Strategic Memory Debt in AI-Assisted Firms: Causal-Knowledge Erosion and Strategic Renewal
Keywords:
Artificial Intelligence, Organizational Memory, Organizational Forgetting, Causal Knowledge, Human-AI Collaboration, Dynamic Capabilities, Strategic Renewal, Managerial CognitionAbstract
Artificial intelligence can accelerate strategic analysis, expand information processing, standardize documentation, and improve access to prior decisions. Yet repeated reliance on AI-generated interpretations and recommendations may also weaken the human and organizational understanding of why choices were made, which assumptions were decisive, and how actions produced outcomes. This paper develops the construct of strategic memory debt: the accumulated loss, weakening, or inaccessibility of organizational causal knowledge produced when AI assistance substitutes for sustained managerial reasoning, explanation, and reflection. The theoretical model links AI decision dependence to reduced human analytical engagement, weaker causal-knowledge retention, organizational forgetting, and debt accumulation, which in turn constrains strategic adaptability and renewal. Environmental turbulence and managerial turnover intensify these effects, whereas decision traceability, explainable AI, and reflective human-AI routines operate as protective or restorative mechanisms. Eight propositions specify the causal structure and boundary conditions. A transparent stock-and-flow formulation and a 50-period Python simulation illustrate three trajectories: high AI dependence with weak reflection, balanced human-AI collaboration, and high AI dependence supported by strong traceability and renewal routines. The illustrative results show that dependence is not inherently harmful; debt grows when displacement exceeds knowledge reconstruction and reflective repayment. The paper contributes a process explanation for why firms can retain extensive digital records yet lose the causal understanding needed for strategic renewal.
References
M. S. Jansen van Rensburg, “Using organisational memory in evaluations,” African Evaluation Journal, vol. 2, no. 1, Art. 75, pp. 1–9, 2014. doi: 10.4102/aej.v2i1.75. Open access: https://aejonline.org/index.php/aej/article/view/75
P. Martin de Holan and N. Phillips, “Remembrance of things past? The dynamics of organizational forgetting,” Management Science, vol. 50, no. 11, pp. 1603–1613, 2004. doi: 10.1287/mnsc.1040.0273. Open-access manuscript: https://ssrn.com/abstract=590003
M. S. Feldman and B. T. Pentland, “Reconceptualizing organizational routines as a source of flexibility and change,” Administrative Science Quarterly, vol. 48, no. 1, pp. 94–118, 2003. doi: 10.2307/3556620. Open-access version: https://escholarship.org/uc/item/6rb6f923
J.-G. Cegarra-Navarro, “Linking unlearning with innovation through organizational memory and technology,” Electronic Journal of Knowledge Management, vol. 8, no. 1, pp. 179–187, 2010. Open access: https://academic-publishing.org/index.php/ejkm/article/view/884
M. Zollo and S. G. Winter, “Deliberate learning and the evolution of dynamic capabilities,” Organization Science, vol. 13, no. 3, pp. 339–351, 2002. doi: 10.1287/orsc.13.3.339.2780. Open-access working paper: https://flora.insead.edu/fichiersti_wp/inseadwp2001/2001-60.pdf
D. J. Teece, G. Pisano, and A. Shuen, “Dynamic capabilities and strategic management,” Strategic Management Journal, vol. 18, no. 7, pp. 509–533, 1997. doi: 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z. Open-access copy: https://citeseerx.ist.psu.edu/document?doi=08a6d05e3355519bcc55f704b94afe62ba5ea51b
R. Agarwal and C. E. Helfat, “Strategic renewal of organizations,” Organization Science, vol. 20, no. 2, pp. 281–293, 2009. doi: 10.1287/orsc.1090.0423. Open-access author copy: https://www.terpconnect.umd.edu/~rajshree/research/32%20Agarwal%2C%20Helfat%20-%202009.pdf
C. E. Helfat and M. A. Peteraf, “Managerial cognitive capabilities and the microfoundations of dynamic capabilities,” Strategic Management Journal, vol. 36, no. 6, pp. 831–850, 2015. doi: 10.1002/smj.2247. Open-access publisher full text: https://sms.onlinelibrary.wiley.com/doi/full/10.1002/smj.2247
M. H. Jarrahi, “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making,” Business Horizons, vol. 61, no. 4, pp. 577–586, 2018. doi: 10.1016/j.bushor.2018.03.007. Open-access author copy: https://www.jarrahi.com/publication.html
S. Raisch and S. Krakowski, “Artificial intelligence and management: The automation–augmentation paradox,” Academy of Management Review, vol. 46, no. 1, pp. 192–210, 2021. doi: 10.5465/amr.2018.0072. Open-access accepted manuscript: https://zenodo.org/records/8338404
Y. R. Shrestha, S. M. Ben-Menahem, and G. von Krogh, “Organizational decision-making structures in the age of artificial intelligence,” California Management Review, vol. 61, no. 4, pp. 66–83, 2019. doi: 10.1177/0008125619862257. Open-access author copy: https://ethz.ch/content/dam/ethz/special-interest/mtec/strategic-mgmt-and-innovation/img_news/cmr.pdf
D. Dellermann, N. Lipusch, P. Ebel, and J. M. Leimeister, “Design principles for a hybrid intelligence decision support system for business model validation,” Electronic Markets, vol. 29, no. 3, pp. 423–441, 2019. doi: 10.1007/s12525-018-0309-2. Open-access version: https://arxiv.org/abs/2105.03356
M. H. Jarrahi, D. Askay, A. Eshraghi, and P. Smith, “Artificial intelligence and knowledge management: A partnership between human and AI,” Business Horizons, vol. 66, no. 1, pp. 87–99, 2023. doi: 10.1016/j.bushor.2022.03.002. Open access: https://doi.org/10.1016/j.bushor.2022.03.002
F. Olan, E. O. Arakpogun, J. Suklan, F. Nakpodia, N. Damij, and U. Jayawickrama, “Artificial intelligence and knowledge sharing: Contributing factors to organizational performance,” Journal of Business Research, vol. 145, pp. 605–615, 2022. doi: 10.1016/j.jbusres.2022.03.008. Open access: https://doi.org/10.1016/j.jbusres.2022.03.008
D. Lyell and E. Coiera, “Automation bias and verification complexity: A systematic review,” Journal of the American Medical Informatics Association, vol. 24, no. 2, pp. 423–431, 2017. doi: 10.1093/jamia/ocw105. PMCID: PMC7651899. Open access: https://pmc.ncbi.nlm.nih.gov/articles/PMC7651899/
V. Lai, C. Chen, A. Smith-Renner, Q. V. Liao, and C. Tan, “Towards a science of human-AI decision making: An overview of design space in empirical human-subject studies,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023, pp. 1369–1385. doi: 10.1145/3593013.3594087. Open-access record: https://par.nsf.gov/biblio/10491345
T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence, vol. 267, pp. 1–38, 2019. doi: 10.1016/j.artint.2018.07.007. Open-access version: https://arxiv.org/abs/1706.07269
A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, “Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, 2020. doi: 10.1016/j.inffus.2019.12.012. Open-access version: https://arxiv.org/abs/1910.10045
G. Bansal, T. Wu, J. Zhou, R. Fok, B. Nushi, E. Kamar, M. T. Ribeiro, and D. S. Weld, “Does the whole exceed its parts? The effect of AI explanations on complementary team performance,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Art. 81, pp. 1–16, 2021. doi: 10.1145/3411764.3445717. Open-access version: https://arxiv.org/abs/2006.14779
C. Gonzalez, K. Donahue, D. G. Goldstein, H. Heidari, M. S. Jalali, B. Schelble, A. Singh, and A. W. Woolley, “Toward a science of human–AI teaming for decision making: A complementarity framework,” PNAS Nexus, vol. 5, no. 3, Art. pgag030, 2026. doi: 10.1093/pnasnexus/pgag030. PMCID: PMC12983458. Open access: https://pmc.ncbi.nlm.nih.gov/articles/PMC12983458/
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
