The AI-DLC is changing the way software is actually being developed, moving beyond the concept of AI being integrated into Agile and Scrum to a process that is centered on AI from the ground up. AWS's Raja SP coined the methodology in 2025, positioning AI as a core collaborator rather than a code-generation tool. Work gets organized around intents, translated into units, and executed through short iterations called bolts, a structure built specifically around what AI can now do, rather than one adapted from how human teams have always worked.
"Although 84% of developers use AI in the build phase of software development, two-thirds of developers say AI-generated code requires more testing than human-generated code." —InfoTech Research Group
That gap, high adoption paired with real oversight concerns, is precisely what AI-DLC's guiding principles were designed to close. Traditional SDLC keeps planning, coding, and testing as separate, sequential phases, with handoffs between each one. AI-DLC blurs those boundaries into continuous flow instead, while still requiring explicit human approval at every meaningful decision point along the way.
Managing this shift well takes more than adopting new tools. It takes the judgment to use them responsibly, and that's where structured learning/generative AI certifications earn their place. USAII's CAIE™ certification focuses on that foundation, covering machine learning, deep learning, and the applied skills behind working effectively with AI coding agents throughout a real development lifecycle.
The infographic below takes a closer look at the AI-DLC meaning, its core principles, and how it stacks up against the traditional SDLC.
