AI-SDLC
What Is AI-SDLC?
AI-SDLC means applying artificial intelligence across the software development lifecycle, from planning and coding to review, testing, deployment, security, and operational feedback.
In practice, AI-SDLC is not only about using an AI coding assistant in the IDE. It is about understanding how AI-assisted work moves through the full delivery system and whether it improves speed, quality, reliability, and developer experience.
Why AI-SDLC Matters
AI can increase coding activity very quickly, but more generated code does not automatically mean better delivery. Engineering leaders need visibility into how AI affects:
- cycle time and lead time,
- pull request review load,
- rework and code churn,
- defect and vulnerability trends,
- test coverage and CI/CD results,
- developer experience,
- governance and policy exceptions.
Without AI-SDLC visibility, teams may see higher output while hidden bottlenecks, quality risks, or governance gaps grow elsewhere in the workflow.
How AI-SDLC Is Measured
AI-SDLC maturity is usually measured by connecting AI usage and code-origin signals with delivery, quality, and governance data. Useful signals include:
- adoption of AI coding assistants,
- AI-assisted and human-authored code attribution,
- pull request cycle time,
- review pass rates,
- automated test outcomes,
- DORA metrics,
- SPACE-style developer experience signals,
- policy exceptions and approval paths.
The goal is to measure AI impact across the system, not only AI activity inside the IDE.
Related Terms
Related Assessment
Use the AI-SDLC Maturity & Visibility Assessment to evaluate how ready your engineering organization is for AI-assisted delivery.
Related Pages
Move from definition to product context with the most relevant Oobeya pages for this term.
