Conversations about AI often begin with tools and models. A more useful first question is which job is worth testing. Where do staff spend time searching, reading documents or sorting information? One defined task gives you something you can actually evaluate.
Choose a repeated job
Finding clauses in contracts, sorting customer messages or extracting fields from documents all have recognisable inputs and outputs. Start with one job rather than an assistant expected to answer everything. A smaller scope makes results easier to check.
Frequency helps, but it is not the only factor. A frequent task with costly mistakes may be a poor first candidate. A weekly task that requires reading many documents might be easier to test safely.
Some work needs rules, not AI
When a condition is clear, such as routing a complete form for approval or notifying a manager above a set amount, ordinary software can handle it predictably. AI may be useful when the work involves varied documents, language or searching through large amounts of information.
One workflow can combine both. AI might read a document, rules can check required fields and amounts, and a person can confirm the result. The right split depends on data quality and the cost of an error.
Test the cases likely to fail
Use examples from real work, including unclear content, missing fields and different formats. Ask someone familiar with the task to review the output. Record what can be used, what must be corrected and how long corrections take. A simple count of correct answers does not tell the whole story.
Check whether the data may be used for the test, especially customer and employee information. If every result takes longer to verify than the original work, narrow the task or improve the data before integrating anything.
Only then make it part of daily work
Once the test shows value, decide where staff will use the feature, what data it may access and whether results should return to an existing system. Otherwise people may simply gain another screen to copy from.
Set out where a person must review a result and what happens when the system cannot answer or the data is wrong. The first AI project should improve a job, not serve as a display of model choices.