MACHINE LEARNING-DRIVEN INTELLIGENT DECISION SUPPORT SYSTEMS IN MANAGEMENT
Keywords:
machine learning, decision support systems, explainable artificial intelligence, managerial decisions, deep learning, digital transformation, management intellectualization, Big Data, predictive analytics
Abstract
The article explores the theoretical foundations and practical mechanisms for the intellectualization of managerial decision support systems through machine learning (ML) methods. The relevance of the research is driven by the rapid growth of unstructured data volumes, increasing complexity of managerial tasks, and the limited adaptive capabilities of traditional deterministic decision support systems (DSSs). The study aims to develop a concept of an Intelligent Decision Support System (IDSS) that integrates ML algorithms with explainable artificial intelligence (XAI) tools. Based on a systematic review of contemporary domestic and international scientific sources, ML algorithms are classified according to types of managerial tasks: demand forecasting, risk classification, anomaly detection, resource optimization and text data analysis. A comparative analysis of seven leading ML algorithms by prediction accuracy and interpretability criteria reveals the optimal balance of these parameters in ensemble methods (XGBoost, Random Forest) combined with SHAP and LIME. An original IDSS concept based on a five-level architecture is proposed, encompassing: a data collection and processing level (Data Layer), a model training level (ML Engine), a decision generation level (Decision Layer), an interpretability level (XAI Layer) and a user interaction level (UI/UX Layer). The key innovation is the XAI module, which eliminates the black-box problem and ensures transparency of recommendations for management personnel. The sectoral potential of IDSS is assessed across seven economic sectors, with maximum effects identified in the financial sector and healthcare (25–35% efficiency gains). A three-phase IDSS implementation mechanism is developed to minimize organizational risks of digital transformation. The scientific novelty lies in the improvement of the IDSS conceptual framework through comprehensive integration of deep learning algorithms, NLP and XAI into a unified management architecture, ensuring a qualitatively new level of transparency and validity of managerial decisions.
References
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16. Zorrilla J., Seijo S., Arenal U., Mena J. R. AI-Driven Decision Support System for Proactive Risk Management in Construction Projects. Intelligent Infrastructure and Construction. 2026. Vol. 2. No. 2. Article 4. DOI: https://doi.org/10.3390/iic2020004
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1. Horbachenko S. A., Chepurna O. Ye., Ihnatenko A. I. Digital transformation in management: the influence of artificial intelligence and big data analytics on strategic decision-making. Business Navigator. 2025. Vol. 4. No. 81. pp. 481–487. DOI: https://doi.org/10.32782/business-navigator.81-76
2. Balabukha K. Ye. Intellectualization of enterprise development management systems based on artificial intelligence in the digital economy. Achievements of Economy: Prospects and Innovations. 2026. No. 28. DOI: https://doi.org/10.5281/zenodo.19335315
3. Chepurna O. Ye. Mathematical modeling of information security risks: application of stochastic analysis and machine learning for strategy optimization. Information and Infrastructure Security of Information and Communication Systems: Interdisciplinary Approach. Riga. Liha-Pres. 2025. pp. 103–141. DOI: https://doi.org/10.36059/978-966-397-537-5-4
4. Goga A. J. Impact of AI-Based Decision Support Systems on Managerial Decision-Making in Contemporary Organizations. Journal of Cultural Analysis and Social Change. 2025. Vol. 10. No. 4. DOI: https://doi.org/10.64753/jcasc.v10i4.3117
5. Kostopoulos G., Davrazos G., Kotsiantis S. Explainable artificial intelligence based decision support systems. Electronics. 2024. Vol. 13. No. 14. Article 2842. DOI: https://doi.org/10.3390/electronics13142842
6. Bondac G. T., Stanescu S. G., Ionescu C. A. Decision making in complex systems using AI based decision support: the role of trust, transparency and data quality. Electronics. 2026. Vol. 15. No. 2. Article 372. DOI: https://doi.org/10.3390/electronics15020372
7. Balkan D., Akyuz G. Artificial intelligence and machine learning in procurement and purchasing decision support. Artificial Intelligence Review. 2025. Vol. 58. DOI: https://doi.org/10.1007/s10462-025-11336-1
8. Casido J., Calumpang Z. AI driven decision support: a systematic review of machine learning models in organizational intelligence systems. International Journal of Multidisciplinary Research in Science, Engineering and Technology. 2025. Vol. 8. No. 10. DOI: https://doi.org/10.15680/IJMRSET.2025.0810034
9. Rao A. Impact of data breaches on user intentions toward GenAI bots. Journal of Computer Information Systems. 2026. pp. 1–17. DOI: https://doi.org/10.1080/08874417.2026.2638467
10. Piletska S. T., Korytko T. Yu. Business process reengineering based on the information support model. Economy and Society. 2025. No. 81. DOI: https://doi.org/10.32782/2524-0072/2025-81-37
11. Sauer C. R., Burggräf P., Steinberg F. Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assembly. Production Engineering. 2025. Vol. 19. No. 2. pp. 211–224. DOI: https://doi.org/10.1007/s11740-024-01306-x
12. Soori M., Karimi Ghaleh Jough F., Dastres R., Arezoo B. AI-based decision support systems in Industry 4.0: a review. Journal of Economy and Technology. 2026. Vol. 4. No. 2. pp. 206–225. DOI: https://doi.org/10.1016/j.ject.2024.08.005
13. Lee K. H. et al. Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study. npj Digital Medicine. 2024. Vol. 7. No. 1. Article 2. DOI: https://doi.org/10.1038/s41746-023-00976-8
14. Shokare C. Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models. Asian Journal of Science, Technology, Engineering, and Art. 2025. Vol. 3. No. 2. pp. 240–253. DOI: https://doi.org/10.58578/AJSTEA.v3i2.4933
15. Namdarzadegan M., Bozorgi Amiri A. Development of a Machine Learning-Based Decision Support System for Smart Technology Selection in Small and Medium-Sized Enterprises Considering Implementation Risks. Journal of Industrial and Systems Engineering. 2025. Vol. 17. No. 2. pp. 103–131.
16. Zorrilla J., Seijo S., Arenal U., Mena J. R. AI-Driven Decision Support System for Proactive Risk Management in Construction Projects. Intelligent Infrastructure and Construction. 2026. Vol. 2. No. 2. Article 4. DOI: https://doi.org/10.3390/iic2020004
17. Malatji M. A cybersecurity AI agent selection and decision support framework. arXiv preprint arXiv:2510.01751. 2025. Available at: https://arxiv.org/abs/2510.01751
18. Sagetap. The State of AI in Cybersecurity 2026: 264 Security Leader Decisions. 2026. Available at: https://www.sagetap.io/resource/h2-2025-cybersecurity-report
19. SAPEA. Artificial intelligence in emergency and crisis management: Rapid Evidence Review Report. 2025. 104 p. DOI: https://doi.org/10.5281/zenodo.17737962
20. Stanford Institute for Human Centered Artificial Intelligence. AI Index Report 2024. Stanford University. 2024. Available at: https://aiindex.stanford.edu/report
2. Балабуха К. Є. Інтелектуалізація системи управління розвитком підприємств на основі штучного інтелекту в умовах цифрової економіки. Здобутки економіки, перспективи та інновації. 2026. № 28. DOI: https://doi.org/10.5281/zenodo.19335315
3. Чепурна О. Є. Математичне моделювання ризиків інформаційної безпеки. застосування стохастичного аналізу та машинного навчання для оптимізації стратегій. Безпека інформації та інфраструктури інформаційно-комунікаційних систем. міждисциплінарний підхід. Riga : Liha-Pres, 2025. С. 103–141. DOI: https://doi.org/10.36059/978-966-397-537-5-4
4. Goga A. J. Impact of AI-Based Decision Support Systems on Managerial Decision-Making in Contemporary Organizations. Journal of Cultural Analysis and Social Change. 2025. Vol. 10. No. 4. DOI: https://doi.org/10.64753/jcasc.v10i4.3117
5. Kostopoulos G., Davrazos G., Kotsiantis S. Explainable artificial intelligence based decision support systems. Electronics. 2024. Vol. 13. No. 14. Article 2842. DOI: https://doi.org/10.3390/electronics13142842
6. Bondac G. T., Stanescu S. G., Ionescu C. A. Decision making in complex systems using AI based decision support. The role of trust, transparency and data quality. Electronics. 2026. Vol. 15. No. 2. Article 372. DOI: https://doi.org/10.3390/electronics15020372
7. Balkan D., Akyuz G. Artificial intelligence and machine learning in procurement and purchasing decision support. Artificial Intelligence Review. 2025. Vol. 58. DOI: https://doi.org/10.1007/s10462-025-11336-1
8. Casido J., Calumpang Z. AI driven decision support. A systematic review of machine learning models in organizational intelligence systems. International Journal of Multidisciplinary Research in Science, Engineering and Technology. 2025. Vol. 8. No. 10. DOI: https://doi.org/10.15680/IJMRSET.2025.0810034
9. Rao A. Impact of data breaches on user intentions toward GenAI bots. Journal of Computer Information Systems. 2026. P. 1–17. DOI: https://doi.org/10.1080/08874417.2026.2638467
10. Пілецька С. Т., Коритько Т. Ю. Реінжиніринг бізнес-процесів на основі моделі інформаційного забезпечення. Економіка та суспільство. 2025. № 81. DOI: https://doi.org/10.32782/2524-0072/2025-81-37
11. Sauer C. R., Burggräf P., Steinberg F. Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assembly. Production Engineering. 2025. Vol. 19. No. 2. P. 211–224. DOI: https://doi.org/10.1007/s11740-024-01306-x
12. Soori M., Karimi Ghaleh Jough F., Dastres R., Arezoo B. AI-based decision support systems in Industry 4.0: a review. Journal of Economy and Technology. 2026. Vol. 4. No. 2. P. 206–225. DOI: https://doi.org/10.1016/j.ject.2024.08.005
13. Lee K. H. et al. Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study. npj Digital Medicine. 2024. Vol. 7. No. 1. Article 2. DOI: https://doi.org/10.1038/s41746-023-00976-8
14. Shokare C. Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models. Asian Journal of Science, Technology, Engineering, and Art. 2025. Vol. 3. No. 2. P. 240–253. DOI: https://doi.org/10.58578/AJSTEA.v3i2.4933
15. Namdarzadegan M., Bozorgi Amiri A. Development of a Machine Learning-Based Decision Support System for Smart Technology Selection in Small and Medium-Sized Enterprises Considering Implementation Risks. Journal of Industrial and Systems Engineering. 2025. Vol. 17. No. 2. P. 103–131.
16. Zorrilla J., Seijo S., Arenal U., Mena J. R. AI-Driven Decision Support System for Proactive Risk Management in Construction Projects. Intelligent Infrastructure and Construction. 2026. Vol. 2. No. 2. Article 4. DOI: https://doi.org/10.3390/iic2020004
17. Malatji M. A cybersecurity AI agent selection and decision support framework. arXiv preprint arXiv:2510.01751. 2025. Available at: https://arxiv.org/abs/2510.01751
18. Sagetap. The State of AI in Cybersecurity 2026: 264 Security Leader Decisions. 2026. Available at: https://www.sagetap.io/resource/h2-2025-cybersecurity-report
19. SAPEA. Artificial intelligence in emergency and crisis management. Rapid Evidence Review Report. 2025. 104 p. DOI: https://doi.org/10.5281/zenodo.17737962
20. Stanford Institute for Human Centered Artificial Intelligence. AI Index Report 2024. Stanford University, 2024. Available at: https://aiindex.stanford.edu/report
1. Horbachenko S. A., Chepurna O. Ye., Ihnatenko A. I. Digital transformation in management: the influence of artificial intelligence and big data analytics on strategic decision-making. Business Navigator. 2025. Vol. 4. No. 81. pp. 481–487. DOI: https://doi.org/10.32782/business-navigator.81-76
2. Balabukha K. Ye. Intellectualization of enterprise development management systems based on artificial intelligence in the digital economy. Achievements of Economy: Prospects and Innovations. 2026. No. 28. DOI: https://doi.org/10.5281/zenodo.19335315
3. Chepurna O. Ye. Mathematical modeling of information security risks: application of stochastic analysis and machine learning for strategy optimization. Information and Infrastructure Security of Information and Communication Systems: Interdisciplinary Approach. Riga. Liha-Pres. 2025. pp. 103–141. DOI: https://doi.org/10.36059/978-966-397-537-5-4
4. Goga A. J. Impact of AI-Based Decision Support Systems on Managerial Decision-Making in Contemporary Organizations. Journal of Cultural Analysis and Social Change. 2025. Vol. 10. No. 4. DOI: https://doi.org/10.64753/jcasc.v10i4.3117
5. Kostopoulos G., Davrazos G., Kotsiantis S. Explainable artificial intelligence based decision support systems. Electronics. 2024. Vol. 13. No. 14. Article 2842. DOI: https://doi.org/10.3390/electronics13142842
6. Bondac G. T., Stanescu S. G., Ionescu C. A. Decision making in complex systems using AI based decision support: the role of trust, transparency and data quality. Electronics. 2026. Vol. 15. No. 2. Article 372. DOI: https://doi.org/10.3390/electronics15020372
7. Balkan D., Akyuz G. Artificial intelligence and machine learning in procurement and purchasing decision support. Artificial Intelligence Review. 2025. Vol. 58. DOI: https://doi.org/10.1007/s10462-025-11336-1
8. Casido J., Calumpang Z. AI driven decision support: a systematic review of machine learning models in organizational intelligence systems. International Journal of Multidisciplinary Research in Science, Engineering and Technology. 2025. Vol. 8. No. 10. DOI: https://doi.org/10.15680/IJMRSET.2025.0810034
9. Rao A. Impact of data breaches on user intentions toward GenAI bots. Journal of Computer Information Systems. 2026. pp. 1–17. DOI: https://doi.org/10.1080/08874417.2026.2638467
10. Piletska S. T., Korytko T. Yu. Business process reengineering based on the information support model. Economy and Society. 2025. No. 81. DOI: https://doi.org/10.32782/2524-0072/2025-81-37
11. Sauer C. R., Burggräf P., Steinberg F. Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assembly. Production Engineering. 2025. Vol. 19. No. 2. pp. 211–224. DOI: https://doi.org/10.1007/s11740-024-01306-x
12. Soori M., Karimi Ghaleh Jough F., Dastres R., Arezoo B. AI-based decision support systems in Industry 4.0: a review. Journal of Economy and Technology. 2026. Vol. 4. No. 2. pp. 206–225. DOI: https://doi.org/10.1016/j.ject.2024.08.005
13. Lee K. H. et al. Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study. npj Digital Medicine. 2024. Vol. 7. No. 1. Article 2. DOI: https://doi.org/10.1038/s41746-023-00976-8
14. Shokare C. Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models. Asian Journal of Science, Technology, Engineering, and Art. 2025. Vol. 3. No. 2. pp. 240–253. DOI: https://doi.org/10.58578/AJSTEA.v3i2.4933
15. Namdarzadegan M., Bozorgi Amiri A. Development of a Machine Learning-Based Decision Support System for Smart Technology Selection in Small and Medium-Sized Enterprises Considering Implementation Risks. Journal of Industrial and Systems Engineering. 2025. Vol. 17. No. 2. pp. 103–131.
16. Zorrilla J., Seijo S., Arenal U., Mena J. R. AI-Driven Decision Support System for Proactive Risk Management in Construction Projects. Intelligent Infrastructure and Construction. 2026. Vol. 2. No. 2. Article 4. DOI: https://doi.org/10.3390/iic2020004
17. Malatji M. A cybersecurity AI agent selection and decision support framework. arXiv preprint arXiv:2510.01751. 2025. Available at: https://arxiv.org/abs/2510.01751
18. Sagetap. The State of AI in Cybersecurity 2026: 264 Security Leader Decisions. 2026. Available at: https://www.sagetap.io/resource/h2-2025-cybersecurity-report
19. SAPEA. Artificial intelligence in emergency and crisis management: Rapid Evidence Review Report. 2025. 104 p. DOI: https://doi.org/10.5281/zenodo.17737962
20. Stanford Institute for Human Centered Artificial Intelligence. AI Index Report 2024. Stanford University. 2024. Available at: https://aiindex.stanford.edu/report
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2026-07-03
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Horbachenko, S., Chepurna, O., & Shkrabak, Y. (2026). MACHINE LEARNING-DRIVEN INTELLIGENT DECISION SUPPORT SYSTEMS IN MANAGEMENT. Transformational Economy, (3 (16), 21-27. https://doi.org/10.32782/2786-8141/2026-16-3
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Copyright (c) 2026 С. А. Горбаченко, О. Є. Чепурна, Є. В. Шкрабак
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