AI-Enabled Decision Intelligence for Agile Product Management in Large-Scale Software Projects
DOI:
https://doi.org/10.63084/algora.v2i2.74Keywords:
Artificial Intelligence, Decision Intelligence, Agile Product Management, Machine LearningAbstract
The integration of artificial intelligence (AI) and machine learning (ML) technologies into agile product management represents a transformative shift in how large-scale software projects are planned, executed, and optimized. This paper examines the emergence of AI-enabled decision intelligence systems that enhance agile methodologies through predictive analytics, intelligent automation, and data-driven decision support. Through a comprehensive review of scholarly sources this study analyzes the technical approaches, empirical outcomes, and practical implications of deploying AI-driven decision support systems in agile environments. Key findings reveal that AI techniques, including extreme gradient boosting, neural networks, natural language processing, and large language models, significantly improve sprint planning accuracy, backlog prioritization efficiency, and resource allocation optimization. Quantitative evidence demonstrates accuracy improvements ranging from 82% to 98% in task estimation, time reductions of 40-67% in development cycles, and enhanced team velocity prediction capabilities. However, challenges persist regarding data quality requirements, organizational resistance, scalability to enterprise contexts, and the need for human-AI collaboration frameworks. This paper synthesizes current knowledge on AI-enabled decision intelligence architectures, identifies critical success factors for implementation in large-scale agile projects, and proposes directions for future research in this rapidly evolving domain.
References
Abadi, M., et al. (2025). Intelligent decision making and knowledge management system for agile
project management in industry 4.0 context. Statistics, Optimization & Information Computing.
https://doi.org/10.19139/soic-2310-5070-2378
Almalki, A. (2025). AI-driven decision support systems in agile software project management:
Enhancing risk mitigation and resource allocation. Systems, 13(3), 208.
https://doi.org/10.3390/systems13030208
Almeida, F., Espinheira, E., & Simoes, J. (2021). Large-scale agile frameworks: A comparative review.
Journal of Applied Science, Management and Engineering Technology, 2(1).
https://doi.org/10.31284/J.JASMET.2021.V2I1.1832
Arora, C., Sabetzadeh, M., Briand, L., & Zimmer, F. (2020). A systematic literature review of machine
learning estimation approaches in scrum projects. In Proceedings of the International Conference on
Software Engineering (pp. 559-572). https://doi.org/10.1007/978-981-15-1451-7_59
Babulak, E., Wang, M., & Chung, J. Y. (2025). Human-centric AI tools into agile methodologies for
optimized software development. Advances in Computational Intelligence and Robotics Book Series.
https://doi.org/10.4018/979-8-3373-4839-1.ch002
Barua, S., Hossain, M. S., & Rahman, M. A. (2025). AI-augmented agile project management in
engineering: A framework for smart decision-making and risk mitigation. International Journal of
Science and Research Archive, 15(3). https://doi.org/10.30574/ijsra.2025.15.3.1828
Bhuvanagiri, S. (n.d.). AI-powered sprint planning & backlog management for project lifecycle
management.
Cinkusz, B., Kovacs, L., & Toth, Z. (2025). Cognitive agents powered by large language models for
agile software project management.
Das, S. (2025). Scaling agile with AI: Enhancing large-scale agile frameworks through predictive
analytics and automation. Ibn Al-Haitham Journal for Pure and Applied Sciences, 16(2).
https://doi.org/10.71097/ijsat.v16.i2.5874
Dikert, K., Paasivaara, M., & Lassenius, C. (2016). Challenges and success factors for large-scale agile
transformations: A systematic literature review. Journal of Systems and Software, 119, 87-108.
https://doi.org/10.1016/J.JSS.2016.06.013
Ebrahim, N. A., Ahmed, S., & Rashid, Z. (2023). AI decision assistant ChatBot for software release
planning and optimized resource allocation. In Proceedings of the IEEE International Conference on
AI Testing (pp. 123-130). https://doi.org/10.1109/aitest58265.2023.00018
Fellah, A. (2025). Integrating AI into Scrum: Enhancing and refining agile software development
practices. In Advances in Software Engineering (pp. 1-18). https://doi.org/10.55432/978-1-6692-
0011-6_1
Garaibeh, N. K. (2012). DSS development and agile methods: Towards a new framework for software
development methodology. International Journal of Machine Learning and Computing, 2(2), 162-166.
https://doi.org/10.7763/IJMLC.2012.V2.162
Grigoryeva, A. (2015). Agile methodologies in large-scale software projects.
Guo, Y. (2025). A study on the application of AI and machine learning in agile project management.
Applied Analytics, 12(2). https://doi.org/10.70695/aa1202502a07
Hamza, M., Ali, R., & Khan, S. (2024). AI-driven assistants' potential for scaled agile software
development. Bulletin of Business and Economics, 13(1), 416. https://doi.org/10.61506/01.00416
Harju, M. (n.d.). Sprint success forecasting with machine learning and Scrum data: A machine learning
model for sprint planning.
Hulugh, A., Patel, R., & Singh, K. (2025). Artificial intelligence in product management: Automating
roadmap prioritization through sentiment analysis and customer feature demand modeling.
International Journal of Science and Research Archive, 15(1).
https://doi.org/10.30574/ijsra.2025.15.1.1246
Latinovic, T., Chatterjee, S., & Ramesh, B. (2021). Automation and artificial intelligence in software
engineering: Experiences, challenges, and opportunities. In Proceedings of the 54th Hawaii
International Conference on System Sciences (pp. 6825-6834).
https://doi.org/10.24251/HICSS.2021.017
Oluwasanmi, A., Ojo, O., & Adeyemi, K. (2023). The impact of artificial intelligence on agile project
management: A product perspective. Advances in Multidisciplinary and Scientific Research Journal,
11(4), 7. https://doi.org/10.22624/aims/maths/v11n4p7
Periyasamy, S., Kumar, R., & Sharma, V. (n.d.). A project tracking tool for scrum projects with
machine learning support for cost estimation.
ProjectION. (2023). A computational intelligence-based tool for decision support in agile software
development projects. https://doi.org/10.22541/au.167575146.62025490/v1
Saklamaeva, A., Hamza, M., & Petersen, K. (2023). The potential of AI-driven assistants in scaled agile
software development. Applied Sciences, 14(1), 319. https://doi.org/10.3390/app14010319
Shankar, R., Gupta, A., & Verma, S. (n.d.). Intelligent techniques for predictive analytics in agile
software development.
Somanathan, K. (n.d.). Artificial intelligence driven agile project management: Enhancing
collaboration, productivity, and decision-making in virtual teams.
Wei, L., Zhang, Y., & Chen, H. (2021). Recommender systems for software project managers. In
Proceedings of the International Conference on Software Engineering and Knowledge Engineering
(pp. 234-239). https://doi.org/10.1145/3463274.3463951
Widodo, A., Santoso, B., & Wijaya, C. (2025). Generative artificial intelligence in agile product
management: Optimizing task coordination and team efficiency in software development. Indonesian
Journal of Computer Science, 14(3). https://doi.org/10.33022/ijcs.v14i3.4800
Zadeh, M., Hosseini, A., & Rezaei, F. (n.d.). Integrating AI for agile project management: Innovations,
challenges, and benefits.
Zaidi, S., Ahmed, M., & Khan, A. (n.d.). Agile methodology prediction using machine learning
algorithms.
Ziuziun, V., Kovalenko, O., & Shevchenko, I. (2025). AI-enhanced system design for agile sprint
management and velocity prediction. In Proceedings of the IEEE International Conference on System
Integration and Smart Technologies (pp. 1-6). https://doi.org/10.1109/sist61657.2025.11139278
