Skip to main content


The AI Pyramid: A Conceptual Model Outlining Workforce Capability

Issue Abstract

Abstract

Artificial intelligence (AI) marks a major shift in technological change by extending cognitive labor rather than just automating routine tasks. Recent evidence shows that generative AI is impacting highly educated, white-collar jobs more than expected. This challenges traditional assumptions about which parts of the workforce are most vulnerable. As a result, conventional approaches to digital and AI literacy are no longer sufficient. This paper introduces “AI Nativity,” the ability to seamlessly integrate AI into everyday thinking and decision-making. It also proposes the AI Pyramid as a framework for understanding workforce capabilities in an AI-driven economy. The pyramid includes three interconnected layers: AI Native, AI Foundation, and AI Deep capabilities. AI Native serves as a baseline for participation, while AI Foundation focuses on building and maintaining systems. AI Deep capability advances cutting-edge AI knowledge and innovation. The framework emphasizes capability development as ongoing infrastructure, shaping policy, education, and workforce strategies.


Author Information
Dr. Nijina Jose Assistant Professor, Department of Commerce: N. Subhashree III B. Com (Accounting & Finance): A. Hemalatha III B. Com (Accounting & Finance). Valliammal College for Women.
Issue No
3
Volume No
6
Issue Publish Date
05 Mar 2026
Issue Pages
24-31

Issue References

References

  1. Brynjolfsson, E.,Li, D., & Raymond, L. (2025). Generative AIat work. The Quarterly Journal of Economics, 140(2), 889–942. 

    https://doi.org/10.1093/qje/qjae044

  2. Cazzaniga, M.,Jaumotte, F., Li,L., Melina, G.,Panton, A. J.,Pizzinelli, C., Rockall, E. J., & Tavares, M. M. (2024). Gen-AI: Artificial intelligence and the future of work. IMF Staff Discussion Notes, 2024(001). https://doi.org/10.5089/9798400262548.006

  3. Deming, D.J., & Noray, K. (2020). Earnings dynamics, changing jobskills, and STEMcareers.

  4. The Quarterly Journal of Economics135(4), 1965–2005. 

    https://doi.org/10.1093/qje/qjaa021

  5. Felten, E. W., Raj, M., & Seamans, R. (2023). Occupational heterogeneity in exposure to generative AI (SSRN Working Paper No.4414065). SSRN. https://doi.org/10.2139/ssrn.4414065

  6. Hosseinioun, M.,Neffke, F., Zhang, L., & Youn, H. (2025). Skill dependencies uncover nested human capital. Nature Human Behaviour, 9(4), 673–687. https://doi.org/10.1038/s41562-024-02093-2

  7. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings ofthe 2020 CHIConference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727

  8. Strobel, J.,& van Barneveld, A. (2009). Whenis PBL moreeffective? A meta-synthesis of meta-analyses comparing PBL to conventional classrooms. Interdisciplinary Journal of Problem-Based Learning, 3(1), 44–58. https://doi.org/10.7771/1541-5015.1046

  9. Clark, A.(2025). Extending minds with generative AI.Nature Communications, 16,Article 4627. https://doi.org/10.1038/s41467-025-59906-9