Practical AI starts with a narrow task
The AI projects that reach production rarely begin with 'let's add a chatbot'. They begin with one well-defined job.
Generative AI makes it easy to build an impressive demo in an afternoon. It is much harder to build something an organization will trust with real work. The difference is almost always scope.
Pick a task with a clear right answer
Extracting an invoice number, classifying an incoming request, or finding the paragraph of a policy that answers a question — these tasks have answers that can be checked. That makes them measurable, and measurable systems can be improved.
Build the evaluation set first
Before choosing a model, collect fifty to a few hundred real examples with the correct outcome. This set tells you whether a prompt change helped, whether a cheaper model is good enough, and when quality drifts in production.
Design for being wrong
Every AI system makes mistakes. The useful question is what happens when it does. Confidence thresholds, human review queues and clear citations turn occasional errors into minor corrections instead of silent failures.
Start narrow, measure honestly, and expand once the first task is working. That's slower than a demo — and much faster than a pilot that never ships.