEM.2021.3138503

Chiu, T. K. F. (2025). AI literacy and competency: definitions, frameworks, development and future research directions. Interactive Learning Environments, 33(5), 3225–3229. https://doi.org/10.1080/10494820.2025.2514372

Gerhart, B., & Feng, J. (2021). The resource-based view of the firm, human resources, and human capital: Progress and prospects. Journal of Management, 47(7), 1796–1819. https://doi.org/10.1177/0149206320978799

Liu, X., Zhang, L., & Wei, X. (2025). Generative artificial intelligence literacy: Scale development and its effect on job performance. Behavioral Sciences, 15(6), 811. https://doi.org/10.3390/bs15060811

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). ACM. https://doi.org/10.1145/3313831.3376727

Lythreatis, S., Singh, S. K., & El-Kassar, A. N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, Article 121359. https://doi.org/10.1016/j.techfore.2021.121359

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072

Robinson, L., Schulz, J., Blank, G., Ragnedda, M., Ono, H., Hogan, B., Mesch, G., Cotten, S., Kretchmer, S., Hale, T., Drabowicz, T., Yan, P., Wellman, B., Harper, M., Quan-Haase, A., & Dunn, H. (2020). Digital inequalities 2.0: Legacy inequalities in the information age. First Monday, 25(7). https://doi.org/10.5210/fm.v25i7.10842

van Deursen, A. J., & van Dijk, J. A. (2019). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media & Society, 21(2), 354-375. https://doi.org/10.1177/1461444818797082

Published
2026-08-06
How to Cite
Bodunrin, S., & Alayinde, J. (2026). The New Digital Divide: The New Digital Divide: A Perspective of AI Literacy as Workplace Capital and Organizational Inequality. International Journal Administration, Business & Organization, 7(2), 366-372. https://doi.org/10.61242/ijabo.26.791
Section
Research Note