The Augmentation Trap in Analytics Teams: Short-Run AI Productivity, Skill Erosion, and Decision Resilience
DOI:
https://doi.org/10.61424/rj1dhn51Keywords:
Artificial Intelligence, Human-AI Collaboration, Skill Erosion, Automation Bias, Decision Resilience.Abstract
AI can make analytics teams faster, but it can also be faster without human ability. A concept of augmentation trap is developed and tested in this study. The fallacy is that continuously using AI will enhance existing productions but will also decrease the ability to do analysis without AI and make people overly reliant on the AI system. The paper establishes the connections between human-AI complementarity, experiential learning, cognitive offloading, automation bias and organizational resilience. A twelve week-long quasi-experiment is suggested, which is a mixed-methods approach, and with 48 analytics teams and 192 analysts. Teams are divided into groups that have unrestricted AI access, periodic unaided practice and verification with calibrated AI access, delayed or withheld AI access. The design features repeated assessments without AI, an unannounced model-outage exercise, and a task that has a plausible but erroneous AI recommendation. Multilevel growth models test the changes in productivity and unaided skill. Cognitive offloading and team-level models of decision resilience are the focus of mediation tests. The synthetic analysis is only included as a reporting template. It shows an example of a trend where unrestricted access to AI increases throughput, but leads to poorer unaided skill, outage performance and error detection. When calibrated, it will yield smaller gains in speed and higher long run capability. The study adds a time-based perspective to the discussion of AI augmentation, and it reveals why companies should assess performance and capability with and without AI, as well as with incorrect AI.
References
Alla, P. B. (2025). Automation as Augmentation: Stories of Human-AI Collaboration in the Workplace. European Modern Studies Journal, 9(4), 1019–1036. https://doi.org/10.59573/emsj.9(4).2025.96
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
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021). Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3411764.3445717
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
Caosun, M., & Aral, S. (2026). The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading (Version 6). arXiv. https://doi.org/10.48550/ARXIV.2604.03501
Dell’Acqua, F., Ayoubi, C., Lifshitz, H., Sadun, R., Mollick, E., Mollick, L., Han, Y., Goldman, J., Nair, H., Taub, S., & Lakhani, K. R. (2026). The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork. Organization Science, orsc.2025.20702. https://doi.org/10.1287/orsc.2025.20702
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
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them. Management Science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643
Duchek, S. (2020). Organizational resilience: A capability-based conceptualization. Business Research, 13(1), 215–246. https://doi.org/10.1007/s40685-019-0085-7
Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., & Satzger, G. (2025). Complementarity in human-AI collaboration: Concept, sources, and evidence. European Journal of Information Systems, 34(6), 979–1002. https://doi.org/10.1080/0960085X.2025.2475962
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
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
Lewis, K. (2003). Measuring transactive memory systems in the field: Scale development and validation. Journal of Applied Psychology, 88(4), 587–604. https://doi.org/10.1037/0021-9010.88.4.587
Li, X., & Lin, H. (2024). How to leverage flexibility-oriented HRM systems to build organizational resilience in the digital era: The mediating role of intellectual capital. Journal of Intellectual Capital, 25(1), 1–22. https://doi.org/10.1108/JIC-03-2023-0038
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005
March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71
Mehrotra, S., Degachi, C., Vereschak, O., Jonker, C. M., & Tielman, M. L. (2024). A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction: Trends, Opportunities and Challenges. ACM Journal on Responsible Computing, 1(4), 1–45. https://doi.org/10.1145/3696449
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
Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
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., The University of Queensland Business School, Penttinen, E., Aalto University School of Business, Salovaara, A., Aalto University / University of Helsinki, Soliman, W., University of Agder, Norway / University of Jyväskylä, Ruissalo, J., & Aalto University School of Business. (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
Schmutz, J. B., Outland, N., Kerstan, S., Georganta, E., & Ulfert, A.-S. (2024). AI-teaming: Redefining collaboration in the digital era. Current Opinion in Psychology, 58, 101837. https://doi.org/10.1016/j.copsyc.2024.101837
Seeber, I., Bittner, E., Briggs, R. O., De Vreede, T., De Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174
Shao, Y., Huang, C., Song, Y., Wang, M., Song, Y. H., & Shao, R. (2025). Using Augmentation-Based AI Tool at Work: A Daily Investigation of Learning-Based Benefit and Challenge. Journal of Management, 51(8), 3352–3390. https://doi.org/10.1177/01492063241266503
Shen, J. H., & Tamkin, A. (2026). How AI Impacts Skill Formation (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2601.20245
Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991–1006. https://doi.org/10.1006/ijhc.1999.0252
Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8(12), 2293–2303. https://doi.org/10.1038/s41562-024-02024-1
Zercher, D., Jussupow, E., Benke, I., & Heinzl, A. (2025). How Can Teams Benefit From AI Team Members? Exploring the Effect of Generative AI on Decision‐Making Processes and Decision Quality in Team–AI Collaboration. Journal of Organizational Behavior, job.2898. https://doi.org/10.1002/job.2898
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Md. Nabidul Haque (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
