Start with a workflow, not a model
The most common mistake is starting from “we should use AI” instead of “this process is expensive, slow or error-prone.” Pick a specific, repeated workflow with a measurable cost (hours spent, errors made, customers lost) and ask whether AI can move that number.
A good first candidate is high-volume, rule-loose and tolerant of a human check: support triage, document extraction, drafting, internal search. Avoid workflows where a wrong answer is catastrophic and hard to detect, those come later, with guardrails.
Score candidates on value and feasibility
Rank each candidate on two axes: the value of automating it, and how feasible it is given your data and systems. Value is about frequency times cost. Feasibility is about whether the data exists, is accessible, and is clean enough to act on.
The best first project sits high on both. A high-value, low-feasibility idea is worth doing, after a data-readiness step. A low-value, high-feasibility idea is a distraction, no matter how easy it is.
Prove value before you industrialize
Build a focused pilot that a real user runs on real data. Measure the target metric against the manual baseline. If it moves, you have a case for production investment; if it does not, you have saved a large budget cheaply.
Only then add the production layer: monitoring, evaluation, human oversight, security and integration. This is where most “successful” pilots quietly fail, the gap between a demo and a dependable system is real work.
The takeaway
Start narrow, choose by value and feasibility, prove the metric, then industrialize. AI creates value when it is pointed at the right problem and held to a production standard, not when it is impressive in a slide.