Artificial Intelligence in Strategic Decision Making: A Systematic Literature Review on Opportunities, Applications, and Challenges

Abstract

This study presents a systematic literature review on the role of Artificial Intelligence (AI) in strategic decision-making, synthesizing insights from 87 peer-reviewed articles published between 2018 and 2025. Drawing on the Resource-Based View, Dynamic Capabilities Theory, and the Technology–Organization–Environment framework, the study investigates how AI contributes to forecasting, resource optimization, and real-time decision-making, while also addressing ethical, technical, and organizational challenges. The findings are organized around four thematic areas: data-informed forecasting and planning, resource allocation efficiency, real-time strategic responsiveness, and implementation barriers such as data privacy, algorithmic bias, and governance issues. The review highlights that while AI offers transformative strategic potential, its successful adoption is contingent upon organizational readiness, leadership, digital maturity, and ethical frameworks. The paper concludes by proposing a research agenda that emphasizes sector-specific strategies, the role of explainable AI, and the inclusion of small and medium-sized enterprises (SMEs) in future studies. This review aims to inform scholars, practitioners, and policymakers on the critical enablers and inhibitors of AI integration in strategic contexts.

Keywords

• Artificial Intelligence
• Strategic Decision-Making
• Resource-Based View
• Dynamic Capabilities
• Organizational Readiness
• Digital Strategy

Stratejik Karar Almada Yapay Zeka: Fırsatlar, Uygulamalar ve Zorluklar Üzerine Sistematik Bir Literatür İncelemesi

Abstract

Bu çalışma, Yapay Zeka'nın (YZ) stratejik karar almadaki rolü üzerine sistematik bir literatür incelemesi sunmakta ve 2018 ile 2025 yılları arasında yayınlanmış 87 hakemli makaleden elde edilen bilgileri sentezlemektedir. Kaynak Tabanlı Görüş, Dinamik Yetenekler Teorisi ve Teknoloji-Örgüt-Çevre çerçevesinden yararlanan çalışma, YZ'nin tahmin, kaynak optimizasyonu ve gerçek zamanlı karar almaya nasıl katkıda bulunduğunu incelerken, aynı zamanda etik, teknik ve organizasyonel zorlukları da ele almaktadır. Bulgular dört tematik başlık altında sınıflandırılmıştır: veriye dayalı öngörü ve planlama, kaynak tahsisinde etkinlik, stratejik tepkisellikte gerçek zamanlılık ve veri gizliliği, algoritmik önyargılar ve yönetişim sorunları gibi uygulama engelleri. Çalışma, YZ’nin stratejik bağlamda dönüştürücü bir potansiyel taşıdığını ortaya koymakla birlikte, bu potansiyelin etkili biçimde hayata geçirilebilmesinin örgütsel hazırlık düzeyi, liderlik niteliği, dijital olgunluk seviyesi ve etik çerçevelerle doğrudan ilişkili olduğunu vurgulamaktadır. Makale, sektör bazlı stratejilere, açıklanabilir yapay zekânın rolüne ve küçük ve orta ölçekli işletmelerin (KOBİ) gelecekteki çalışmalara dâhil edilmesine odaklanan bir araştırma gündemi önererek sonlanmaktadır. Bu derleme, YZ’nin stratejik bağlamlarda entegrasyonunu etkileyen temel kolaylaştırıcı ve engelleyici unsurlar hakkında akademisyenlere, uygulayıcılara ve politika yapıcılara rehberlik etmeyi amaçlamaktadır.

Keywords

• Yapay Zeka
• Stratejik Karar Alma
• Kaynak Tabanlı Bakış Açısı
• Dinamik Yetenekler
• Kurumsal Hazırlık
• Dijital Strateji

References

1. Agrawal, P., Nikhade, P., & Nikhade, P. P. (2022). Artificial intelligence in dentistry: Past, present, and future. Cureus, 14(7), e27405-e27405. https://doi.org/10.7759/cureus.27405
2. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
3. Bibri, S. E. (2021). Data-driven smart sustainable cities of the future: Urban computing and intelligence for strategic, short-term, and joined-up planning. Computational Urban Science, 1(1), 8.
4. Biloslavo, R., Edgar, D., Aydin, E., & Bulut, C. (2024). Artificial intelligence (AI) and strategic planning process within VUCA environments: a research agenda and guidelines. Management Decision, 63(10), 3599–3624. https://doi.org/10.1108/md-10-2023-1944
5. Brynjolfsson, E., & McAfee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 7(1), 1-31.
6. Büber, H., & Seven, E. (2025). Strategic decision-making in the AI era: An integrated approach classical, adaptive, resource-based, and processual views. International Journal of Management and Administration, 9(17), 67-97.
7. Castillo, M. J., & Taherdoost, H. (2023). The impact of AI technologies on e-business. Encyclopedia, 3(1), 107-121.
8. 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.

9. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
10. 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.
11. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
12. Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., & Foropon, C. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics, 250, 108618.
13. Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
14. Farayola, O. A., Abdul, A. A., Irabor, B. O., & Okeleke, E. C. (2023). Innovative business models driven by ai technologies: a review. Computer Science & IT Research Journal, 4(2), 85-110.
15. Fogel, D. B. (2022). Defining artificial intelligence. In S. Carta (Ed.) Machine Learning and the City: Applications in Architecture and Urban Design, 91-120. https://doi.org/10.1002/9781119815075.ch7
16. Gînguță, A., Ștefea, P., Noja, G. G., & Munteanu, V. P. (2023). Ethical impacts, risks and challenges of Artificial Intelligence technologies in business consulting: A new modelling approach based on structural equations. Electronics, 12(6), 1462.
17. Khatri, N., & Ng, H. A. (2000). The role of intuition in strategic decision making. Human relations, 53(1), 57-86.
18. Kitchenham, B. (2004). Procedures for performing systematic reviews (Joint Technical Report TR/SE-0401 & NICTA Technical Report 0400011T.1). Keele University & National ICT Australia.
19. Kumar, V. (2023). The economic value of digital disruption: A holistic assessment for CXOs. Springer. https://doi.org/10.1007/978-981-19-8148-7
20. Lavin, A., Krakauer, D., Zenil, H., Gottschlich, J., Mattson, T., Brehmer, J., Anandkumar, A., Choudry, S., Rocki, K., Baydin, A. G., Prunkl, C., Paige, B., Isayev, O., Peterson, E., McMahon, P. L., Macke, J., Cranmer, K., Zhang, J., Wainwright, H., … Pfeffer, A. (2021). Simulation intelligence: Towards a new generation of scientific methods. arXiv. https://arxiv.org/abs/2112.03235
21. Mikalef, P., Fjørtoft, S. O., & Torvatn, H. Y. (2019). Developing an artificial intelligence capability: A theoretical framework for business value. In Business Information Systems Workshops: BIS 2019 International Workshops, Seville, Spain, June 26–28, 2019, Revised Papers 22 (pp. 409-416). Springer International Publishing.
22. Mintzberg, H. (1979). The structuring of organizations. In D. Asch & C. Bowman (Eds.), Readings in strategic management (pp. 322-352). Macmillan Education UK.
23. Mondal, S., Das, S., & Vrana, V. G. (2023). How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life. Technologies, 11(2), 44.
24. Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., De-la-Hoz-Franco, E., & De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. In A. Jain, D. Vasant, R. Kumar, & J. Kacprzyk (Eds.), Advances in intelligent data analysis and applications: Proceedings of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania (pp. 309–325). Springer Singapore.
25. Nigmatov, A., & Pradeep, A. (2023, September). The impact of AI on business: Opportunities, risks, and challenges. In 2023 13th International Conference on Advanced Computer Information Technologies (ACIT) (pp. 618–622). IEEE.
26. Porter, M. E. (1996). Competitive advantage, agglomeration economies, and regional policy. International Regional Science Review, 19(1-2), 85-90.
27. Shaheen, M. Y. (2021). Applications of artificial intelligence (AI) in healthcare: A review. ScienceOpen Preprints. https://doi.org/10.14293/S2199-1006.1.SOR-.PPVRY8K.v1
28. Simon, H. A. (2000). Bounded rationality in social science: Today and tomorrow. Mind & Society, 1(1), 25-39.
29. Steyvers, M., & Kumar, A. (2024). Three challenges for AI-assisted decision-making. Perspectives on Psychological Science, 19(5), 722-734.
30. Tarawneh, M., AbdAlwahed, H., & Alzyoud, F. (2024). Innovating project management: AI applications for success prediction and resource optimization. In A. Abraham, A. Gandhi, & P. Dutta (Eds.), Lecture notes in networks and systems (Vol. 839, pp. 382–391). Springer Nature. https://doi.org/10.1007/978-3-031-56950-0_32
31. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
32. Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, 1-10.
33. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
34. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222.
35. Wamba-Taguimdje, S. L., Wamba, S. F., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1905–1933.
36. Williamson, S. M., & Prybutok, V. (2024). Balancing privacy and progress: A review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Applied Sciences, 14(2), 675.