International Journal on Science and Technology

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Scaling Agile with AI: Enhancing Large-Scale Agile Frameworks through Predictive Analytics and Automation

Author(s) Ullas Das
Country India
Abstract This review explores the integration of artificial intelligence (AI) into large-scale Agile frameworks (such as the Scaled Agile Framework (SAFe), Large-Scale Scrum (LeSS), and Disciplined Agile) to enhance their scalability and performance. Key contributions include AI-enhanced Agile models that augment traditional frameworks with machine learning and intelligent automation; the use of predictive analytics for improved project forecasting and risk management; and the introduction of AI-driven automation tools to streamline coordination across multiple teams. The analysis highlights significant impacts on Agile theory, methodology, and industry practice: extending Agile principles with data-driven decision-making (theoretical contribution), introducing new AI-supported practices and tools in Agile workflows (methodological contribution), and demonstrating improved efficiency and decision-making in real-world implementations (practical contribution). These findings underscore the significance of AI in improving Agile scalability (better handling of complex, multi-team projects), enhancing efficiency (through automation of repetitive tasks and optimized resource allocation), and strengthening decision-making (via intelligent decision support and analytics). The review concludes with a discussion of implications for future research on Agile-AI integration and for industry adoption, suggesting that embracing AI in scaled Agile frameworks can drive significant improvements in project outcomes and organizational agility.
Keywords AI-driven Agile automation; predictive analytics; Scaled Agile Framework (SAFe); Large-Scale Scrum (LeSS); Disciplined Agile; Agile methodologies; AI in project management; scaled Agile frameworks; automation; decision support.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-28
DOI https://doi.org/10.71097/IJSAT.v16.i2.5874
Short DOI https://doi.org/g9r8gk
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