Most AI projects we are asked about fail the same test: nobody can say what number should move. Without that, there is no way to tell whether it worked, so it never gets turned off and never gets improved.
What pays
Document extraction, when someone is currently retyping fields off PDFs. The volume is countable, the error rate is measurable, and the before and after are obvious within a month.
Classification and routing, when a human currently reads each item to decide where it goes. The model does not need to be right every time. It needs to be right often enough that the review queue shrinks.
Search across your own material, when the alternative is asking a colleague who has been there longest. This one is easy to underestimate because the cost it removes is spread thin across everyone.
First drafts of repetitive writing, where a person still edits and approves. The gain is real and the risk is contained by the human at the end.
What usually does not
A chatbot on the website, when the actual problem is that the answers are not written down anywhere. The model cannot retrieve what does not exist.
Anything customer-facing and unreviewed in a business where being wrong is expensive. The cost of one confidently wrong answer to a client can exceed a year of the savings.
Replacing a judgment call that carries liability. If a person has to stand behind the decision, a person has to make it.
How to scope it so you find out fast
Pick one task. Measure how long it takes and how often it is wrong today, before any model is involved. Build the smallest thing that addresses it. Measure the same two numbers again.
If the numbers did not move, stop. That is a cheap answer arrived at quickly, which is the whole point.
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