AI-Native Development Calls for Rethinking Entire Engineering Workflow, Not Just Coding

A growing conversation in software engineering argues that AI adoption has so far focused on speeding up individual roles—such as developers, QA, and business analysts—rather than redesigning the end-to-end workflow. The broader problem lies in persistent bottlenecks like handoff delays, repeated context-sharing, and rework caused by fragmented knowledge spread across tickets, documentation, code, and chat. Analysts point to 'AI fragmentation' as a new challenge, where different team members use separate AI tools with inconsistent context, undermining any unified engineering process. The concept of AI-Native Software Development proposes optimizing lead time, cycle time, and coordination costs across the entire pipeline—not just the coding phase. Proponents argue the key question should shift from how fast AI can help individuals code to how AI can make the whole software lifecycle faster and less error-prone.