EVOLUTION OF ARTIFICIAL INTELLIGENCE IN STRATEGIZING
Abstract and keywords
Abstract:
The ongoing digital transformation intensifies the demand for sophisticated organizational management tools that are capable of navigating the growing global uncertainty. While Artificial Intelligence (AI) excels in data analysis and scenario modeling, its specific efficiency within strategic management remains vague. This article explores the conceptual evolution of AI and its prospective application as a tool for strategizing. Drawing on Professor V.L. Kvint’s methodology, which defines strategy as a conscious choice of development trajectory, the study employs comparative and historical-logical analysis to evaluate AI’s analytical capabilities. The research traces AI development from early formalization concepts to the latest machine learning paradigms and integrative approaches. While AI enhances strategizing through big data processing and scenario modeling, algorithmic models remain limited in that they cannot yet account for the value-based foundations, human interests, and long-term priorities essential to strategy. The research results provide a framework for designing strategic decision-support systems and integrating AI into strategic management frameworks.

Keywords:
artificial intelligence, strategizing, digital transformation, concept evolution, intelligent systems
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References

1. Wiener N. Cybernetics, or control and communication in an animal and a machine. Moscow: Nauka; 1983. 344 p. (In Russ.)

2. Glazyev SYu. Teoriya dolgosrochnogo texniko-e`konomicheskogo razvitiya [Theory of long-term technical and economic development]. Moscow: VlaDar; 1993. 310 p. (In Russ.) https://elibrary.ru/YSXIUV

3. Zhuravlev DM. Strategizing of digital transformation of complex socio-economic systems. Ed. VL Kvint. St. Petersburg: IPC SZIU RANEPA; 2024. 352 p. (In Russ.) https://doi.org/10.55959/978-5-89781-862-4

4. Kantorovich LV. Matematicheskie metody` organizacii i planirovaniya proizvodstva [Mathematical methods of organization and planning of production]. Leningrad: Leningrad State University; 1939. 68 p. (In Russ.) https://elibrary.ru/ZIGTUB

5. Kvint VL. The concept of strategizing. Vol. 1. St. Petersburg: NWIM RANEPA; 2019. 132 p. (In Russ.) https://elibrary.ru/VUMJTW

6. Akaev AA, Devezas TK, Korablev VV, Sarygulov AI. Critical technologies and prospects for Russia’s development under economic and technological restrictions. Terra Economicus. 2024;22(2):6–21. (In Russ.) https://doi.org/10.18522/2073-6606-2024-22-2-6-21

7. Makarov VL, Bakhtizin AR. Modern tools for modeling socio-economic processes. Economy of the North-West: Problems and prospects of development. 2024;(1):21–32. (In Russ.) https://doi.org/10.52897/2411-4588-2024-1-21-32

8. Makarov VL, Bakhtizin AR, Epstein DM. Agent-based modeling for a complex world. Moscow: Maks Press; 2022. 88 p. (In Russ.) https://elibrary.ru/BHWTOH

9. Nekipelov AD. On economic strategy and economic policy in Russia under current conditions. Scientific works of the Free Economic Society of Russia. 2021;230(4):76–89. (In Russ.) https://doi.org/10.38197/2072-2060-2021-230-4-76-89

10. Novikova IV. Strategic leader in the digital economy: Role, qualities and characteristics. Social and Labor Research. 2021;(4):150–160. (In Russ.) https://elibrary.ru/GWMTPO

11. Polterovich VM. Institucional`ny`e lovushki i e`konomicheskie reformy` [Institutional traps and economic reforms]. Economics and mathematical methods. 1999;35(2):3–20. (In Russ.) https://elibrary.ru/QOLEBJ

12. Sasaev NI, Kvint VL. Strategizing the industrial core of the national economy. Russian Journal of Industrial Economics. 2024;17(3):245–260. (In Russ.) https://doi.org/10.17073/2072-1633-2024-3-1349

13. Chaudhry M, Shafi I, Mahnoor M, Vargas DLR, Thompson EB, Ashraf I. A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry. 2023;15(9):1679. https://doi.org/10.3390/sym15091679

14. Akaev AA, Sadovnichiy VA. Information models for forecasting nonlinear economic dynamics in the digital era. Applied Mathematics. 2021;12:171–208. https://doi.org/10.4236/am.2021.123012

15. Ansoff HI. Corporate strategy: An analytic approach to business policy for growth and expansion. NY: McGraw-Hill; 1965. 241 p.

16. Arrow KJ. Social choice and individual values. Connecticut: Martino Fine Books; 2012. 110 p.

17. Biloslavo R, Edgar D, Aydin E, Bulut C. Artificial intelligence (AI) and strategic planning process within VUCA environments: A research agenda and guidelines. Management Decision. 2024;63(10):3599–3624. https://doi.org/10.1108/MD-10-2023-1944

18. Wong LW, Tan GWH, Ooi KB, Lin B, Dwivedi YK. Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research. 2022;62(15):5535–5555. https://doi.org/10.1080/00207543.2022.2063089

19. Barney J. Firm resources and sustained competitive advantage. Journal of Management. 1991;17(1):99–120. https://doi.org/10.1177/014920639101700108

20. Egger R, Yu J. A topic modeling comparison between LDA, NMF, Top2Vec, and BERTopic to demystify Twitter posts. Frontiers in Sociology. 2022;7:886498. https://doi.org/10.3389/fsoc.2022.886498

21. Kahneman D. Thinking, fast and slow. NY: Farrar, Straus and Giroux; 2011. 512 p.

22. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. Econometrica. 1979;47(2):263–292. https://doi.org/10.2307/1914185

23. Klein G. Sources of power: How people make decisions. Leadership and Management in Engineering. 2001;1:21–21. https://doi.org/10.1061/(ASCE)1532-6748(2001)1:1(21)

24. Porter M. Competitive strategy: Techniques for analyzing industries and competitors. NY: Free Press; 1980. 396 p.

25. Raisch S, Krakowski S. Artificial intelligence and management: The automation – augmentation paradox. Academy of Management Review. 2021;46(1):192–210. https://doi.org/10.5465/amr.2018.0072

26. Russell SJ, Norvig P. Artificial intelligence: A modern approach. Harlow: Pearson; 2020. 1115 p.

27. Sawicki J, Ganzha M, Paprzycki M. The state of the art of natural language processing – A systematic automated review of NLP literature using NLP techniques. Data Intelligence. 2023;5(3):707–749. https://doi.org/10.1162/dint_a_00213

28. Simon A. Administrative behavior: A study of decision-making processes in administrative organization. NY: Simon and Schuster; 1997. 368 p.

29. Simon HA. A behavioral model of rational choice. The Quarterly Journal of Economics. 1955;69(1):99–118. https://doi.org/10.2307/1884852

30. Teece DJ, Pisano G, Shuen A. Dynamic capabilities and strategic management. Strategic Management Journal. 2008:27–51. https://doi.org/10.1142/9789812834478_0002

31. Pu Y, Li H, Hou W, Pan X. The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence. Scientific Reports. 2025;15:17942. https://doi.org/10.1038/s41598-025-02842-x

32. Makridakis S, Spiliotis E, Assimakopoulos V, Chen Z, Gaba A, Tsetlin I, et al. The M5 uncertainty competition: Results, findings and conclusions. International Journal of Forecasting. 2022;38(4):1365–1385. https://doi.org/10.1016/j.ijforecast.2021.10.009

33. Turing AM. Computing machinery and intelligence. Mind. 1950;LIX(236):433–460. https://doi.org/10.1093/mind/LIX.236.433

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