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Integrating AI into Agile Workflows: Opportunities and Challenges
- 1 School of Computing, The Australian National University, Canberra, ACT, 2601, Australia
* Author to whom correspondence should be addressed.
Abstract
Over the past few years, Agile development approaches have become an increasingly popular methodology for software engineering, focused on iterative progress, continuous feedback, and teamwork cooperation. However, the raising difficulties of projects and more demand for faster and more efficient workflows have challenged the situation further. The introduction of Artificial Intelligence into the Agile processes paves the way to optimize decision-making, automate routine tasks, and boost team productivity. This review summarizes the innovations and challenges created by the integration of AI into Agile development practices. Using AI technologies like machine learning, predictive analysis, and natural language processing, Agile teams can enhance sprint planning, resource coordination, and risk management. Also, it presents some risks such as data privacy, workforce skills need, and possible over-dependence of AI. The paper emphasizes on providing a full overview of the innovations and challenges to the application of AI in Agile workflows, providing thoughts for future research and practices.
Keywords
Agile workflows, AI integration, software development, AI-driven sprint planning, risk management, task automation, team collaboration.
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Cite this article
Jin,Z. (2024). Integrating AI into Agile Workflows: Opportunities and Challenges. Applied and Computational Engineering,116,49-54.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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