Choose a task with a verifiable output and manageable failure consequences. Model availability alone does not establish a useful product opportunity.
Compare candidate tasks
In a fictional document workspace, classifying a file, drafting a summary and approving an expense have different consequences. Define the input, desired output and reviewer. If nobody can recognise a good result, an evaluation programme will be difficult to trust.
| Criterion | Scoping question |
|---|---|
| Value | Which existing work disappears? |
| Verification | Who can correct the output? |
| Data | Are representative examples available? |
| Error cost | What happens when the answer is wrong? |
| Alternative | Would a simpler rule solve the problem? |
Test a narrow slice
Include ambiguous documents and compare against a non-AI baseline. For an international product, sample the languages and document conventions customers actually use rather than assuming English results transfer unchanged. Measure review time as well as generation time.
Should the first version act autonomously?
Not necessarily. A revisable suggestion can reveal value before the system receives authority to take external actions. Define fallback behaviour and error reporting. The first use case should produce interpretable evidence; it does not need to handle every format and language immediately.
Multilingual extraction is a separate test dimension
An English-language buyer may upload invoices and contracts in several languages. Evaluate the actual input mix, including mixed-language documents. Do not assume a successful English demonstration proves the same extraction quality across the customer's document collection.
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