Most teams have already felt AI’s impact on speed: copilots, AI-assisted QA, faster prototyping. But speed alone doesn’t compound. Work still moves through the same lossy handoffs between strategy, design, engineering, compliance, and operations, so learning stays local and the next project starts almost from scratch.
The real gap isn’t whether your team can build with AI. It’s whether what you learn on project one becomes leverage on project five. Without a connected lifecycle, you can ship ten successful initiatives and still rediscover the same problems on the eleventh.
An AI-native SDLC closes that gap, designing the connective tissue so decisions, context, and rationale are captured, queryable, and reusable. The result is a delivery system that gets smarter with use: lower risk, faster onboarding, fewer repeated discovery cycles. Sava Marinkovich, the paper’s lead author, argues that organizations that win won’t be the ones that adopted AI tools first; they’ll be those that connected their disciplines into a lifecycle where every engagement makes the next one smarter.
In this ebook, you’ll find:
- Why AI acceleration often increases output without reducing end-to-end cycle time
- Learn the difference between using AI to go faster and going smarter with each new project
- Why “judgment work” becomes the true differentiator as AI commoditizes routine intelligence tasks
- A practical knowledge maturity model: from tribal → documented → structured → connected → predictive
- What a connected lifecycle looks like across strategy, design, engineering, compliance, and operations
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