Ravishankar V Garg, Raghavendran Ramakrishnan
Agile methodology has revolutionized software development by prioritizing adaptability, incremental progress, and ongoing customer engagement, setting itself apart from conventional, more structured methods. Originating from the Agile Manifesto, this methodology enables cross-functional teams to deliver incremental updates that swiftly adapt to evolving customer needs. However, to meet escalating demands for faster delivery and heightened productivity, integrating artificial intelligence (AI) presents a compelling opportunity to enhance Agile practices. AI technologies, including machine learning, natural language processing (NLP), and predictive analytics, revolutionize the software development lifecycle by automating tasks, optimizing decision-making, and predicting project outcomes. This integration accelerates development cycles, enhances software quality, and boosts responsiveness to market dynamics. This paper explores Agile's evolution and principles, underscoring its foundational role in fostering adaptive and collaborative development practices. It examines how AI complements Agile by facilitating data-driven decision-making, automating tasks, and fostering continuous improvement. Case studies illustrate successful AI implementations in Agile environments, showcasing benefits like productivity gains, improved risk management, and accelerated innovation. Addressing challenges such as ethical concerns and skill requirements, this research identifies emerging trends in AI-driven DevOps and autonomous Agile teams. By synthesizing literature and empirical evidence, this study comprehensively analyzes AI's impact on Agile methodologies, outlining avenues for future research to maximize transformative potential and sustain competitive advantage.
Agile methodology, artificial intelligence (AI), machine learning, NLP, software development