Learning Beyond the Individual: Building Collective Capability in Human AI team

Artificial intelligence is entering the workflow faster than most organizations are redesigning learning. This paper argues that the central L&D challenge is no longer individual AI literacy alone, but the collective capability of teams to reason, coordinate, challenge, remember, and improve with AI. An integrative review of peer-reviewed research and recent workforce studies is used to connect human-AI teaming, transactive memory, shared models, workplace learning, and capability development. The evidence is striking: 84% of executives expect regular human-AI collaboration within three years, yet only 26% of workers report being trained to collaborate effectively with AI; high-performing teams report higher AI use than other teams (78% versus 54%), but their advantage is also associated with trust, apprenticeship, agility, and human connection. The paper proposes the CYCLE framework: Clarify roles, Yield to evidence, Capture memory, Learn through correction, and Embed routines. It translates the framework into a practical operating model for L&D, including team simulations, decision-trace practices, peer challenge, AI debriefs, and measures of transfer at team level. The argument is deliberately human-centered: AI may accelerate access to knowledge, but collective capability develops only when people can question outputs, speak up, share judgment, and retain learning. The paper concludes with propositions and a field-research agenda for testing durable team capability over time.

Human-AI Teaming; Collective Capability; Workplace Learning; Transactive Memory

1. Accenture. (2025). Learning, reinvented: Accelerating human-AI collaboration. https://www.accenture.com/us-en/insights/consulting/learning-reinvented-accelerating-human-ai-collaboration [Google Scholar] [Crossref]

2. Bienefeld, N., Kolbe, M., Camen, G., Huser, D., & Buehler, P. K. (2023). Human-AI teaming: Leveraging transactive memory and speaking up for enhanced team effectiveness. Frontiers in Psychology, 14, 1208019. https://doi.org/10.3389/fpsyg.2023.1208019 [Google Scholar] [Crossref]

3. Callari, T. C., & Puppione, L. (2025). Can generative artificial intelligence productivity tools support workplace learning? A qualitative study on employee perceptions in a multinational corporation. Journal of Workplace Learning, 37(3), 266-283. https://doi.org/10.1108/JWL-11-2024-0258 [Google Scholar] [Crossref]

4. Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Working Paper No. 24-013. https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf [Google Scholar] [Crossref]

5. Deloitte. (2026, January 14). Human capabilities are at the heart of high-performing teams. https://www.deloitte.com/us/en/about/press-room/high-performing-teams.html [Google Scholar] [Crossref]

6. Gonzalez, C., Donahue, K., Goldstein, D. G., Heidari, H., Jalali, M. S., Schelble, B., Singh, A., & Woolley, A. W. (2026). Toward a science of human-AI teaming for decision making: A complementarity framework. PNAS Nexus, 5(3), pgag030. https://doi.org/10.1093/pnasnexus/pgag030 [Google Scholar] [Crossref]

7. Jaffe, S., Shah, N. P., Butler, J., Farach, A., Cambon, A., Hecht, B., Schwarz, M., & Teevan, J. (Eds.). (2024). Generative AI in real-world workplaces: The second Microsoft report on AI and productivity research. Microsoft. https://www.microsoft.com/en-us/research/wp-content/uploads/2024/07/Generative-AI-in-Real-World-Workplaces.pdf [Google Scholar] [Crossref]

8. LinkedIn Learning. (2025). Workplace Learning Report 2025: The rise of career champions. https://learning.linkedin.com/resources/workplace-learning-report [Google Scholar] [Crossref]

9. Meske, C., Hermanns, T., & Brachten, F. (2025). The GenAI who knew too little: Revisiting transactive memory systems in human-GenAI collaboration. Proceedings of the 20th International Conference on Wirtschaftsinformatik. [Google Scholar] [Crossref]

10. Yan, B., Hollingshead, A. B., Alexander, K. S., Cruz, I., & Shaikh, S. J. (2021). Communication in transactive memory systems: A review and multidimensional network perspective. Small Group Research, 52(1), 3-32. https://doi.org/10.1177/1046496420967764 [Google Scholar] [Crossref]

• Employee Training Efficacy in Banking Sector
• Multidimensional Predictors of University Students’ Examination Performance: A Quantitative Analysis of Psychological, Environmental, And Skill-Based Factors
• Digital Learning Innovation: Evaluating the Effectiveness of MOOC and Politicbox in Enhancing Students Understanding
• An Investigation of Group Work through Mcclelland’s Theory
• The Impact of a Practical Flipped Process-Genre Writing Model on Moroccan EFL High School Students’ Writing Performance