Employing Artificial Intelligence in Management Information Systems to Improve Business Efficiency
DOI:
https://doi.org/10.51903/jmi.v3i2.30Keywords:
Management Information Systems, Operational Efficiency, Technology Integration, AI in ManagementAbstract
In today's competitive business environment, organizations are increasingly adopting Artificial Intelligence (AI) to enhance the efficiency of their Management Information Systems (MIS). The integration of AI into MIS has the potential to improve operational efficiency, decision-making processes, and customer satisfaction. This study aims to investigate the impact of AI on business performance by exploring its role in automating processes and providing data-driven insights. A systematic literature review (SLR) methodology was employed to analyze a range of studies on AI integration into MIS, focusing on improving business efficiency. The findings indicate that AI significantly reduces data processing time, increases decision-making accuracy, and improves customer satisfaction. Specifically, AI implementation led to a 66% reduction in data processing time, a 29% increase in decision-making accuracy, and a 20% reduction in operational costs. These results highlight AI's ability to optimize business processes and enhance overall productivity. However, the study also identified key challenges, including the need for high-quality data, specialized workforce training, and ethical considerations surrounding data privacy. This research contributes to both theoretical and practical knowledge by providing a comprehensive understanding of AI's role in MIS. It offers strategic recommendations for organizations aiming to leverage AI to drive operational efficiency and maintain competitive advantage. Future research should focus on exploring synergies between AI and emerging technologies such as big data and the Internet of Things (IoT) to further improve business outcomes.
References
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Applied Sciences, 13(12), 7082. https://doi.org/10.3390/APP13127082
Ali, M., Dewan, A., Sahu, A. K., & Taye, M. M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91. https://doi.org/10.3390/COMPUTERS12050091
Alsafadi, Y., & Altahat, S. (2021). Human Resource Management Practices and Employee Performance: The Role of Job Satisfaction. The Journal of Asian Finance, Economics and Business, 8(1), 519–529. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.519
Amankwah-Amoah, J., & Lu, Y. (2024). Harnessing AI for Business Development: a Review of Drivers and Challenges in Africa. Production Planning & Control, 35(13), 1551–1560. https://doi.org/10.1080/09537287.2022.2069049
Amaya, J., & Holweg, M. (2024). Using Algorithms to Improve Knowledge Work. Journal of Operations Management, 70(3), 482–513. https://doi.org/10.1002/JOOM.1296
Antony, P. J., Kannan, R., & Professor, A. (2024). Revolutionizing the Tourism Industry through Artificial Intelligence: A Comprehensive Review of AI Integration, Impact on Customer Experience, Operational Efficiency, and Future Trends. International Journal for Multidimensional Research Perspectives (IJMRP, ISSN(1), 2584–2613.
Bhawna Sharma, S. M. R. R. K. (2024). "The Impact of Change Management Practices on Employee Resistance and Acceptance”: A Case Study Analysis. Journal of Informatics Education and Research, 4(2). https://doi.org/10.52783/JIER.V4I2.948
Chen, J. S., Le, T. T. Y., & Florence, D. (2021). Usability and Responsiveness of Artificial Intelligence Chatbot on Online Customer Experience in E-Retailing. International Journal of Retail and Distribution Management, 49(11), 1512–1531. https://doi.org/10.1108/IJRDM-08-2020-0312/FULL/XML
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework. Human Resource Management Review, 33(1), 100899. https://doi.org/10.1016/J.HRMR.2022.100899
Dauvergne, P. (2022). Is Artificial Intelligence Greening Global Supply Chains? Exposing the Political Economy of Environmental Costs. Review of International Political Economy, 29(3), 696–718. https://doi.org/10.1080/09692290.2020.1814381
Drolet, M.-J., Rose-Derouin, E., Leblanc, J.-C., Ruest, · Mélanie, Williams-Jones, B., & Drolet, M.-J. (2023). Ethical Issues in Research: Perceptions of Researchers, Research Ethics Board Members and Research Ethics Experts. Journal of Academic Ethics, 21(2), 269–292. https://doi.org/10.1007/s10805-022-09455-3
Eboigbe, E. O., Farayola, O. A., Olatoye, F. O., Nnabugwu, O. C., & Daraojimba, C. (2023). Business Intelligence Transformation through AI and Data Analytics. Engineering Science & Technology Journal, 4(5), 285–307. https://doi.org/10.51594/ESTJ.V4I5.616
Farida, I., & Setiawan, D. (2022). Business Strategies and Competitive Advantage: The Role of Performance and Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 163. https://doi.org/10.3390/JOITMC8030163
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., Singh, M., Mehta, H., Ghosh, S. K., Baker, T., Parlikad, A. K., Lutfiyya, H., Kanhere, S. S., Sakellariou, R., Dustdar, S., … Uhlig, S. (2022). AI for Next Generation Computing: Emerging Trends and Future Directions. Internet of Things, 19(1), 100514. https://doi.org/10.1016/J.IOT.2022.100514
Goto, M. (2022). Accepting the Future as Ever-Changing: Professionals’ Sensemaking about Artificial Intelligence. Journal of Professions and Organization, 9(1), 77–99. https://doi.org/10.1093/jpo/joab022
Hernita, H., Surya, B., Perwira, I., Abubakar, H., & Idris, M. (2021). Economic Business Sustainability and Strengthening Human Resource Capacity Based on Increasing the Productivity of Small and Medium Enterprises (SMEs) in Makassar City, Indonesia. Sustainability, 13(6), 3177. https://doi.org/10.3390/SU13063177
Jacobides, M. G., Brusoni, S., & Candelon, F. (2021). The Evolutionary Dynamics of the Artificial Intelligence Ecosystem. Strategy Science, 6(4), 412–435. https://doi.org/10.1287/stsc.2021.0148
Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A., & Guizani, M. (2022). The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions. IEEE Internet of Things Journal, 9(15), 12861–12885. https://doi.org/10.1109/JIOT.2021.3139827
Kar, S., Kar, A. K., & Gupta, M. P. (2021). Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective. Intelligent Systems in Accounting, Finance and Management, 28(4), 217–238. https://doi.org/10.1002/ISAF.1503
Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial Intelligence and the Changing Sources of Competitive Advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/10.1002/SMJ.3387
Li, H. (2021). Optimization of the Enterprise Human Resource Management Information System Based on the Internet of Things. Complexity, 2021(1), 5592850. https://doi.org/10.1155/2021/5592850
Liladhar Rane, N., Achari, A., & Choudhary, S. P. (2023). Enhancing Customer Loyalty through Quality of Service: Effective Strategies to Improve Customer Satisfaction, Experience, Relationship, and Engagement. International Research Journal of Modernization in Engineering Technology and Science, 5(5), 427–452. https://doi.org/10.56726/IRJMETS38104
Liladhar Rane, N., Choudhary, S. P., & Rane, J. (2024). Acceptance of Artificial Intelligence Technologies in Business Management, Finance, and E-Commerce: Factors, Challenges, and Strategies. Studies in Economics and Business Relations, 5(2), 23–44. https://doi.org/10.48185/SEBR.V5I2.1333
Melles, M., Albayrak, A., & Goossens, R. (2021). Innovating Health Care: Key Characteristics of Human-Centered Design. International Journal for Quality in Health Care, 33(1), 37–44. https://doi.org/10.1093/INTQHC/MZAA127
Napitupulu, I. H. (2023). Internal Control, Manager’s Competency, Management Accounting Information Systems and Good Corporate Governance: Evidence from Rural Banks in Indonesia. Sage Journals, 24(3), 563–585. https://doi.org/10.1177/0972150920919845
Oeda, S., Seko, Y., Hayashi, H., Arai, T., Iwaki, M., Yoneda, M., Shima, T., Notsumata, K., Ikegami, T., Fujii, H., Toyoda, H., Miura, K., Morishita, A., Kawata, K., Tomita, K., Kawanaka, M., Isoda, H., Yamaguchi, K., Fukushima, H., … Takahashi, H. (2023). Validation of the utility of Agile scores to identify advanced fibrosis and cirrhosis in Japanese patients with nonalcoholic fatty liver disease. Hepatology Research, 53(6), 489–496. https://doi.org/10.1111/HEPR.13890
Olan, F., Ogiemwonyi Arakpogun, E., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial Intelligence and Knowledge Sharing: Contributing Factors to Organizational Performance. Journal of Business Research, 145(1), 605–615. https://doi.org/10.1016/J.JBUSRES.2022.03.008
Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., & Deepak, S. (2022). Future of Business Culture: An Artificial Intelligence-Driven Digital Framework for Organization Decision-Making Process. Complexity, 2022(1), 7796507. https://doi.org/10.1155/2022/7796507
Raval, S. J., & Kant, R. (2017). Study on Lean Six Sigma frameworks: a critical literature review. International Journal of Lean Six Sigma, 8(3), 275–334. https://doi.org/10.1108/IJLSS-02-2016-0003/FULL/XML
Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial Intelligence Supported Reduction of Employees’ Workload to Increase the Company’s Performance in Today’s VUCA Environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/SU15065019
Sarker, I. H. (2022). AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science, 3(2), 1–20. https://doi.org/10.1007/S42979-022-01043-X
Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2022). Implementing Challenges of Artificial Intelligence: Evidence from Public Manufacturing Sector of An Emerging Economy. Government Information Quarterly, 39(4), 101624. https://doi.org/10.1016/J.GIQ.2021.101624
Taeihagh, A. (2021). Governance of Artificial Intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928377
Touretzky, D., Gardner-McCune, C., & Seehorn, D. (2023). Machine Learning and the Five Big Ideas in AI. International Journal of Artificial Intelligence in Education, 33(2), 233–266. https://doi.org/10.1007/S40593-022-00314-1/METRICS
Tschang, F. T., & Almirall, E. (2021). Artificial Intelligence as Augmenting Automation: Implications for Employment. Academy of Management Perspectives, 35(4), 642–659. https://doi.org/10.5465/AMP.2019.0062
Um, T. W., Kim, J., Lim, S., & Lee, G. M. (2022). Trust Management for Artificial Intelligence: A Standardization Perspective. Applied Sciences, 12(12), 6022. https://doi.org/10.3390/APP12126022
Wahid, S. A. Al, Mohammad, N., Islam, R., Faisal, Md. H., Rana, Md. S., Wahid, S. A. Al, Mohammad, N., Islam, R., Faisal, Md. H., & Rana, Md. S. (2024). Evaluation of Information Technology Implementation for Business Goal Improvement under Process Functionality in Economic Development. Journal of Data Analysis and Information Processing, 12(2), 304–317. https://doi.org/10.4236/JDAIP.2024.122017
Wong, Y. C., Lin, Y. B., & Chen, M. S. (2024). Artificial Intelligence in Computer Science. International Journal of Electrical Engineering, 2(2), 1–21. https://doi.org/10.5281/zenodo.10937515
Yang, M., Fu, M., & Zhang, Z. (2021). The Adoption of Digital Technologies in Supply Chains: Drivers, process and Impact. Technological Forecasting and Social Change, 169, 120795. https://doi.org/10.1016/J.TECHFORE.2021.120795
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Management and Informatics
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.