Governing AI-Assisted Strategy Tools: A Substitutability–Commitment Framework and the Role of Epistemic Custody
DOI:
https://doi.org/10.19166/ms.v6i2.11693Schlagworte:
Artificial Intelligence, Strategy Tools, Strategic Decision-Making, Epistemic Custody, Corporate GovernanceAbstract
Artificial intelligence now performs much of the analytical work that established strategy tools were designed to structure. Existing research asks which tools can be automated. This article asks a prior question: whether the authority and organisational value of a strategic instrument survive when its analytical core is machine-generated. Approach. The article is conceptual and follows a theory-construction design, synthesising the strategy-as-practice, AI-enabled decision-making and corporate governance literatures through a documented search and inclusion procedure. Framework. The Substitutability–Commitment Framework classifies strategic instruments along two independent dimensions: analytical substitutability, the extent to which a tool’s cognitive work is machine-executable, and commitment durability, the extent to which its output binds organisational resources. Crossing the dimensions yields four classes, each implying a distinct governance response. The framework is complemented by epistemic custody, defined as the explicit allocation of responsibility for producing, verifying, contesting and authorising strategic judgements. Contribution. The article separates substitutability from commitment, identifies three observable failure modes, maps thirteen widely used instruments to the framework, and advances seven refutable propositions. Epistemic custody is distinguished from accountability, human oversight and decision rights. Implications. Boards require custody arrangements rather than technical assurance alone. The withdrawal of South Africa’s Draft National Artificial Intelligence Policy in April 2026, following the discovery of fabricated references, is used solely to illustrate the framework’s plausibility. It is not evidence validating the framework, which remains untested.
Literaturhinweise
Alekseeva, L., Azar, J., Giné, M., & Samila, S. (2026). Artificial intelligence adoption and the demand for managerial expertise. Strategic Management Journal, 1(1), 1–27. https://doi.org/10.1002/smj.70099
Anthony, C., Bechky, B. A., & Fayard, A.-L. (2023). "Collaborating" with AI: Taking a system view to explore the future of work. Organization Science, 34(5), 1672–1694. https://doi.org/10.1287/orsc.2022.1651
ASEAN. (2024). ASEAN guide on AI governance and ethics. https://asean.org/wp-content/uploads/2024/02/ASEAN-Guide-on-AI-Governance-and-Ethics_beautified_201223_v2.pdf
ASEAN. (2025). Expanded ASEAN guide on AI governance and ethics – generative AI. https://asean.org/wp-content/uploads/2025/01/Expanded-ASEAN-Guide-on-AI-Governance-and-Ethics-Generative-AI.pdf
Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: Themes, knowledge gaps and future agendas. Internet Research, 33(7), 133–167. https://doi.org/10.1108/INTR-01-2022-0042
Brynjolfsson, E. (2022). The Turing trap: The promise and peril of human-like artificial intelligence. Daedalus, 151(2), 272–287. https://doi.org/10.1162/daed_a_01915
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
Burke, G. T., & Wolf, C. (2021). The process affordances of strategy toolmaking when addressing wicked problems. Journal of Management Studies, 58(2), 359–388. https://doi.org/10.1111/joms.12572
Csaszar, F. A., Ketkar, H., & Kim, H. (2024). Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors. Strategy Science, 9(4), 322–345. https://doi.org/10.1287/stsc.2024.0190
Department of Communications and Digital Technologies. (2024). South Africa national artificial intelligence policy framework. https://www.dcdt.gov.za/sa-national-ai-policy-framework/file/338-sa-national-ai-policy-framework.html
Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2025). Generative artificial intelligence and evaluating strategic decisions. Strategic Management Journal, 46(3), 583–610. https://doi.org/10.1002/smj.3677
European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (artificial intelligence act). https://eur-lex.europa.eu/eli/reg/2024/1689/oj
George, B. (2025). Towards purposeful strategic planning: A mixed research synthesis across disciplines. Long Range Planning, 58(4), 2–11. https://doi.org/10.1016/j.lrp.2025.102563
Hilb, M. (2020). Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance. Journal of Management and Governance, 24, 851–870. https://doi.org/10.1007/s10997-020-09519-9
Institute of Directors in South Africa. (2016). King IV report on corporate governance for South Africa 2016.
International Organization for Standardization. (2023). ISO/IEC 42001:2023 — Information technology — Artificial intelligence — Management system. https://www.iso.org/standard/81230.html
Jarzabkowski, P., Seidl, D., & Balogun, J. (2022). From germination to propagation: Two decades of strategy-as-practice research and potential future directions. Human Relations, 75(8), 1533–1559. https://doi.org/10.1177/00187267221089473
Kazim, E., Denny, D. M. T., & Koshiyama, A. (2021). AI auditing and impact assessment: According to the UK Information Commissioner’s Office. AI and Ethics, 1, 301–310. https://doi.org/10.1007/s43681-021-00039-2
Keding, C. (2021). Understanding the interplay of artificial intelligence and strategic management: Four decades of research in review. Management Review Quarterly, 71(1), 91–134. https://doi.org/10.1007/s11301-020-00181-x
Kemp, A. (2024). Competitive advantage through artificial intelligence: Toward a theory of situated AI. Academy of Management Review, 49(3), 618–635. https://doi.org/10.5465/amr.2020.0205
Kim, H., Glaeser, E. L., Hillis, A., Kominers, S. D., & Luca, M. (2024). Decision authority and the returns to algorithms. Strategic Management Journal, 45(4), 619–648. https://doi.org/10.1002/smj.3569
Kleinberg, J., & Raghavan, M. (2021). Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences, 118(22), 1–7. https://doi.org/10.1073/pnas.2018340118
Kohtamäki, M., Whittington, R., Vaara, E., & Rabetino, R. (2022). Making connections: Harnessing the diversity of strategy-as-practice research. International Journal of Management Reviews, 24(2), 210–232. https://doi.org/10.1111/ijmr.12274
Kourabas, S., & Tsang, C.-Y. (2025). The board monitoring function: Artificial intelligence in the era of heightened accountability. The Chinese Journal of Comparative Law, 13, 1–29. https://doi.org/10.1093/cjcl/cxaf013
Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/10.1002/smj.3387
Laato, S., Tiainen, M., Islam, A. K. M. N., & Mäntymäki, M. (2022). How to explain AI systems to end users: A systematic literature review and research agenda. Internet Research, 32(7), 1–31. https://doi.org/10.1108/INTR-08-2021-0600
Linardon, J., Jarman, H. K., McClure, Z., Anderson, C., Liu, C., & Messer, M. (2025). Influence of topic familiarity and prompt specificity on citation fabrication in mental health research using large language models: Experimental study. Journal of Medical Internet Research, 12, 1–8. https://doi.org/10.2196/80371
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 1–26. https://doi.org/10.1016/j.techfore.2021.121390
Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2(4), 603–609. https://doi.org/10.1007/s43681-022-00143-x
Mugaanyi, J., Cai, L., Cheng, S., Lu, C., & Huang, J. (2024). Evaluation of large language model performance and reliability for citations and references in scholarly writing: Cross-disciplinary study. Journal of Medical Internet Research, 26, 1–7. https://doi.org/10.2196/52935
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). https://www.nist.gov/itl/ai-risk-management-framework
Organisation for Economic Co-operation and Development. (2024). OECD AI principles overview. https://oecd.ai/en/ai-principles
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25(1), 123–141. https://doi.org/10.1007/s10796-022-10251-y
Qehaja, A. B., & Kutllovci, E. (2020). Strategy tools in use: New empirical insights from the strategy-as-practice perspective. Management: Journal of Contemporary Management Issues, 25(1), 145–169. https://doi.org/10.30924/mjcmi.25.1.9
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
Republic of South Africa. (2008). Companies Act 71 of 2008. Government Printer.
Republic of South Africa. (2013, November 26). Protection of Personal Information Act. Government Printer.
Republic of South Africa. (2026, April 10). Draft national artificial intelligence policy. Government Printer.
Rest of World. (2026, May 7). Fact-check fail: When AI hallucinations derail governments. https://restofworld.org/2026/government-ai-hallucinations-south-africa-deloitte/
Seidl, D., Ma, S., & Splitter, V. (2024). What makes activities strategic: Toward a new framework for strategy-as-practice research. Strategic Management Journal, 45(12), 2395–2419. https://doi.org/10.1002/smj.3668
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
Simkute, A., Tankelevitch, L., Kewenig, V., Scott, A. E., Sellen, A., & Rintel, S. (2024). Ironies of generative AI: Understanding and mitigating productivity loss in human–AI interaction. International Journal of Human–Computer Interaction, 41(5), 1–12. https://doi.org/10.1080/10447318.2024.2405782
South African Government News Agency. (2026, April 26). Minister announces withdrawal of draft AI policy. https://www.sanews.gov.za/south-africa/minister-announces-withdrawal-draft-ai-policy
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, 2293–2303. https://doi.org/10.1038/s41562-024-02024-1
Vanneste, B. S., & Puranam, P. (2025). Artificial intelligence, trust, and perceptions of agency. Academy of Management Review, 50(4), 726–744. https://doi.org/10.5465/amr.2022.0041
Wang, W., Gao, G., & Agarwal, R. (2023). Friend or foe? Teaming between artificial intelligence and workers with variation in experience. Management Science, 70(9), 5753–5775. https://doi.org/10.1287/mnsc.2021.00588
Wyk, I. V. (2023). Strategic decision-making in small and medium enterprises in South Africa. Southern African Journal of Entrepreneurship and Small Business Management, 15(1), 1–12. https://hdl.handle.net/10520/ejc-sajesbm_v15_n1_a684
Downloads
Veröffentlicht
Ausgabe
Rubrik
Lizenz
Copyright (c) 2026 Michael Willie

Dieses Werk steht unter der Lizenz Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website). The final published PDF should be used and bibliographic details that credit the publication in this journal should be included.