Everyone wants to add AI. Few stop to ask where it returns more than it costs. Here are the three places it consistently does, and the trap to avoid.
1. Turning messy text into action
Support tickets, emails, contracts, PDFs. A Large Language Model is very good at reading unstructured text and turning it into something structured: a category, a summary, a next step. The payoff is measurable in hours saved and faster response times, not vague ‘innovation’.
2. First drafts a human finishes
Copy, replies, documentation, proposals. The model gets you past the blank page in seconds; you keep judgment and final say. In practice that is most of the time spent, gone, while quality stays in human hands.
3. Asking your own knowledge in plain language
Point the model at your own documents and let people ask questions in plain words. It quietly removes the ‘where is that file’ tax that every growing company pays.
Where it just burns budget
An open-ended ‘chatbot for everything’ with no metric and no human in the loop. Start narrow, measure the result, then expand. That is the difference between AI that pays for itself and AI that becomes a line item nobody can justify.
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