AI-POWERED MACHINE LEARNING ANALYTICS FOR ENHANCING STRATEGIC BUSINESS DECISION-MAKING IN US ENTERPRISES
DOI:
https://doi.org/10.48047/mryxab80Keywords:
AI analytics, Machine learning, Strategic decision making, Explainable AI, Enterprise business analyticsAbstract
The obliging processing of them becomes AI/ML analytics, and U.S. businesses are increasingly relying on it to make decisions in a data-rich and competitive environment. While previous research has focused mostly on technical aspects of model performance, there is scant empirical evidence as to how ML analytics can be transformed into interpretable and actionable strategic insights for managers. This gap is addressed in this paper by presenting an AI-driven machine learning analytics framework that is directed towards improving strategic decision-making at the enterprise level. A quantitative empirical design applied through an enterprise workforce analytics data set containing 500 observations and prominent demographic, organizational structural, and behavioral measures. Turnover was chosen as the strategic dependent variable because it pertains to stability in workforce and long-term performance of an organization. Pre-model development data pre-processing and exploratory analysis were carried out. Two machine learning methods were used: logistic regression, chosen as an interpretable baseline model, and a non-linear ensemble model (Random Forest). The model was assessed for performance on the basis of accuracy, precision Recall F1 score, ROC–AUC, and confusion matrix analysis. Furthermore, explainable analytics tools such as feature relevance ranking and partial dependence plots were used to improve interpretability and managerial actionability. The findings demonstrate that in comparison to logistic regression, the Random Forest model performs significantly better with an ROC–AUC value of 0.94 against 0.86. Compensation, organizational tenure, and age joined up in the output as the most contributing factors for predicting attrition, while workload intensity also presented a relevant effect. The explainability analysis detected non-linear threshold effects, making it useful for strategic interpretation beyond prediction performance. Together, the results show that AI-based machine learning analytics can give robust, interpretable, and strategically useful insights. The concept proposal illustrates a promising explanation-based ML as a useful decision-support technology for improved business strategy making of U.S. companies.
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References
Bharadwaj, A., et al., Digital business strategy: toward a next generation of insights. MIS quarterly, 2013: p. 471-482.
Masud, S., et al., The revolution of AI in enhancing infrastructure and facilities management. CDF, 54 (4), 5605–5624. 2025.
Ulrich, D. and M.F. Wiersema, Gaining strategic and organizational capability in a turbulent business environment. Academy of Management Perspectives, 1989. 3(2): p. 115-122.
Rahman, M.B., et al., Appraising the historical and projected spatiotemporal changes in the heat index in Bangladesh. Theoretical and Applied climatology, 2021. 146(1-2): p. 125.
Md Habibul Arif, I.R., Md Abdul Alim, Md Reduanur Rahman, Md Shakhawat Hossen, Mamunur Rahman, AI-Powered DDoS Detection and Mitigation: Developing Adaptive Machine Learning Frameworks to Predict and Block Next-Generation Attacks. Journal of Advanced Research in Applied Sciences and Engineering Technology, 2025. 56(231–243).
Uddin, M.K., et al., A greener approach to cotton pre-treatment using bentonite clay. The Journal of The Textile Institute, 2022. 113(9): p. 1775-1784.
Farhan, M., Business Adaptation Strategies in an Era of Uncertainty: The Key to Success in Facing Economic and Financial Fluctuations. Journal Of Multi Currency, 2025. 1(1): p. 14-27.
Masud, S.B., et al., AI-Driven Predictive Maintenance in Infrastructure and Facilities Management. 2025.
Begum, S., Artificial Intelligence and Economic Resilience: A Review of Predictive Financial Modelling for Post-Pandemic Recovery in the United States SME Sector. International Journal of Innovative Science and Research Technology, 2025. 10(7): p. 3620-3627.
TOHFA, N.A., et al., Machine learning–enabled anomaly detection for environmental risk management in banking. 2025.
Noor, N., et al., A Big Data–Driven Optimization Framework for Enterprise Financial Management: Enhancing Predictive Decision-Making, Risk Control, and Computational Efficiency. The Asian Bulletin of Big Data Management, 2025. 5(4): p. 190-217.
S A Mohaiminul Islam Nusrat Yasmin Nadia, M.S.B., Mohammed Majid Bakhsh, Ankur Sarkar, AI-Driven Test Data Management for Large-Scale BI Applications. International Journal of Innovative Science and Research Technology (IJISRT), 2025/2/15. 10(1): p. 11.
Adwani, A., Predictive analytics for business strategy: Leveraging machine learning for competitive advantage. Available at SSRN 5356744, 2025.
Prince, N.U., et al. Deep Transfer Learning Approach to Detect Dragon Tree Disease. in 2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS). 2024. IEEE.
Vudugula, S., et al., Integrating artificial intelligence in strategic business decision-making: A systematic review of predictive models. International Journal of Scientific Interdisciplinary Research, 2023. 4(1): p. 01-26.
Roy, K.K., et al., Leveraging artificial intelligence for strategic decision-making in healthcare organizations: a business it perspective. The American Journal of Applied Sciences, 2025. 7(8): p. 74-93.
Theodorakopoulos, L., A. Theodoropoulou, and C. Halkiopoulos, Cognitive bias mitigation in executive decision-making: A data-driven approach integrating big data analytics, AI, and explainable systems. Electronics, 2025. 14(19): p. 3930.
Shaker, A.S.M., et al., TACS-Net: Temporal-Aware Customer Segmentation Network. IEEE Open Journal of the Computer Society, 2025.
Ahmed, M.S., et al., Ai-driven predictive operations management: a business science framework for dynamic hospital resource optimization and clinical workflow efficiency. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 2025. 10(8): p. 5.
Alzlfawi, A., et al., Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis. Scientific Reports, 2025. 15(1): p. 42982.
Saeed, M., Data-Driven Healthcare: The Role of Business Intelligence Tools in Optimizing Clinical and Operational Performance. The American Journal of Applied Sciences, 2025. 7(8): p. 50-73.
Rane, N.L., et al., Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review. Partners Universal International Innovation Journal, 2024. 2(3): p. 147-171.
Sobuz, M.H.R., et al., An explainable machine learning model for encompassing the mechanical strength of polymer-modified concrete. Asian Journal of Civil Engineering, 2025. 26(2): p. 931-954.
Hoque, A., et al., Cloud Computing in Banking Flexibility and Scalability for Financial Institute. Well Testing Journal, 2025. 34(S2): p. 165-184.
Kabbo, M.K.I., et al., Analyzing the influence of chemical components of incinerated bottom ash on compressive strength of magnesium phosphate cement using machine learning analysis. Geoenvironmental Disasters, 2025. 12(1): p. 35.
Tursunalieva, A., et al., Making sense of machine learning: a review of interpretation techniques and their applications. Applied Sciences, 2024. 14(2): p. 496.
Rahman, M., et al., Quantum Machine Learning Integration: A Novel Approach to Business and Economic Data Analysis. 2021.
Mir, M.N.H., et al., ABMF-Net: An Attentive Bayesian Multi-Stage Deep Learning Model for Robust Forecasting of Electricity Price and Demand. IEEE Open Journal of the Computer Society, 2025.
Begum, S., et al., AttenGene: A deep learning model for gene selection in PDAC classification using autoencoder and attention mechanism for precision oncology. Well Testing Journal, 2025. 34(S3): p. 705-726.
Zamil, M.Z.H., et al. Stroke Prediction on Healthcare Data Using SMOTE and Explainable Machine Learning. in 2025 13th International Symposium on Digital Forensics and Security (ISDFS). 2025. IEEE.
Hossen, M.S., et al., Parallel Deep Learning for Cybersecurity-Oriented Multi-Platform Social Media Bot Detection. Frontiers in Computer Science and Artificial Intelligence, 2026. 5(2): p. 13-20.
Sazzadul, I., et al., Designing a Real-Time Human-AI decision support framework for US Healthcare providers using big data analytics. Journal of Computer Science and Technology Studies, 2025. 7(8): p. 716-723.
Hoque, A., et al., Reshaping Fintech: Unveiling Recent Developments on Fintech Integration. Well Testing Journal, 2025. 34(S3): p. 121-148.
Bakhsh, M.M., G.T. Alam, and N.Y. Nadia, Adapting Agile Methodologies to Incorporate Digital Twins in Sprint Planning, Backlog Refinement, and QA Validation. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2025. 4(2): p. 67-79.
Siyam, O.F., et al., Interpretable Deep Learning for Symptom-Based Lung Cancer Prediction Using a 1D CNN Framework.
Islam, I., et al., The Future of Banking Fraud Detection: Emerging AI Technologies and Trends. Well Testing Journal, 2025. 34(S3): p. 102-120.
Islam, M.T., et al. FinGraphRisk: A Graph Neural Network-Based Framework for Sentiment-Driven Financial Risk Assessment. in 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN). 2025. IEEE.
Nayak, B., S.S. Bhattacharyya, and B. Krishnamoorthy, Integrating the dialectic perspectives of resource-based view and industrial organization theory for competitive advantage–a review and research agenda. Journal of Business & Industrial Marketing, 2023. 38(3): p. 656-679.
Khatun, M. and M.S. Oyshi, Advanced Machine Learning Techniques for Cybersecurity: Enhancing Threat Detection in US Firms. Journal of Computer Science and Technology Studies, 2025. 7(2): p. 305-315.
Hitt, M.A., K. Xu, and C.M. Carnes, Resource based theory in operations management research. Journal of operations management, 2016. 41: p. 77-94.
Alim, M.A., et al., Enhancing fraud detection and security in banking and E-Commerce with AI-powered identity verification systems. 2020.
Greitemeyer, J., Business Intelligence: An Analysis based on the Resource-based View. 2002: diplom. de.
Mandal, N.C., et al., A Case Report of Middle Aortic Syndrome: A Rare Vascular Disorder. Cardiovascular Journal, 2013. 6(1): p. 60-62.
Narne, S., et al., AI-driven decision support systems in management: enhancing strategic planning and execution. International journal on recent and innovation trends in computing and communication, 2024. 12(1): p. 268-276.
Uddin, M.M., et al. Interpretable Alzheimer's Disease Diagnosis Via CNNs and MRI: an Explainable AI Approach. in 2025 4th International Conference on Electronics Representation and Algorithm (ICERA). 2025. IEEE.
Hoque, A., et al., AI and Machine learning in Banking: Driving Efficiency and Innovation. Well Testing Journal, 2025. 34(S3): p. 80-101.
Sufia Zareen, N.A.T., Md Abdul Alim, Md Reduanur Rahman, Md Habibul Arif, Iftekhar Rasul Md Shakhawat Hossen, To Secure the Digital Age: The application of Quantum Computing, and Ethical Frameworks. 2023. 8(6).
Basak, S., M.D.H. Gazi, and S. Mazharul Hoque Chowdhury. A Review Paper on Comparison of different algorithm used in Text Summarization. in International Conference on Intelligent Data Communication Technologies and Internet of Things. 2019. Springer.
Brynjolfsson, E., D. Rock, and C. Syverson, Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. 2017, National Bureau of Economic Research.
Bekaroğlu, C., ARTIFICIAL INTELLIGENCE AND EMPLOYMENT. AI RENAISSANCE-Transforming Industries and Decision-Making in the 21st Century, 2023: p. 29.
Arif, M.H., et al., AI-Driven Risk Assessment in National Security Projects: Investigating machine learning models to predict and mitigate risks in defense and critical infrastructure projects. Journal of Computer Science and Technology Studies, 2025. 7(2): p. 71-85.
Samia Ara Chowdhury, A.H., Md Sajedul Karim Chy, Mohammad Delowar Hossain Gazi, Next Generation Financial Security: Leveraging AI for Fraud Detection, Compliance and Adaptive Risk Management. Well Testing Journal, 2025/7/25. 34(S3): p. 61-79.
Sobuz, M.H.R., et al., Microstructural assessment and supervised machine learning-aided modeling to explore the potential of quartz powder as an alternate binding material in concrete. Case Studies in Construction Materials, 2025. 22: p. e04568.
Masud, S.B., et al., The revolution of AI in enhancing infrastructure and facilities management. Cuestiones de Fisioterapia, 2025. 54(4): p. 5605-5624.
Alam, G.T., et al., Predictive Analytics in QA Automation:: Redefining Defect Prevention for US Enterprises. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2025. 4(2): p. 55-66.
Sozib, H.M., et al., Cloud Computing in Business: Leveraging SaaS, IaaS, and PaaS for Growth. Journal of Applied Research: p. 38.
Md Abdul Alim, M.R.R., Md Shakhawat Hossen, Mamunur Rahman, Md Habibul Arif, Iftekhar Rasul, Zero-Trust Security Models in Multi-Cloud Environments: Scalability, Challenges, and Implementation Strategies. Journal of Advanced Research in Applied Sciences and Engineering Technology,, 2025. 56(282–29).
Islam, M.T., et al., LaplaceSalesNet: A Neural Laplace-Transformer Framework for Continuous-Time Sales Forecasting. IEEE Open Journal of the Computer Society, 2025.
Bellal, R.B., et al., The Economic Impact of AI-Driven Carbon Emission Reduction Strategies in Large-Scale Industrial and Office Settings. Cuestiones de Fisioterapia, 2023. 52(3): p. 717-736.
Abubakkar, M., et al. Explainable suicide risk prediction with DeepFusion: A hybrid intelligence approach. in 2025 4th International Conference on Electronics Representation and Algorithm (ICERA). 2025. IEEE.
Hossain, M.I., et al., Zero-ETL Analytics: Transforming operational data into actionable insights. 2025.
Sizan, M.M.H., Machine Learning-Based Unsupervised Ensemble Approach for Detecting New Money Laundering Typologies in Transaction Graphs. International Journal of Applied Mathematics, 2025. 38(2s): p. 351-374.
Biswas, A.K., et al., A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment. IEEE Access, 2025.
Bari, M., et al., Toward an Adaptive AI/ML-Based QA Framework with HRM Integration for Inclusive and Secure Healthcare Solutions in Edge Environments. Journal of Neonatal Surgery, 2025. 14(32S): p. 7612-7620.
Pankaj Kumar Sarker, S.C.S., Santanu Palit, Abdullah Al Naseeh Chowdhury, Mst Sanjida Alam, Mohammad Delowar Hossain Gazi, Mahabubur Rahman, Machine learning applications in predicting structural failures and earthquake damage.
Talukder, A.R., et al., Underwater image enhancement and restoration with YOLO-based object detection and recognition. Well Test J, 2025. 34(S3): p. 727-48.
Rabby, H.R., et al. Coronavirus Disease Outbreak Prediction and Analysis Using Machine Learning and Classical Time Series Forecasting Models. in 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). 2024. IEEE.
Arafat, Y., et al., Big Data Analytics in Information Systems Research: Current Landscape and Future Prospects Focus: Data science, cloud platforms, real-time analytics in IS. Emerging Frontiers Library for The American Journal of Engineering and Technology, 2025. 7(8): p. 177-201.
Rabby, H.R., et al. Comparative Analysis of Machine Learning Algorithms for Breast Cancer Prediction Using Fine Needle Aspirate Image Features. in 2025 International Conference on Emerging Trends in Industry 4.0 Technologies (ICETI4T). 2025. IEEE.
Mohammed Majid Bakhsh, M.S.A.J., Sazzadul Islam, Nusrat Yeasmin Nadia, Ankur Sarkar, Md Shadikul Bari, SA Islam, From Compliance to Code: An NLP-Based QA Automation Framework For HRM Software in the US Healthcare Sector. Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology, ISSN (Online), 2024/3/11: p. 0493-2137.
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