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The Use of Artificial Intelligence for Decision-Making Process for Strategic Management

Yıl 2025, Cilt: 22 Sayı: 2, 195 - 210
https://doi.org/10.26466/opusjsr.1632110

Öz

Artificial intelligence (AI) is a rapidly developing technology with the potential to create profound changes across various domains, from societal structures to economic systems. This study focuses specifically on the impact of AI on strategic management decision-making processes. The primary aim is to explore how AI can enhance the quality of these processes and contribute to improved organizational performance. A literature review method was employed in the research, through which the theoretical foundations of artificial intelligence, its historical development, and its application areas in decision-making processes were thoroughly examined. The findings indicate that AI accelerates decision-making processes, leading to significant time and cost savings, while also minimizing human error. Decision support systems empowered by AI technologies enable organizations to make more accurate, consistent, and data-driven decisions in strategic management. However, to effectively integrate and benefit from these technologies, organizations must be adequately prepared in terms of both technological infrastructure and organizational culture. In this regard, the study emphasizes that AI is not merely a technical tool, but also a strategic asset that can offer competitive advantages when effectively adopted and utilized

Etik Beyan

Makale içinde sunduğum verileri, bilgileri ve dokümanları akademik ve etik kurallar çerçevesinde elde ettiğimi, Tüm bilgi, belge, değerlendirme ve sonuçları bilimsel etik ve ahlak kurallarına uygun olarak sunduğumu, Atıfta bulunduğum eserlerin tümünü kaynak olarak gösterdiğimi, Kullanılan verilerde herhangi bir değişiklik yapmadığımı, Bu makalede sunduğum çalışmanın özgün olduğunu, bildirir, aksi bir durumda aleyhime doğabilecek tüm hak kayıplarını kabullendiğimi beyan ederim

Kaynakça

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Stratejik Yönetimde Karar Alma Sürecinde Yapay Zekanın Kullanımı

Yıl 2025, Cilt: 22 Sayı: 2, 195 - 210
https://doi.org/10.26466/opusjsr.1632110

Öz

Yapay zeka, günümüzde toplumların işleyişinden ekonomik yapılara kadar pek çok alanda köklü değişimlere yol açma potansiyeline sahip, hızla gelişen bir teknolojidir. Bu çalışma, özellikle yapay zekanın stratejik yönetim karar süreçlerine olan etkisini incelemeye odaklanmaktadır. Temel amaç, yapay zekanın bu süreçlerin kalitesini nasıl artırabileceğini ve organizasyonel performansa nasıl katkı sağlayabileceğini ortaya koymaktır. Araştırmada, literatür taraması yöntemi kullanılmış ve bu bağlamda yapay zekanın teorik altyapısı, tarihsel gelişimi ve karar verme süreçlerindeki uygulama alanları detaylı biçimde ele alınmıştır. Elde edilen bulgular, yapay zekanın karar alma süreçlerini hızlandırarak zaman ve maliyet açısından önemli avantajlar sağladığını, ayrıca insan kaynaklı hataları büyük ölçüde azaltabildiğini ortaya koymaktadır. Yapay zeka destekli karar destek sistemleri sayesinde, stratejik yönetimde daha isabetli, tutarlı ve veriye dayalı kararlar alınması mümkün hale gelmektedir. Ancak bu teknolojilerin etkin bir şekilde kullanılabilmesi için, organizasyonların hem teknolojik altyapı açısından donanımlı olmaları hem de yeniliklere açık bir organizasyonel kültüre sahip olmaları büyük önem taşımaktadır. Bu yönüyle çalışma, yapay zekanın yalnızca teknik bir araç değil, aynı zamanda stratejik bir avantaj unsuru olduğuna dikkat çekmektedir.

Kaynakça

  • Abramson, N., Braverman, D., & Sebestyen, G. (1963). Pattern recognition and machine learning. IEEE Transactions on Information Theory, 9(4), 257–261.
  • Al-Imam, A., Motyka, M. A., & Jędrzejko, M. Z. (2020). Conflicting opinions in connection with digital superintelligence. IAES International Journal of Artificial Intelligence, 9(2), 336-348.
  • Alenezi, H. S. & Faisal, M. H. (2020). Utilizing crowdsourcing and machine learning in education: Literature review. Education and Information Technologies, 25(4), 2971-2986.
  • Ashritha, P. & Reddy, P. S. (2023). Impact of artificial intelligence on management decision-making. International Journal of Advances in Business and Management Research (IJABMR), 1(2), 10-18.
  • Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165-1195.
  • Barnabè, F. (2011). A “system dynamics‐based Balanced Scorecard” to support strategic decision making: Insights from a case study. International Journal of Productivity and Performance Management, 60(5), 446-473.
  • Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International journal of production research, 57(7), 2179-2202.
  • Basak, S., Agrawal, H., Jena, S., Gite, S., Bachute, M., Pradhan, B. & Assiri, M. (2023). Challenges and limitations in speech recognition technology: A critical review of speech signal processing algorithms, tools and systems. CMES-Computer Modeling in Engineering & Sciences, 135(2), 1053-1089.
  • Bean, R. (2019). Why fear of disruption is driving investment in AI. MIT Sloan Management Review. Boden, M. A. (1984). Impact of artificial intelligence. Futures, 16(1), 60-70.
  • Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International journal of information management, 57, 102225.
  • Bose, B. K. (1994). Expert system, fuzzy logic, and neural network applications in power electronics and motion control. Proceedings of the IEEE, 82(8), 13031323.
  • Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
  • Boyd, D. & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication and Society, 15(5), 662–679.
  • Brown, J. A., Ward, A., & Duane, J. N. (2024). Data privacy, ethics and strategic leadership. In Handbook of Research on Strategic Leadership in the Fourth Industrial Revolution, 330-347. Edward Elgar Publishing.
  • Bruno, Z. (2024). The impact of artificial intelligence on business operations Global Journal of Management and Business Research, 24(1), 1-9.
  • Burlacu, F. & Luta, G. D. G. (2023). The crucial importance of the European Union AI act as the world's first regulation on artificial intelligence. Journal of Information Systems & Operations Management, 17(2), 59-76
  • Büyükyılmaz, O. (2024). İşletme yönetim süreçleri ve yapay zekâ. in Gürkan, S. & Aydoğan, D. (Eds.) Işletme ve Yapay Zekâ, 1-18. Ekin Basım Yayın Dağıtım.
  • Cellan-Jones, R. (2014). Stephen Hawking warns artificial intelligence could end mankind. BBC news, 2(10), 2014.
  • Chang, W. Y. (2019). A data envelopment analysis on the performance of using artificial intelligence-based environmental management systems in the convention and exhibition industry. Ekoloji Dergisi, 107, 4897.
  • Chintala, S. (2024). Next-Gen BI: Leveraging AI for competitive advantage. International Journal of Science and Research (IJSR), 13(7), 972-977.
  • Cockburn, I. M., Henderson, R., & Stern, S. (2019). The impact of artificial intelligence on innovation: An exploratory analysis. (Ed.: A. Agrawal, J. Gans & A. Goldfarb), The Economics of Artificial Intelligence: An Agenda, 115-148. Chicago: The University of Chicago Press.
  • Craig, P. S., Goldstein, M., Rougier, J. C., & Seheult, A. H. (2001). Bayesian forecasting for complex systems using computer simulators. Journal of the American Statistical Association, 96(454), 717-729.
  • Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257.
  • D’Souza, S., Prema, K. V., & Balaji, S. (2020). Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discovery Today, 25(4), 748-756.
  • Davenport, T. H. & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  • Dhaliwal, J. S. & Tung, L. L. (2000). Using group support systems for developing a knowledge-based explanation facility. International Journal of Information Management, 20(2), 131-149.
  • 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.
  • Duangekanong, S. (2022). Applications of artificial intelligence for strategic management of organization. ABAC ODI Journal Vision. Action. Outcome, 9(2), 202-217.
  • Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734.
  • Edwards, J. S., Duan, Y., & Robins, P. (2000). An analysis of expert systems for business decision making at different levels and in different roles. European Journal of Information Systems, 9(1), 36–46.
  • Eisenhardt, K. M. (1999). Strategy as strategic decision making. MIT Sloan Management Review, 40(3), 65.
  • Eisenhardt, K. M. & Zbaracki, M. J. (1992). Strategic decision making. Strategic Management Journal, 13(2), 17-37.
  • Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications, 7(1), 1-9.
  • Ford, N. (1989). From information-management to knowledge-management - the role of rule induction and neural net machine learning techniques in knowledge generation. Journal of Information Science, 15(4–5), 299–304.
  • Foulquier, N., Redou, P., Le Gal, C., Rouvière, B., Pers, J. O., & Saraux, A. (2018). Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review. Human Vaccines & Immunotherapeutics, 14(11), 2553-2558.
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Toplam 97 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Örgütsel Davranış
Bölüm Research Articles
Yazarlar

Osman Kurter 0000-0001-7581-1316

Erken Görünüm Tarihi 25 Mart 2025
Yayımlanma Tarihi
Gönderilme Tarihi 3 Şubat 2025
Kabul Tarihi 24 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 22 Sayı: 2

Kaynak Göster

APA Kurter, O. (2025). The Use of Artificial Intelligence for Decision-Making Process for Strategic Management. OPUS Journal of Society Research, 22(2), 195-210. https://doi.org/10.26466/opusjsr.1632110