We Know AI practice · No invented numbers
AI case studies: what changed, how it was checked and what failed
This library provides a transparent case-study method and demonstration scenarios. A scenario is never presented as a verified business result.
A useful case-study framework
Start with the original process, the problem and a measurable definition of success. Document the data, tool, settings, number of attempts, human corrections and verification. Finish with limitations and the next experiment.
Demonstration scenarios
Customer-support drafts
Use approved sources to produce drafts while a person verifies facts and tone. Measure the complete response time, edit rate and factual errors—not only generation speed.
Expert interview to article
Transcribe owned material, extract claims with timestamps and prepare a structure. The expert validates meaning and the editor removes generic AI phrasing.
Internal tool prototype
Build one user path in a safe environment, add tests and review every change. Do not connect production secrets or customer data during the first iteration.