Building something with AI is a bit like planning a long trip: you do not jump straight to the destination, you move through stops in order. There are six of them — first you work out the problem, then you gather data, look closely at that data, build a model, check how good it is, and finally put it to use. You cannot do them out of order, and when you reach the end you often loop back to make the next version better. The whole trip is the work, not any one stop.
The very first stop is to get crystal-clear about the problem before you collect a single thing. Four small questions do the job: who is stuck, what exactly is going wrong, where it happens, and why fixing it matters. A fuzzy problem leads to a fuzzy project — a machine is very precise, so the question you hand it has to be precise too.
Next you go and get the data — but not just any data, and not as much as possible. You want the data that actually fits your question, taken from places you can trust. A small, well-chosen set beats a giant pile gathered carelessly, because whatever the machine learns, it learns straight from this data.
Before building anything, spend time really looking at what you collected. Draw charts so the numbers become a picture, hunt for patterns of what goes with what, notice anything missing, and flag the surprises that do not fit. Spotting a problem here is easy and cheap; missing it now and finding it later is a real headache.
Now you build the part that actually decides. The big question first: do you already know the rule? If the rule is known and stable, you can just write it down yourself. If the rule is buried in the data and too tangled to spell out, you let the machine learn it from lots of examples instead. The problem decides which path you take.
Once you have a model, you have to be honest about how good it really is. One nice-looking score can fool you — a model can seem clever overall and still get the important cases wrong. So you test it on examples it has not seen before, and you look at the kind of mistakes it makes, not just the count.
The last stop is letting the model do its job for real — and keeping an eye on it. The world keeps changing, so a model that worked yesterday can slip over time. That is why this is not really the end: what you learn from real use flows back to the first stop, and the trip begins again, a little better each time.