Integrating AI Technologies into Business Intelligence Systems for Smarter Supply Chains
DOI:
https://doi.org/10.63544/ijss.v5i3.295Keywords:
Artificial Intelligence, Business Intelligence, Supply Chain Management, AI Integration, Predictive Analytics, Decision-Making, Digital Transformation, Supply Chain PerformanceAbstract
This study examines the integration of artificial intelligence (AI) into business intelligence (BI) systems and its influence on supply chain performance. It focuses on awareness, adoption, benefits, challenges, and user satisfaction related to AI-enabled BI systems. The research adopts a quantitative approach using a structured questionnaire to collect primary data from practitioners across manufacturing, retail, logistics, healthcare, and energy sectors. A total of 318 valid responses were analyzed using descriptive statistics and reliability testing. Key variables, including AI adoption, supply chain performance, strategic benefits, and implementation challenges, were measured through a Likert scale. Cronbach’s Alpha values ranged from 0.812 to 0.901, confirming strong reliability of the research instrument. The findings reveal that most respondents possessed high or very high familiarity with AI in BI systems, indicating considerable organizational awareness. In terms of adoption, many organizations reported partial implementation, while others had fully integrated AI into their BI processes, suggesting a growing trend toward adoption. Machine learning and predictive analytics emerged as the most commonly used AI technologies. AI integration was found to positively affect real-time decision-making, forecasting, inventory management, and risk identification, demonstrating its practical value in enhancing supply chain efficiency. However, high implementation costs and poor data quality were identified as major barriers to successful integration. Despite these challenges, user satisfaction was largely positive, with most respondents expressing confidence in the reliability and usefulness of AI-generated insights. The study offers valuable implications for managers, policymakers, and organizations by emphasizing the importance of effective data management, workforce training, infrastructure, and strategic planning to maximize AI benefits in BI systems and create smarter, more resilient supply chains.
References
Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024). Enhancing supply chain resilience through artificial intelligence: Analyzing problem-solving approaches in logistics management. International Journal of Management & Entrepreneurship Research, 5(12), 3248–3265. https://doi.org/10.51594/ijmer.v5i12.1156
Charles, V., Emrouznejad, A., & Gherman, T. (2023). A critical analysis of the integration of blockchain and artificial intelligence for supply chain. Annals of Operations Research, 327(1), 7–47. https://doi.org/10.1007/s10479-023-05169-w
Chowdhury, R. H. (2024). Blockchain and AI: Driving the future of data security and business intelligence. World Journal of Advanced Research and Reviews, 23(1), 2559–2570. https://doi.org/10.30574/wjarr.2024.23.1.2124
Hasan, M. A., Mazumder, M. T. R., Motari, M. C., Shourov, M. S. H., & Sarkar, M. (2026). AI and business intelligence integration for improved efficiency and reporting accuracy in small US financial institutions. Journal of Fintech, Business, and Development, 3(1), 1–25.
Hasan, M. A., Mazumder, M. T. R., Motari, M. C., Shourov, M. S. H., & Sarkar, M. (2025). AI-powered fraud detection: Strengthening risk monitoring with business intelligence in US financial institutions. Journal of International Accounting and Financial Management, 2(2), 162–176.
Hasan, S. T. (2025). Machine learning models for forecasting employee demand in healthcare HR. Journal of Engineering and Computational Intelligence Review, 3(2), 159–172.
Majumder, C., Choain, A. H. K., Nasir, M. A., & Sultana, N. (2026). Developing hybrid post-quantum encryption frameworks for US databases integrating financial, governmental, and critical infrastructure protections. Journal of International Accounting and Financial Management, 3(1), 1–18.
Marques, R. P., & Santos, D. (2025). Integrating business intelligence and operations research for sustainable supply chain systems: A systematic review. Systems, 13(12), 1111. https://doi.org/10.3390/systems13121111
Mirza, M. R., Raja, S., & Nizami, A. N. (2026). AI-Based Adaptive Learning Systems and their role in Enhancing Student Academic Performance. Inverge Journal of Social Sciences, 5(1), 227–240. https://doi.org/10.63544/ijss.v5i1.233
Mohammed, I. A., Sofia, R., Radhakrishnan, G. V., Jha, S., & Al Said, N. (2025). The role of artificial intelligence in enhancing business efficiency and supply chain management. Journal of Information Systems Engineering and Management, 10(10s), 509–518. https://doi.org/10.52783/jisem.v10i10S.1368
Mohsen, B. M. (2023). Impact of artificial intelligence on supply chain management performance. Journal of Service Science and Management, 16(1), 44–58. https://doi.org/10.4236/jssm.2023.161004
Naureen, R., & Mohammad , R. F. (2026). Becoming a Ph.D. Scholar: A Study of Doctoral Students’ Experiences and Challenges. Inverge Journal of Social Sciences, 5(2), 267–280. https://doi.org/10.63544/ijss.v5i2.265
Noor, S. R., & Alim, I. (2023). Blockchain-integrated ERP platforms for ensuring security in US financial supply chains. Journal of Business Insight and Innovation, 2(2), 107–119.
Nweje, U., & Taiwo, M. (2025). Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 14(1), 230–250. https://doi.org/10.30574/ijsra.2025.14.1.0185
Radhakrishnan, G. V., Gabhane, M., Jha, S., Al Said, N., & Murthy, B. S. R. (2025). AI and IoT in supply chains: Creating intelligent and autonomous business operations. Journal of Information Systems Engineering and Management, 10, 625–632. https://doi.org/10.52783/jisem.v10i1S.1402
Riad, M., Naimi, M., & Okar, C. (2024). Enhancing supply chain resilience through artificial intelligence: Developing a comprehensive conceptual framework for AI implementation and supply chain optimization. Logistics, 8(4), 111. https://doi.org/10.3390/logistics8040111
Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., & Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: Mapping the territory. International Journal of Production Research, 60(24), 7527–7550. https://doi.org/10.1080/00207543.2022.2058438
Shorif, M. N., & Islam, M. J. (2024). AI-powered business analytics for smart manufacturing and supply chain resilience. Review of Applied Science and Technology, 3(1), 183–220.
Sultana, N., Nasir, M. A., Majumder, C., & Choain, A. H. K. (2024). Exploring AI-driven approaches for safeguarding sensitive ERP, HR, and defense data within US organizations. Journal of Business Insight and Innovation, 3(2), 43–59.
Ullah, A., & Khan, S. D. (2024). Impact of sound decision-making on small and medium businesses in Pakistan. International Journal of Asian Business and Management, 3(2), 177–192. https://doi.org/10.55927/ijabm.v3i2.8443
Wu, H., Li, G., & Zheng, H. (2025). How does digital intelligence technology enhance supply chain resilience? Sustainable framework and agenda. Annals of Operations Research, 355(1), 901–923. https://doi.org/10.1007/s10479-024-06189-0
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Warda Ghafoor, Muhammad Wasim, Nauman Hassan, Muzammil Shafi

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The work is concurrently licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, which permits others to share the work with an acknowledgement of the authorship and the work's original publication in this journal, while the authors retain copyright and grant the journal the right of first publication.