Home⇛Journal of Interdisciplinary Perspectives⇛vol. 4 no. 9 (2026)

Artificial Intelligence as a Strategic Resource in Business: A Systematic Literature Review

Loui Maie De La Fuente | Francis Michael P. Yambao

Discipline: business studies

 

Abstract:

Artificial intelligence (AI) has emerged as an important strategic resource in the contemporary business environment, influencing organizational competitiveness, innovation, and decision-making across multiple industries. This systematic literature review examines how the existing literature positions AI in the development of business strategies across the manufacturing, financial, retail, e-commerce, education, agriculture, and technology sectors. Following the PRISMA 2020 guidelines, studies published between 2015 and 2025 were identified through Scopus, Web of Science, ScienceDirect, and Google Scholar, screened using predefined inclusion and exclusion criteria, and evaluated based on publication year, document type, language, research themes, empirical contribution, theoretical value, and journal impact factor. Of the records retrieved, 20 articles met the eligibility criteria and were included in the final review. The findings indicate that most studies relied on secondary data sources, including patent databases, bibliometric records, company datasets, and published literature, whereas relatively few employed primary data collected through surveys, interviews, or organizational respondents. The review also found that much of the literature adopts a global or non-region- specific perspective, with limited empirical evidence from specific regional contexts. Across the reviewed studies, machine learning, AI-based computational modeling, text mining, natural language processing, and bibliometric analysis were the most frequently used methodological approaches. The literature also consistently identifies implementation challenges, ethical considerations, governance issues, and organizational readiness as critical factors influencing AI adoption. Overall, the reviewed literature positions AI as a strategic resource that can enhance business performance and competitiveness when supported by appropriate governance, organizational capabilities, and context-specific implementation.



References:

  1. Akter, S., Hossain, M. A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D., & McCarthy, G. (2023). A framework for AI-powered service innovation capability: Review and agenda for future research. Technovation, 125, 102768-102768. https://doi.org/10.1016/j.technovation.2023.102768
  2. Alhajeri, R., & Alhashem, A. (2023). Using artificial intelligence to combat money laundering. Intelligent Information Management, 15(4), 284-305. https://doi.org/10.4236/iim.2023.154014
  3. Attard-Frost, B., & Widder, D. G. (2025). The ethics of AI value chains. Big Data & Society, 12(2). https://doi.org/10.1177/20539517251340603
  4. Bampasidou, M., Goldgaber, D., Gentimis, T., & Mandalika, A. (2024). Overcoming "Digital Divides": Leveraging higher education to develop next generation digital agriculture professionals. Computers and Electronics in Agriculture, 224, 109181-109181. https://doi.org/10.1016/j.compag.2024.109181
  5. Bucovețchi, O., Voipan, A. E., Voipan, D., & Stanciu, R. (2025). Redefining organizational resilience and success: A natural language analysis of strategic domains, semantics, and AI opportunities. Systems, 13(11), 999. https://doi.org/10.3390/systems13110999
  6. Cepa, K., & Schildt, H. (2022). What to teach when we teach digital strategy? An exploration of the nascent field. Long Range Planning, 102271. https://doi.org/10.1016/j.lrp.2022.102271
  7. Coccia, M. (2025). How research variability drives technological evolution for fostering and managing emerging innovations. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-025-02816-4
  8. Ding, S., Du, M., Cui, T., Zhang, Y., & Duygun, M. (2024). Impact of board diversity on Chinese firms' cross-border M&A performance: An artificial intelligence approach. International Review of Economics & Finance, 92, 1321-1335. https://doi.org/10.1016/j.iref.2024.02.077
  9. Fuster, A., Goldsmith‐Pinkham, P., Ramadorai, T., & Walther, A. (2021). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5-47. https://doi.org/10.1111/jofi.13090
  10. George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196. https://doi.org/10.3390/admsci13090196
  11. Gouveia, S., de la Iglesia, D., Abrantes, J. L., López Rivero, A., Silva, E., Gouveia, E., & Santos, V. (2024). Electrifying strategic management: Leveraging electric vehicles for sustainable value creation? Energies, 17(18), 4581-4581. https://doi.org/10.3390/en17184581
  12. Han, N., Xu, W., Song, Q., Zhao, K., & Xu, Y. (2025). Application of interpretable artificial intelligence for sustainable tax management in the manufacturing industry. Sustainability, 17(3), 1121-1121. https://doi.org/10.3390/su17031121
  13. Hui, X., & Tucker, C. (2025). Decentralization, blockchain, artificial intelligence (AI): Challenges and opportunities. Journal of Product Innovation Management. https://doi.org/10.1111/jpim.12800
  14. Hyiamang, O., & Liu, X. (2025). Artificial intelligence (AI) strategies for organizational innovation, growth, and productivity: A multi-case study approach. Issues in Information Systems, 26(1), 20-36. https://doi.org/10.48009/1_iis_103
  15. Islam, T., Islam, S. A. M., Sarkar, A., Khan, A. J. M. O. R., Paul, R., & Bari, M. S. (2024). Artificial intelligence in fraud detection and financial risk mitigation: Future directions and business applications. International Journal for Multidisciplinary Research, 6(5). https://doi.org/10.36948/ijfmr.2024.v06i05.28496
  16. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  17. Kelly, M., Peters, T. M., & Farber, J. S. (2024). Secondary data analysis: Using existing data to answer new questions. Journal of Pediatric Health Care, 38(4), 615-618. ScienceDirect. https://doi.org/10.1016/j.pedhc.2024.03.005
  18. Krakowski, S., Luger, J., & Raisch, S. (2022). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425-1452. https://doi.org/10.1002/smj.3387
  19. Lim, F. P. (2025). The transformative impact of artificial intelligence on business operations across industries. International Journal of Research Publication and Reviews, 6(10), 214-221. https://doi.org/10.55248/gengpi.6.1025.3507
  20. Liu, Q., & Li, J. (2022). The progress of business analytics and knowledge management for enterprise performance using artificial intelligence and man-machine coordination. Journal of Global Information Management, 30(11). https://doi.org/10.4018/jgim.302642
  21. Maldonado-Canca, L.-A., Cabrera-Sánchez, J.-P., & Casado-Molina, A.-M. (2025). AI in enterprise management: Determinants of purchase intention among CEOs without AI experience. Journal of Enterprise Information Management. https://doi.org/10.1108/jeim-03-2025-0230
  22. Mohd Amin, M. R., Asbi, A., Sivakumaran, V. M., Kim, J., & Septiarini, E. (2025). Artificial intelligence (AI) adoption in marketing strategies: Navigating the present and shaping the future business landscape. Social Sciences & Humanities Open, 12, 102048. https://doi.org/10.1016/j.ssaho.2025.102048
  23. Nweje, U., & Taiwo, M. (2025, January 10). Leveraging artificial intelligence for predictive supply chain management, focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2025.14.1.0027
  24. Paramita, D., Okwir, S., & Nuur, C. (2024). Artificial intelligence in talent acquisition: Exploring organisational and operational dimensions. International Journal of Organizational Analysis, 32(11), 108-131. https://doi.org/10.1108/ijoa-09-2023-3992
  25. Plathottam, S. J., Rzonca, A., Lakhnori, R., & Iloeje, C. (2023). A review of artificial intelligence applications in manufacturing operations. Journal of Advanced Manufacturing and Processing, 5(3). https://doi.org/10.1002/amp2.10159
  26. Pu, Y., Li, H., Hou, W., & Pan, X. (2025). The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-02842-x
  27. Ragazou, K., Garefalakis, A., Zafeiriou, E., & Passas, I. (2022). Agriculture 5.0: A new strategic management mode for a cut cost and an energy efficient agriculture sector. Energies, 15(9), 3113. https://doi.org/10.3390/en15093113
  28. Ruoxing, C., Wang, J., Basem, A., Hussein, R. A., Salahshour, S., & Baghaei, S. (2025, March 1). Examining the application of strategic management and artificial intelligence, with a focus on artificial neural network modeling to enhance human resource optimization with advertising and brand campaigns. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2025.110029
  29. Štrukelj, T., & Dankova, P. (2025). Ethical leadership and management of small- and medium-sized enterprises: The role of AI in decision making. Administrative Sciences, 15(7), 274. https://doi.org/10.3390/admsci15070274
  30. Suominen, A., Toivanen, H., & Seppänen, M. (2017). Firms' knowledge profiles: Mapping patent data with unsupervised learning. Technological Forecasting and Social Change, 115, 131-142. https://doi.org/10.1016/j.techfore.2016.09.028
  31. Wang, W. (2024). Artificial intelligence in strategic business decisions: Enhancing market competitiveness. Advances in Economics, Management and Political Sciences, 117(1), 87-93. https://doi.org/10.54254/2754-1169/117/20241987

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