Journal of Ai ML DL
RESEARCH ARTICLE   (Open Access)

Integrating Business Analytics and Machine Learning for Immediate Decision Making Through a Unified Framework

Al Akhir1*,   Sonia Khan Papia2, Sonia Nashid3, Fahim Rahman4, Ariful Islam

+ Author Affiliations

Journal of Ai ML DL 1 (1) 1-8 https://doi.org/10.25163/ai.1110366

Submitted: 06 January 2025 Revised: 09 March 2025  Accepted: 12 March 2025  Published: 12 March 2025 


Abstract

Background: Businesses need to make quick decisions to stay competitive in today's digital transformation era. Business Analytics (BA) and Machine Learning (ML) have combined to create a revolutionary method for analyzing large datasets which enables immediate decision-making. The practical use of BA and ML integration remains insufficiently studied particularly across multiple industry sectors.

Methods: The research conducted a quantitative survey among 100 business professionals who represent the retail and finance sectors as well as manufacturing, healthcare and information technology industries. The researchers used a structured questionnaire to measure BA and ML integration levels and decision-making speed and operational efficiency and challenges faced.

Results: The results show that 86% of participants have implemented BA-ML systems in partial or complete form. Survey participants showed substantial enhancements in both forecasting accuracy and decision speed and operational efficiency. The main challenges to ML model implementation stem from poor interpretability (48% of respondents) and data privacy problems (42%) and insufficient skilled workforce (37%). This research establishes a real-time decision-making framework consisting of five stages which combines BA and ML to improve organizational performance.

Conclusion: The study demonstrates both business benefits and technical difficulties which organizations need to overcome for successful BA-ML integration. The framework sets a clear path for organizations to achieve operational efficiency and market agility in competitive business environments.

Keywords: Business Analytics, Machine Learning, Real-Time Decision-Making, Predictive Analytics, Data Strategy

References

Aljohani, A. (2023). Predictive analytics and machine learning for Real-Time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088

Alnefaie, A., Kang, K., & Sohaib, O. (2023). Attitudes and Usage Intentions Towards Artificial Intelligence (Ai) Assistants in E-Commerce: A Mixed-Methods Investigation.https://doi.org/10.2139/ssrn.4657676

Baviskar, D., Ahirrao, S., Potdar, V., & Kotecha, K. (2021). Efficient automated processing of the unstructured documents using artificial intelligence: A systematic literature review and future directions. IEEE Access, 9, 72894–72936. https://doi.org/10.1109/access.2021.3072900

Chae, B. (2013). A complexity theory approach to IT-enabled services (IESs) and service innovation: Business analytics as an illustration of IES. Decision Support Systems, 57, 1–10. https://doi.org/10.1016/j.dss.2013.07.005

Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2–12. https://doi.org/10.1080/2573234x.2018.1507324

Etemad, H. (2025). Challenges of smaller entrepreneurial enterprises aiming to generate higher values by adopting artificial intelligence (AI) and competing in the rapidly evolving AI industry. Journal of International Entrepreneurship. https://doi.org/10.1007/s10843-025-00385-w

Helo, P., & Hao, Y. (2021). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690

Inuwa, M. M., & Das, R. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber-attacks on IoT networks. Internet of Things, 26, 101162. https://doi.org/10.1016/j.iot.2024.101162

Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Kalishina, D. (2025). Deep learning architectures in Business Analytics: Unlocking hidden patterns in complex data streams. International Journal of Modern Achievement in Science, Engineering and Technology., 2(1), 133–145. https://doi.org/10.63053/ijset.64

Landset, S., Khoshgoftaar, T. M., Richter, A. N., & Hasanin, T. (2015). A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, 2(1). https://doi.org/10.1186/s40537-015-0032-1

Lee, J., Singh, J., Azamfar, M., & Pandhare, V. (2020). Industrial AI and predictive analytics for smart manufacturing systems. In Smart Manufacturing (pp. 213–244). https://doi.org/10.1016/b978-0-12-820027-8.00008-3

Madhumita, G., Diana, P. D., Pc, N., Kiran, P. B. N., Aggarwal, S., & Nargunde, A. S. (2024). AI-powered Performance Management: Driving Employee Success and Organizational Growth., 204–209. https://doi.org/10.1109/icrtcst61793.2024.10578371

Manta-Costa, A., Araújo, S. O., Peres, R. S., & Barata, J. (2024). Machine learning applications in Manufacturing - challenges, trends, and future directions. IEEE Open Journal of the Industrial Electronics Society, 5, 1085–1103. https://doi.org/10.1109/ojies.2024.3431240

Mohamed, G. (2025). Comparative Analysis of AI-Driven Decision Support Systems and Traditional Spreadsheets: Evaluating Accuracy and Consistency in Business Intelligence. . https://doi.org/10.2139/ssrn.5187060

Okwor, I. A., Hitch, G., Hakkim, S., Akbar, S., Sookhoo, D., & Kainesie, J. (2024). Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial intelligence (AI) and Machine-Learning (ML) adoption, Challenges, and opportunities. AI, 5(4), 1918–1941. https://doi.org/10.3390/ai5040095

Pandarathodiyil, A. K., Mani, S. A., Veerabhadrappa, S. K., Danaee, M., & Zamzuri, A. T. B. (2024b). Cross-cultural validation of Malay version of perceived professionalism among dental patients. BDJ Open, 10(1). https://doi.org/10.1038/s41405-024-00234-3

Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024). Business Intelligence through Artificial Intelligence: A Review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4831916

Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024a). Business intelligence and Business analytics with Artificial intelligence and machine learning: Trends, techniques, and opportunities. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4831920

Selvam, D. C., Devarajan, Y., & Raja, T. (2024). Exploring the potential of artificial intelligence in nuclear waste management: Applications, challenges, and future directions. Nuclear Engineering and Design, 431, 113719. https://doi.org/10.1016/j.nucengdes.2024.113719

Seo, C., Yoo, D., & Lee, Y. (2024b). Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization. Sustainability, 16(12), 5095. https://doi.org/10.3390/su16125095

Sharma, S., Gahlawat, V. K., Rahul, K., Mor, R. S., & Malik, M. (2021). Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics. Logistics, 5(4), 66. https://doi.org/10.3390/logistics5040066

Singh, R., & Bhanot, N. (2019). An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry. International Journal of Production Research, 58(8), 2454–2476. https://doi.org/10.1080/00207543.2019.1675915

Sun, Z., Sun, L., & Strang, K. (2016). Big Data Analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162–169. https://doi.org/10.1080/08874417.2016.1220239

Syed, S. (2024). Integrating predictive Analytics into Manufacturing Finance: A case study on cost control and Zero-Carbon goals in automotive production. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5017983

Tim, N. E., Babalola, N. A., Kossidze, N. a. A., & Goriparthi, N. S. V. (2025). Integrating advanced information analysis techniques to enhance operational efficiency in business administration practices. World Journal of Advanced Research and Reviews, 25(1), 1275–1293. https://doi.org/10.30574/wjarr.2025.25.1.0157

Vatankhah, S., Bamshad, V., Arici, H. E., & Duan, Y. (2024). Ethical implementation of artificial intelligence in the service industries. Service Industries Journal, 44(9–10), 661–685. https://doi.org/10.1080/02642069.2024.2359077

Wamba-Taguimdje, S., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/bpmj-10-2019-0411

Yamani, A. M., Yusuf, N., & Al-Shabrawi, H. A. (2025). The impact of Artificial Intelligence on Management Decision-Making: Analyzing the Role of Data Analytical Skills and Entrepreneurial Orientation. European Journal of Sustainable Development, 14(2), 221. https://doi.org/10.14207/ejsd.2025.v14n2p221


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