I designed a four-stage workflow where AI handles extraction and flagging, and the subject matter expert focuses on the judgment calls. The system helps support the human reviewer.
A large content catalog spanning two platforms. The quality audit found that many products, some over a decade old, didn't meet current requirements. Expert review could identify the issues for remediation, but there wasn't enough expert time to review everything.
The AI never replaces the expert. It produces a briefing that helps the expert review only the problems.
The model's review is guided by detailed process instructions and supported by content frameworks and guidelines.
The workflow redraws where human attention goes.
Each expert tailors the evaluation framework to their content area.
An expert without a briefing has to read everything to get a sense of the gaps. With the AI providing an initial review, the expert knows where to review.
Every page looks the same. The expert reads all of it with equal attention, hunting for where problems might be.
The briefing marks what needs scrutiny. The expert's time goes to the key judgment calls.
Scaling development and quality at the same time requires directing expert attention where it is actually needed.
Designed and proposed as a pilot process. Not a shipped implementation with measured results.