Artificial Intelligence For Risk And Decision Assessment In Agile IT Projects: A Thematic Analysis And Dynamic Structuration Framework Approach
DOI:
https://doi.org/10.29284/9k2nx425Keywords:
Agile project management; artificial intelligence; risk assessment; decision-making; machine learning; thematic analysis.Abstract
The integration of artificial intelligence (AI) into Agile IT project management has emerged as a transformative strategy for enhancing risk assessment and decision-making processes. Despite the increasing interest, a fragmented understanding persists regarding the application, effectiveness, and limitations of AI-driven tools in Agile environments. This thematic review systematically analyses 20 peer-reviewed studies published between 2010 and 2025 to synthesise current knowledge and identify research gaps. The literature reveals that machine learning, predictive analytics, natural language processing, and reinforcement learning offer substantial benefits, including improved sprint planning, early risk detection, optimised resource allocation, and greater responsiveness to dynamic project demands. The study categorises findings into four core themes: AI integration in Agile workflows, AI-driven risk assessment methodologies, AI-enhanced decision support systems, and challenges to adoption. Barriers such as data quality issues, model interpretability, integration complexities, organisational resistance, and high implementation costs are critically examined. A conceptual framework, the Dynamic structuration framework (DSF), which leverages the adaptive structuration theory and Dynamic capability theory, is proposed to guide the structured integration of AI within Agile project lifecycles, emphasising the need for recursive co-evolution, iterative feedback loops, augmented capabilities as well as a governable human–AI collaboration. The review highlights the superiority of AI-driven approaches over traditional methods in terms of predictive accuracy and operational agility, while also calling for further empirical validation and development of explainable AI models. The findings offer valuable insights for researchers and practitioners seeking to advance AI-enabled Agile project management.
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