AI-augmented agile project management in engineering: A framework for smart decision-making and risk mitigation

The study analyzed how Artificial Intelligence-driven decision-making might enhance agile project management. The use of Artificial Intelligence provides predictive analytics, real-time risk assessment. Decision making is driven by data, which increases the flexibility and efficiency of the project. This research proposed a framework that may optimize the effectiveness of Agile processes. All this is made possible by using Artificial Intelligence methods such as intelligent automation, machine learning, and natural language processing. The study highlights how Artificial Intelligence may enhance sprint planning. Impact of AI on backlog prioritizing, and resource allocation is also considered. This is accomplished via use of early detection and scenario analysis. The potential of Artificial Intelligence to transform project management in dynamic contexts is examined, along with its future implications for agile techniques in engineering.

Authors

Publication Details

Journal
International Journal of Science and Research Archive
Published
2025-06-17
DOI
https://doi.org/10.30574/ijsra.2025.15.3.1828
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

AI-augmented agile project management in engineering: A framework for smart decision-making and risk mitigation

Khursid Alam, Jesmin Ul Zannat Kabir, Ashrafur Rahman Nabil, Chapal Barua et al.
1 citations
International Journal of Science and Research Archive
Big Data and Business Intelligence
2.28
article

AI-augmented agile project management in engineering: A framework for smart decision-making and risk mitigation

Khursid Alam, Jesmin Ul Zannat Kabir, Ashrafur Rahman Nabil, Chapal Barua, Sonay Duman
article en
1 citations

Abstract

The study analyzed how Artificial Intelligence-driven decision-making might enhance agile project management. The use of Artificial Intelligence provides predictive analytics, real-time risk assessment. Decision making is driven by data, which increases the flexibility and efficiency of the project. This research proposed a framework that may optimize the effectiveness of Agile processes. All this is made possible by using Artificial Intelligence methods such as intelligent automation, machine learning, and natural language processing. The study highlights how Artificial Intelligence may enhance sprint planning. Impact of AI on backlog prioritizing, and resource allocation is also considered. This is accomplished via use of early detection and scenario analysis. The potential of Artificial Intelligence to transform project management in dynamic contexts is examined, along with its future implications for agile techniques in engineering.

International Journal of Science and Research ArchiveVol. 15(3)
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Big Data and Business Intelligence
2.28
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AI-augmented agile project management in engineering: A framework for smart decision-making and risk mitigation — Khursid Alam, Jesmin Ul Zannat Kabir, et al. · International Journal of Science and Research Archive (2025) | TGRS Research Map | TGRS