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@article{180103,
        author = {Ullas Das},
        title = {AI-Driven Agile Transformation: A Theoretical Framework for Seamless Integration and Predictive Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {513-526},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180103},
        abstract = {Agile strategies are the pillars of modern 
project management that enable teams to be responsive 
to change as they become more efficient. The emergence 
of Artificial Intelligence (AI) presents an opportunity 
for new challenges as well as new possibilities for Agile 
transformation. The paper explores the linkage of Agile 
and AI and proposes an AI-infused Agile 
transformation model that increases predictive 
accuracy, decision-making, and workflow automation 
in Agile. The study details the architecture of the model, 
the input features, and training process and then 
compares the predictive accuracy of the model to 
baseline models. Findings of the study reveal AI-based 
Agile models to be better in their predictions of sprints, 
backlog prioritization, and handling of risks, offering 
measurable advantages over typical methodologies. 
Implications to the practitioner, policymaking, and 
academics in the form of upskilling, ethical integration, 
and scalable adoption are equally explored in the paper. 
Integrating previous research and taking a visionary 
stand, the review aims to advise industry leaders, 
decision-makers, and academics in the application of AI 
in Agile transformation.},
        keywords = {Agile Transformation, Artificial  Intelligence, Predictive Analytics, Machine Learning,  Agile Methodologies, AI-Driven Project Management,  Scaled Agile, Data-Driven Decision Making, Agile-AI  Integration, AI in Software Development.},
        month = {May},
        }