Adoption of AI across the Software Development Life Cycle

Despite the growing adoption of AI across the SDLC, existing literature predominantly focuses on the implementation phase—specifically on AI-generated code. While there is ample research comparing human and AI-generated code for quality, performance, and vulnerability (Liu et al., 2024; Li et al., 2024), a comprehensive understanding of how AI affects the broader SDLC remains elusive. Moreover, most studies emphasize capabilities of AI rather than practical realities faced by developers and organizations. For instance, there is limited empirical data on how AI tools support non-coding activities like requirements elicitation, design evaluation, or release planning. Additionally, organizational factors such as AI governance, developer trust, team integration, and ethical concerns are underrepresented in scholarly work. Industry claims often lack peer-reviewed support, and case studies remain anecdotal. This research aims to bridge these gaps by exploring AI integration in a practical, holistic, and empirically grounded manner across all phases of the SDLC.