Transformasi Sistem Informasi Manajemen melalui Integrasi Kecerdasan Buatan: Tinjauan Konseptual dan Aplikatif
DOI:
https://doi.org/10.31004/joecy.v5i2.2154Keywords:
Sistem Informasi Manajemen, AI, Efisiensi, Strategi OrganisasiAbstract
Penelitian ini bertujuan untuk mengkaji transformasi Sistem Informasi Manajemen (SIM) melalui integrasi kecerdasan buatan (Artificial Intelligence/AI). Dengan pendekatan kualitatif deskriptif berbasis studi literatur, artikel ini menganalisis peran dan dampak AI terhadap efisiensi pengelolaan informasi dalam organisasi. Hasil penelitian menunjukkan bahwa SIM berbasis AI mampu meningkatkan efisiensi waktu dan biaya, mendeteksi pola tersembunyi, serta memberikan prediksi yang lebih akurat untuk mendukung pengambilan keputusan strategis. Selain itu, sistem menjadi lebih adaptif terhadap dinamika pasar dan mampu memberikan keunggulan kompetitif. Namun, tantangan seperti kesiapan infrastruktur, kualitas data, literasi digital sumber daya manusia, serta tata kelola etika informasi menjadi faktor penentu dalam keberhasilan implementasi. Dengan demikian, integrasi AI dalam SIM menuntut pendekatan yang tidak hanya teknis, tetapi juga struktural dan kultural, agar manfaatnya dapat dioptimalkan secara berkelanjutan.
References
Ahmad, M., Hussain, M., & Zafar, N. A. (2023). Artificial intelligence adoption: A review and future research directions. AI and Ethics, 3, 55–70.
Al-Garadi, M. A., Mohamed, M. R., Al-Kabsi, A. M., Duan, Y., & Deng, Y. (2021). Examining the role of AI in detecting online fraud: A comprehensive review. Information Systems Frontiers, 23(3), 597–614. https://doi.org/10.1007/s10796-020-10044-x.
Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. MIS Quarterly, 1(3), 17–32. https://doi.org/10.2307/249019.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Schafer, B. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5.
Holmström, J. (2021). From AI readiness to AI capability: How companies prepare for AI implementation. Information and Organization, 31(2), 100336. https://doi.org/10.1016/j.infoandorg.2021.100336.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415.
Laudon, K. C., & Laudon, J. P. (2020). Management Information Systems: Managing the Digital Firm (16th ed.). Pearson Education.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/Intelligence-Unleashed-Publication.pdf.
Mumford, E. (2000). A socio-technical approach to systems design. Requirements Engineering, 5(2), 125–133. https://doi.org/10.1007/PL00010345.
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.
Shollo, A., & Galliers, R. D. (2016). Towards an understanding of the role of business intelligence systems in organizational knowing. Information Systems Journal, 26(4), 339–367. https://doi.org/10.1111/isj.12071.
Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009.
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