Before choosing a model, find the work that is worth improving.
Start with a problem people recognise
An effective first AI project begins with an everyday task. Look for repeated work with a clear input, a useful output and a person who can judge the result. Drafting a response from approved documentation is easier to evaluate than a broad promise to transform an entire business.
Establish the baseline
Understand how the task is done now. Record the time involved, the common exceptions and the standard of work people expect. Those observations give you something concrete to compare with a prototype. Choose a measure that reflects usefulness, rather than simply the amount of generated output.
Check the information first
Find out whether the necessary information is available, current and appropriate to use. Decide which people and systems should have access. When answers depend on company documents, make it easy to see the source and recognise when the system cannot answer confidently.
Keep a person in the loop
Define which outputs need review and how someone can correct or reject a suggestion. Start with a bounded pilot. Evaluate a representative set of examples, including awkward edge cases, and use the findings to decide whether to continue, change the approach or stop.
A practical starting point
Choose one workflow, one owner and one agreed outcome. Document what the system can and cannot do. A small improvement that people can trust is a better foundation for expansion than a broad prototype nobody knows how to operate.
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