For two years "add AI" meant a chatbot in the corner and a press release. The system underneath didn’t change. That approach is already dead - it produces software that looks modern and behaves exactly as before.
AI-native is a different starting point. We assume a model is part of the runtime, not a feature. Data is structured so it can be retrieved and reasoned over. Workflows expect a probabilistic step in the middle and design for review, fallback, and audit around it.
The practical effect is smaller systems. When a model handles classification, extraction, and routing, you delete thousands of lines of brittle rules. What remains is the part humans actually care about - the decision, the exception, the edge.
It also changes how we ship. AI-native systems are evaluated, not just tested: we measure accuracy and drift the way you’d measure latency. That discipline is the difference between a demo and something you can put in front of a customer.