A Unified Framework for Integrating Generative AI Across the Agile Software Development Lifecycle (SDLC)

Abstract

Generative Artificial Intelligence (AI) is now widely adopted across various software engineering practices to assist in coding, testing, planning, and deployment tasks. Many enterprise deployments are still siloed, though, with AI solutions being used in only part of the software development lifecycle, not as a part of a cohesive agile workflow. This research aims at providing a single perspective of integrating generative AI into the entire agile software development lifecycle, while focusing on continuous context sharing between discovery, planning, development, integration, delivery, and feedback. It illustrates the synergy between AI voice agents and context-aware development prompts, automated Jira epic generation, predictive integration models, machine learning telemetry, and ERP-linked analytics in enhancing product execution. The article draws on experiences from enterprise-scale hardware-software environments to showcase how integrated AI workflows can reduce manual research effort, enhance access management, assist with faster engineering decisions, minimize data discrepancies, and boost release velocity. The study suggests that generative AI can bring more value when it’s not just a productivity tool but a tool that helps product managers, engineers, and stakeholders across the entire product lifecycle. The proposed AI-SDLC framework can provide a practical basis for better agile coordination, minimize the silos in the operation of the enterprise, and maximize the delivery of software in complex enterprise systems.