The traditional Software Development Life Cycle (SDLC) framework, which has long guided software projects through stages such as planning, design, development, testing, deployment, and maintenance, is undergoing a significant transformation with the integration of artificial intelligence. AI technologies, particularly those powered by large language models (LLMs), are revolutionizing the SDLC by enabling simultaneous execution of traditionally sequential phases and facilitating continuous feedback loops across the entire lifecycle. This shift is reshaping workflows, enhancing efficiency, and introducing new challenges and constraints, such as the need for infrastructure that can handle AI-generated code's volume and variability. Additionally, AI's ability to quickly generate code and solutions is changing the roles of engineers and designers, who must now focus more on judgment, context, and integration rather than execution. As organizations adapt to this AI-driven environment, they are urged to invest in scalable infrastructure, redesign review processes to manage large changesets, and evolve cultural norms to leverage AI as a collaborative force multiplier in software delivery.