Choose

AI use cases: from problems to solutions

A useful use case describes who needs to do what, with which data and what result would be sufficient. Starting there avoids comparing brands solving different problems. These routes connect common needs with decision criteria and practical resources.

Turn a need into a test

Write down the input, deliverable and constraints before choosing technology. Add normal examples and cases where errors are costly. Define what can be automated and what needs additional control. Evaluation should include the workflow before AI to check whether the new solution makes a real improvement.

A concrete example

When analyzing customer comments, a sentiment label may be insufficient: you might need quotations, reasons and priorities. In a repository, an explanation is not a validated patch. For private documents, transport and permissions may matter as much as extraction quality.

What to check

Set observable acceptance criteria and a budget per completed task. Compare alternatives using the same sample. If none meets the minimum, narrow the scope or change the workflow; do not turn an attractive demonstration into a production decision.

Use cases

Three frequent starting points.

PRODUCT

Analyze feedback

Prioritize quotes, sufficient context and processing of sensitive data.

Traceability DEVELOPMENT

Maintain a repository

It measures global understanding, tool use and automatic verification.

Code PRIVACY

Process documents locally

Adjusts memory, quantization and context to the available hardware.

Local AI
ANALYSIS

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SourcesStanford HELMcrfm.stanford.edu