Business & Deployment
AI-native
Not “retrofitted with AI features,” but built from the ground up around the new cost structure: what would this process be if reading, writing and first drafts cost almost nothing? AI-native organizations keep their knowledge machine-readable, build review loops instead of sign-off chains, and measure results instead of presence. The rebuild is organizational, not technical.
An example
An insurer layers AI onto the existing claims process: the form gets filled out faster, everything else stays. An AI-native approach first asks why the form exists. It existed because a human needed structured information. If a system can extract the same information from a photo, a voice message and the case history, the form is no longer a tool but a hurdle. The process does not get faster — it becomes a different one.
A closer look
Not AI built into existing workflows, but workflows cut around the new capabilities. Recognizable in small things: what is the smallest unit that gets shipped, and how quickly does usage flow back into the product.
Why it matters
The term is overused, but it means something concrete: not speeding up existing workflows, but recutting them on the assumption that unstructured inputs can be processed. That is considerably more effort and considerably less often worthwhile than the term suggests — most workflows deserve acceleration, not a rebuild. The workable test question: does this work step exist only because a human previously had to translate? If so, the recut pays off. If not, acceleration is enough.
Commonly misunderstood
The word is used for any company with a chat window. The test question is which workflow would no longer function at all without AI.