Your first AI product does not need a model. It needs a boring workflow

The instinct when starting something with AI is to build a product around the model. The pattern that keeps working is narrower: find a task somebody already pays for, that is done by hand, that is repetitive and text-heavy, and do that one task well. The characteristics of a good first target are consistent. The work already exists, so demand does not need to be created. It is bounded, so the output can be judged right or wrong. It is repetitive enough that a saving multiplies. And the cost of being wrong occasionally is recoverable, which rules out the first version touching money, medicine or legal advice without a human in the loop. What that looks like in practice: turning a pile of inbound emails into structured records, drafting a first version of something a person then edits, extracting fields from documents, summarising calls into notes that go into a system. Three build notes. Put a human approval step in the first version and remove it later if the error rate earns it. Log the inputs and outputs from day one, because without that log there is no way to know whether a change made it better. And write down what "good" means before building, as examples, not as adjectives. The competitive edge is rarely the model — everyone can reach the same ones. It is the access to the workflow, the data that comes with it, and the trust of the person whose job it touches. This article is general information, not legal, tax or financial advice. Rules change and every deal is different — check your own case with a licensed professional. Alberto Zaltzberg — Adonait · adonait.com