AI programs stall when they begin with a platform decision instead of a painful workflow. The order matters: pick the model, then go looking for a problem it can solve, and you end up with a demo that never ships. Pick the problem first, and the model choice becomes almost mechanical.
The better sequence: map the process end to end, quantify the delay or error cost in terms the business already tracks, check whether the data required actually exists in usable form, then choose the thinnest model and integration that moves the metric. Computer vision on a grading line beats a chatbot bolted onto a PDF graveyard, because the grading line has a clear input, a clear output, and a cost of error that's already being measured.
Data readiness is the part most AI pitches skip. It's one thing to say "we could classify these images" and another to confirm the images are labeled consistently, captured under comparable lighting, and available in enough volume to train or fine-tune reliably. Half of what looks like a modeling problem is actually a data-collection problem in disguise, and no amount of model sophistication fixes that.
The ROI conversation also needs to happen before the build, not after. A model that's 95% accurate sounds impressive until you calculate what the remaining 5% costs in a workflow where errors compound—a bad crop grade that ships to a buyer, a bad classification that triggers the wrong automated action. Scoping the acceptable error rate against the actual cost of being wrong is a business conversation, not a data-science one, and it has to happen early.
This is the same discipline applied across NxtPerson's crop-grading computer vision and the AI-adoption consulting done at Karan Infosys: start with the workflow that already hurts, instrument it honestly, and let the model earn its place.