When Automation Undermines Expertise: The Paradox of Human Capital Development in Intelligent Workplaces
DOI:
https://doi.org/10.66203/directiva.01302Keywords:
Expertise Paradox Theory, AI-assisted performance, independent professional expertise, human capital development, cognitive delegation, organizational learningAbstract
Intelligent technologies increasingly improve the speed, consistency, and quality of professional work while simultaneously redistributing cognitive activities through which expertise is developed and maintained. This article develops Expertise Paradox Theory (EPT) to address the unresolved question of when AI-assisted performance improvement becomes decoupled from independently retained professional expertise. Drawing on and integrating human capital theory, experiential learning, automation–augmentation research, skill erosion, organizational learning, and work design, the article constructs a conditional developmental framework linking performance-oriented AI delegation to diagnostic practice displacement, feedback detachment, cognitive scaffolding, learning-oriented work design, and verification complexity. EPT advances existing literature by distinguishing assisted performance from independent expertise, conceptualizing performance–expertise decoupling as a longitudinal phenomenon, and specifying both erosion and augmentation pathways. The theory further identifies human capital overestimation and vulnerability during independent intervention as organizational consequences of sustained decoupling. Eight propositions and explicit boundary conditions establish an empirically testable research agenda for examining how intelligent work arrangements can improve productivity while preserving capabilities required for independent professional judgment.
References
Argote, L., & Miron-Spektor, E. (2011). Organizational Learning: From Experience to Knowledge. Organization Science, 22(5), 1123–1137. https://doi.org/10.1287/orsc.1100.0621
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
Baird, A., & Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly, 45(1), 315–341. https://doi.org/10.25300/MISQ/2021/15882
Beane, M. (2019). Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail. Administrative Science Quarterly, 64(1), 87–123. https://doi.org/10.1177/0001839217751692
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Coff, R. W. (1997). Human Assets and Management Dilemmas: Coping With Hazards On the Road to Resource-Based Theory. Academy of Management Review, 22(2), 374–402. https://doi.org/10.5465/amr.1997.9707154063
Darr, E. D., Argote, L., & Epple, D. (1995). The Acquisition, Transfer, and Depreciation of Knowledge in Service Organizations: Productivity in Franchises. Management Science, 41(11), 1750–1762. https://doi.org/10.1287/mnsc.41.11.1750
Dell’Acqua, F., McFowland, E., Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838
Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406. https://doi.org/10.1037/0033-295X.100.3.363
Gerlach, J. P., & Lange, D. (2026). Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation. Academy of Management Review. https://doi.org/10.5465/amr.2024.0408
Giglio, B., Albeloushi, A., Alhaj, A. Kh., Alhantoobi, M., Saeedi, R., Davidovic, V., Uthamacumaran, A., Yilmaz, R., Lapointe, J., Balasubramaniam, N., Tee, T., Fazlollahi, A. M., Correa, J. A., & Del Maestro, R. F. (2025). Artificial Intelligence–Augmented Human Instruction and Surgical Simulation Performance. JAMA Surgery, 160(9), 993. https://doi.org/10.1001/jamasurg.2025.2564
Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature. Journal of Applied Psychology, 92(5), 1332–1356. https://doi.org/10.1037/0021-9010.92.5.1332
Lee, J. D., & See, K. A. (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Leonardi, P. M., & Leavell, V. (2026). Knowing Enough to Be Dangerous: The Problem of “Artificial Certainty” for Expert Authority When Using AI for Decision Making and Planning. Organization Science, 37(2), 516–543. https://doi.org/10.1287/orsc.2023.18224
Lyell, D., & Coiera, E. (2017). Automation bias and verification complexity: a systematic review. Journal of the American Medical Informatics Association, 24(2), 423–431. https://doi.org/10.1093/jamia/ocw105
Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., Smith, P. J., Wilson-Delfosse, A. L., & Ray, S. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness? Cognitive Research: Principles and Implications, 9(1), 46. https://doi.org/10.1186/s41235-024-00572-8
Macnamara, B. N., & Maitra, M. (2019). The role of deliberate practice in expert performance: revisiting Ericsson, Krampe & Tesch-Römer (1993). Royal Society Open Science, 6(8), 190327. https://doi.org/10.1098/rsos.190327
Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91(6), 1321–1339. https://doi.org/10.1037/0021-9010.91.6.1321
Parasuraman, R., & Manzey, D. H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human Factors: The Journal of the Human Factors and Ergonomics Society, 52(3), 381–410. https://doi.org/10.1177/0018720810376055
Piezunka, H., Aggarwal, V. A., & Posen, H. E. (2022). The Aggregation–Learning Trade-off. Organization Science, 33(3), 1094–1115. https://doi.org/10.1287/orsc.2021.1477
Raisch, S., & Fomina, K. (2025). Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations. Academy of Management Review, 50(2), 441–464. https://doi.org/10.5465/amr.2021.0421
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
Rinta-Kahila, T., Penttinen, E., Salovaara, A., Soliman, W., & Ruissalo, J. (2023). The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation. Journal of the Association for Information Systems, 24(5), 1378–1412. https://doi.org/10.17705/1jais.00829
Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Tyre, M. J., & von Hippel, E. (1997). The Situated Nature of Adaptive Learning in Organizations. Organization Science, 8(1), 71–83. https://doi.org/10.1287/orsc.8.1.71
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2026 Lina Marlina (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal applies the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
