Peran Kecerdasan Buatan Untuk Meningkatkan Kualitas Keputusan Strategis Dan Manajemen Berbasis Data
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Abstract
Era bisnis modern ditandai dengan kompleksitas dan volume data yang terus meningkat, menuntut organisasi untuk membuat keputusan strategis yang cepat dan akurat. Namun, pendekatan manajerial konvensional seringkali tidak mampu mengolah informasi secara efektif, yang berisiko menghasilkan keputusan suboptimal. Penelitian ini bertujuan untuk menganalisis peran Kecerdasan Buatan (Artificial Intelligence/AI) sebagai solusi untuk meningkatkan kualitas keputusan strategis dan manajemen berbasis data. Dengan memanfaatkan kemampuan seperti machine learning dan predictive analytics, AI dapat menganalisis dataset besar untuk mengidentifikasi pola, meramalkan tren, dan memberikan rekomendasi yang berbasis bukti. Temuan menunjukkan bahwa penerapan AI secara signifikan meningkatkan akurasi dan kecepatan pengambilan keputusan, serta memungkinkan transisi menuju manajemen yang benar-benar berbasis data. Kesimpulannya, AI bukan lagi sekadar alat teknologi, melainkan aset strategis yang fundamental bagi organisasi untuk mencapai keunggulan kompetitif di tengah dinamika pasar yang tidak pasti.
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References
Bean, R., & Davenport, T. H. (2019). The strategic importance of data-driven decision making. MIT Sloan Management Review, 60(4), 1-5.
Buhalis, D., & Sinarta, Y. (2023). Real-time branding and social media: The role of artificial intelligence in tourism. Journal of Business Research, 159, 113657. https://doi.org/10.1016/j.jbusres.2023.113657
Cao, L., & Duan, Y. (2021). The impact of AI capability on business model innovation: The mediating role of data-driven decision making. Journal of Strategic Information Systems, 30(3), 101688. https://doi.org/10.1016/j.jsis.2021.101688
Côrte-Real, N., Oliveira, T., & Ruivo, P. (2020). Assessing the impact of data-driven capabilities on firm performance. Journal of Strategic Information Systems, 29(4), 101688. https://doi.org/10.1016/j.jsis.2020.101688
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2021). How big data analytics and artificial intelligence can enhance supply chain performance: A comparative case study. International Journal of Production Economics, 236, 108122. https://doi.org/10.1016/j.ijpe.2021.108122
Glikson, E., & Wolff, F. (2022). AI in the workplace: A review of the ethical implications and challenges of AI-based human resource management. Journal of Business Ethics, 181(3), 687-707. https://doi.org/10.1007/s10551-021-04930-w
Gupta, S., & George, J. F. (2020). Toward developing a framework for examining the role of big data analytics in firm performance. Journal of Management Information Systems, 37(1), 197-236. https://doi.org/10.1080/07421222.2020.1717274
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00753-2
Jiang, K., Lyytinen, K., & Yoo, Y. (2021). AI capabilities and firm performance: The role of data-driven decision making and data-driven culture. MIS Quarterly Executive, 20(2), 89-107.
Kar, A. K., Chakraborty, S., & Gupta, P. (2022). Artificial intelligence for strategic decision making: A review and research agenda. International Journal of Information Management, 66, 102525. https://doi.org/10.1016/j.ijinfomgt.2022.102525
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2021). FinTech and AI: A review of recent advances and research trends. International Journal of Information Management, 61, 102399. https://doi.org/10.1016/j.ijinfomgt.2021.102399
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155. https://doi.org/10.1177/0008125619859317
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2022). Fair, transparent, and accountable AI for business. Harvard Data Science Review, 4(2). https://doi.org/10.1162/99608f92.9c1b1e32
Martin, K. (2019). Ethical implications and risks of artificial intelligence. Journal of Business Ethics, 160(4), 823-837. https://doi.org/10.1007/s10551-019-04165-w
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2020). Big data analytics capabilities and firm performance: The mediating role of dynamic capabilities. Journal of Business Research, 115, 111-121. https://doi.org/10.1016/j.jbusres.2020.01.014
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and the strategic management of firms. Academy of Management Perspectives, 35(3), 335-351. https://doi.org/10.5465/amp.2019.0117
Sharma, S., Singh, S., & Singh, R. (2023). Artificial intelligence and business model innovation: A systematic literature review and future research agenda. Technological Forecasting and Social Change, 191, 122447. https://doi.org/10.1016/j.techfore.2023.122447
Tambe, P., Cappelli, P., & Yadav, N. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42. https://doi.org/10.1177/0008125619858743
Vakkuri, V., Kemell, K. B., & Kaski, T. (2023). Algorithmic governance for the public good: A systematic literature review. Government Information Quarterly, 40(2), 101828. https://doi.org/10.1016/j.giq.2022.101828
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003