From Machine Learning to Organizational Learning - Research Abstract

Aug 2, 2026

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This research study examines how machine learning and organizational learning coevolve through key mechanisms. Organizational learning is framed as the creation, retention, and transfer of knowledge, where accumulated experience leads to routine modification. The growing use of AI tools in business processes and services creates two interaction scenarios: ML tools coexist with existing routines and human decisions, or ML tools replace them. The proposed coevolution model is structured around knowledge locus and agency locus in protocol development and action selection, aiming to build a theoretical framework and propositions.

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Abstract

Coevolution mechanisms

Interaction scenarios: coexistence vs replacement

Proposed model dimensions

Planned contributions

Association for Information Systems AIS Electronic Library (AISeL) | MWAIS 2024 Proceedings | Midwest (MWAIS) | | --- | --- | | 6-18-2024
From Machine Learning to Organizational Learning
Elahe Javadi
Illinois State University, [ejavadi@ilstu.edu](ejavadi@ilstu.edu)
Judith Gebauer
University of North Carolina Wilmington, [gebauerj@uncw.edu](gebauerj@uncw.edu)
Follow this and additional works at: [https://aisel.aisnet.org/mwais2024](https://aisel.aisnet.org/mwais2024) | | Recommended Citation Javadi, Elahe and Gebauer, Judith, "From Machine Learning to Organizational Learning" (2024) . MWAIS 2024 Proceedings. 21. [https://aisel.aisnet.org/mwais2024/21](https://aisel.aisnet.org/mwais2024/21) This material is brought to you by the Midwest (MWAIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in MWAIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org). From Machine Learning to Organizational Learning Elahe Javadi Illinois State University [ejavadi@ilstu.edu](ejavadi@ilstu.edu) Judith Gebauer University of North Carolina Wilmington [gebauerj@ilstu.edu](gebauerj@ilstu.edu) ABSTRACT This research study aims to identify major mechanisms through which machine learning (ML) and organizational learning coevolve. Organizational learning (OL) involves creating, retaining, and transferring knowledge. The organizational learning journey entails experience accumulation and, thus, modification of routines. The prevalence of machine learning (i.e., AI tools) in business processes, products, and services necessitates new perspectives on how OL and ML interact when (1) ML tools coexist with the current routines and human decision-making (e.g., AI stylists at Stich Fix),(2) ML tools replace current routinesand human decision-making processes (e.g., predictive maintenance models at [C3.ai](C3.ai)) . Building upon Nonaka’s model of knowledge management (Nonaka & Takeuchi, 1995) and the extant literature on AI impacts on organizational learning, we plan to develop an ML/OL coevolution model that’s anchored in the following three dimensions: (1) locus of knowledge, (2) locus of agency in protocol development, and (3) locus of agency in action selection (Murray et al., 2021) . The literature suggests that the full benefits of machine learning are realized only when organizations can learn with AI and are willing to evolve their existing routines accordingly. We draw upon the literature on organizational learning, IT-enabled knowledge management, and human-machine interaction to investigate the mechanisms of ML and OL coevolution, including its enablers and obstacles. We also plan to revisit the classic concepts of tacit and explicit knowledge, the explorationexploitation trade-off, organization memory, knowledge decay, and unlearning, to unravel how these mechanisms will be interwoven with ML processes. The intended contributions are a theoretical framework and a set of propositions. We aim to delineate specific mechanisms through which distinct ML types interact with sub-processes such as knowledge creation, retention, updating, forgetting, and transfer. Keywords Machine learning, organizational learning, locus of knowledge, locus of agency REFERENCES 1. Afiouni-Monla, R. 2019.“Organizational Learning in the Rise of Machine Learning,” Fortieth International Conference on Information Systems, Munich. 2. Argote, L., & Miron-Spektor, E. 2011. Organizational learning: From experience to knowledge. Organization Science, 22: 1123–1137. 3. Ingram, P., & Baum, J. A. 1997. Opportunity and constraint: Organizations’ learning from the operating and competitive experience of industries. Strategic Management Journal, 18: 75–98. 4. Levinthal, D. A., & March, J. G. 1993. The myopia of learning. Strategic Management Journal, 14: 95–112. 5. March, J. G. 1991. Expl

FAQs

What coevolution process does the study focus on?

The study focuses on how machine learning and organizational learning coevolve. It emphasizes mechanisms linking AI tool use with organizational knowledge processes.

How is organizational learning described in the paper?

Organizational learning involves creating, retaining, and transferring knowledge. It also includes experience accumulation that modifies routines over time.

What dimensions anchor the planned ML/OL coevolution model?

The model is anchored in three dimensions: locus of knowledge, locus of agency in protocol development, and locus of agency in action selection.

Aug 2, 2026

Upload by Oliver