539.3

hybrid intelligent systems, decision support, complex software projects, artificial intelligence, method integration.

In today’s world, where the complexity of software projects is constantly increasing, the development of hybrid intelligent systems for decision support is becoming extremely relevant. This research presents novel models and methods aimed at enhancing the decision-making process in complex software projects. A hybrid intelligent decision support system was developed by integrating agent-based modeling, data-driven analytics, and agile project management principles. It has been shown that the proposed system improves decision accuracy by 18% and reduces project-related risks by 22% compared to conventional project management approaches. New algorithms for decision-making under uncertainty and complexity were developed and tested in simulated environments. The results obtained demonstrate the adaptability and effectiveness of the hybrid approach in dynamic project conditions. It was also established that combining artificial intelligence techniques with traditional methodologies enables faster response to changes in requirements and technology. Hence, the study confirms the feasibility and efficiency of hybrid intelligent systems in supporting managerial decisions throughout the entire software project lifecycle. The findings can be applied to improve project planning, risk mitigation, and overall project quality. This research contributes to the theoretical and practical advancement of decision support systems in the field of software engineering.

1. Lakemond N., Holmberg G. (2022) The quest for combined generativity and criticality in digital-physical complex systems. The Journal of Engineering and Technology Management, no. 65, 101701.
2. Lorson A., Dremel C., Uebernickel F. Evolution of Digital Innovation Units for Digital Transformation – The Convergence of Motors of Change. ICIS 2022 Proceedings. 2022. 15. URL: Available at: https://aisel. aisnet.org/icis2022/entren/entren/15 (accessed: 17.09.2024).
3. Lakemond N., Holmberg G., Pettersson A. (2024) Digital Transformation in Complex Systems. IEEE Transactions on Engineering Management, no. 71, pp. 192–204.
4. Hansen K. L., Rush H. (1998) Hotspots in complex product systems: Emerging issues in innovation management. Technovation, no. 18 (8/9), pp. 555–561.
5. Yu Y., Lakemond N., Holmberg G. (2024) AI in the Context of Complex Intelligent Systems: Engineering Management Consequences. IEEE Transactions on Engineering Management, no. 71, pp. 6512–6525.
6. Vial G. (2021). Understanding digital transformation: A review and a research agenda Management Digital Transformation, pp. 13–66.
7. Pastukh О., Yatsyshyn V. (2024) Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies. Вісник Тернопільського національного технічного університету, no. 113 (1), pp. 143–149.
8. Cooper G. R., Sommer A. F. (2018) Agile–Stage-Gate for Manufacturers. International Journal of Management and Organizational Research. International Journal of Management and Organizational Research, no. 61 (2), pp. 17–26.
9. Hybertson D., Hailegiorghis M., Griesi K., Soeder B., Rouse W. (2018) Evidence-based systems engineering. Systems Engineering, no. 21 (3), pp. 243–258.
10. Benbya H., Davenport T., Pachidi S. (2020) Artificial Intelligence in Organizations: Current State and Future Opportunities. MIS Quarterly Executive, no. 19, pp. 9–21.
11. Al-Mashari M., Zairi M. (2022) Exploring The Factors That Impact The Adoption Of Artificial Intelligence In Project Management. Journal Of Business Research, no. 145, pp. 471–482.
12. Anush K. Moorthy, Ghani A. (2020) The Role Of Project Managers In Artificial Intelligence And Automation Implementation. Proceedings Of The Fifth International Conference In Applied Engineering, pp. 82–88.
13. Taffer M. 5 Emerging Project Management Trends Of 2023. DPM: website. Available at: https:// thedigitalprojectmanager.com/industry/reports/project-management-trends/ (accessed: 17.09.2024).
14. Bannister F., Connolly R. (2020) Administration By Algorithm: A Risk Management Framework. Information Polity, no. 25 (4), pp. 471–490.
15. Barron R., Barron A. (2020). Artificial Intelligence In Project Management. The Palgrave Handbook Of Managing In A Digital World, pp. 207–232.
16. Cai L., Zhu Y. (2015) Data Quality And Data Quality Assessment Challenges In The Significant Data Era. Data Science Journal, no. 14, pp. 2–12.
17. Chui M., Manyika J., Miremadi M. Where Machines Could Replace Humans – And Where They Cannot (Yet). McKinsey Digital: website. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/ our-insights/where-machines-could-replace-humans-and-where-they-cant-yet (accessed: 17.09.2024).
18. Ayres L. The Future of Work: Embracing AI Through Collaboration and Specialization. Available
    at: https://www.linkedin.com/pulse/future-work-embracing-ai-through-collaboration-luciano-ayres-z4owf (accessed: 17.09.2024).
19. Davenport T. H. (2018) From Analytics To Artificial Intelligence. Journal Of Business Analytics, no. 2 (1), pp. 73–80.
20. Floridi L. (2021) Digital Time: Latency, Real-time and the Onlife Experience of Everyday Time. Philosophy & Technology, no. 34 (1). pp. 407–412.
21. Boehm B., Turner R. (2003) Using risk to balance agile and plan driven methods. Computer, no. 36 (6). pp. 57–66.
22. Brocal F., González C., Komljenovic D., Katina P. F., Sebastián M. A. (2019). Emerging risk management in industry 4.0: an approach to improve organizational and human performance in the complex systems. Complexity, pp. 1–13.
23. Elattar H. M., Elminir H.K., Riad A. M. (2018) Conception and implementation of a data-driven prognostics algorithm for safety–critical systems. Soft Computing, no. 23, pp. 3365–3382.

1. Lakemond N., Holmberg G. (2022) The quest for combined generativity and criticality in digital-physical complex systems. The Journal of Engineering and Technology Management, no. 65, 101701.
2. Lorson A., Dremel C., Uebernickel F. Evolution of Digital Innovation Units for Digital Transformation – The Convergence of Motors of Change. ICIS 2022 Proceedings. 2022. 15. URL: Available at: https://aisel. aisnet.org/icis2022/entren/entren/15 (accessed: 17.09.2024).
3. Lakemond N., Holmberg G., Pettersson A. (2024) Digital Transformation in Complex Systems. IEEE Transactions on Engineering Management, no. 71, pp. 192–204.
4. Hansen K. L., Rush H. (1998) Hotspots in complex product systems: Emerging issues in innovation management. Technovation, no. 18 (8/9), pp. 555–561.
5. Yu Y., Lakemond N., Holmberg G. (2024) AI in the Context of Complex Intelligent Systems: Engineering Management Consequences. IEEE Transactions on Engineering Management, no. 71, pp. 6512–6525.
6. Vial G. (2021). Understanding digital transformation: A review and a research agenda Management Digital Transformation, pp. 13–66.
7. Pastukh О., Yatsyshyn V. (2024) Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies. Вісник Тернопільського національного технічного університету, no. 113 (1), pp. 143–149.
8. Cooper G. R., Sommer A. F. (2018) Agile–Stage-Gate for Manufacturers. International Journal of Management and Organizational Research. International Journal of Management and Organizational Research, no. 61 (2), pp. 17–26.
9. Hybertson D., Hailegiorghis M., Griesi K., Soeder B., Rouse W. (2018) Evidence-based systems engineering. Systems Engineering, no. 21 (3), pp. 243–258.
10. Benbya H., Davenport T., Pachidi S. (2020) Artificial Intelligence in Organizations: Current State and Future Opportunities. MIS Quarterly Executive, no. 19, pp. 9–21.
11. Al-Mashari M., Zairi M. (2022) Exploring The Factors That Impact The Adoption Of Artificial Intelligence In Project Management. Journal Of Business Research, no. 145, pp. 471–482.
12. Anush K. Moorthy, Ghani A. (2020) The Role Of Project Managers In Artificial Intelligence And Automation Implementation. Proceedings Of The Fifth International Conference In Applied Engineering, pp. 82–88.
13. Taffer M. 5 Emerging Project Management Trends Of 2023. DPM: website. Available at: https:// thedigitalprojectmanager.com/industry/reports/project-management-trends/ (accessed: 17.09.2024).
14. Bannister F., Connolly R. (2020) Administration By Algorithm: A Risk Management Framework. Information Polity, no. 25 (4), pp. 471–490.
15. Barron R., Barron A. (2020). Artificial Intelligence In Project Management. The Palgrave Handbook Of Managing In A Digital World, pp. 207–232.
16. Cai L., Zhu Y. (2015) Data Quality And Data Quality Assessment Challenges In The Significant Data Era. Data Science Journal, no. 14, pp. 2–12.
17. Chui M., Manyika J., Miremadi M. Where Machines Could Replace Humans – And Where They Cannot (Yet). McKinsey Digital: website. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/ our-insights/where-machines-could-replace-humans-and-where-they-cant-yet (accessed: 17.09.2024).
18. Ayres L. The Future of Work: Embracing AI Through Collaboration and Specialization. Available
    at: https://www.linkedin.com/pulse/future-work-embracing-ai-through-collaboration-luciano-ayres-z4owf (accessed: 17.09.2024).
19. Davenport T. H. (2018) From Analytics To Artificial Intelligence. Journal Of Business Analytics, no. 2 (1), pp. 73–80.
20. Floridi L. (2021) Digital Time: Latency, Real-time and the Onlife Experience of Everyday Time. Philosophy & Technology, no. 34 (1). pp. 407–412.
21. Boehm B., Turner R. (2003) Using risk to balance agile and plan driven methods. Computer, no. 36 (6). pp. 57–66.
22. Brocal F., González C., Komljenovic D., Katina P. F., Sebastián M. A. (2019). Emerging risk management in industry 4.0: an approach to improve organizational and human performance in the complex systems. Complexity, pp. 1–13.
23. Elattar H. M., Elminir H.K., Riad A. M. (2018) Conception and implementation of a data-driven prognostics algorithm for safety–critical systems. Soft Computing, no. 23, pp. 3365–3382.