Скачать или смотреть How Quality Control Charts Revealed Why AI Visibility Requires Variance Measurement

In 1924, Walter Shewhart proved manufacturing reliability comes from measuring and controlling variance, not eliminating it. Control charts made deviation visible, enabling correction before failures occurred. In 2025, AI operates identically: visibility reliability requires measuring entity signal variance and keeping it within acceptable tolerances.
Key Insights:
Visibility variance measurement tracks entity type consistency, category label alignment, and definition overlap across platforms
The Variance Control Model establishes control limits based on AI classification sensitivity
Betweener Engineering™ treats visibility as a controlled process where measured consistency produces predictable outcomes
Learn how to measure and control visibility variance: TheBlackFridayAgency.com
This article is part of Betweener Engineering™, the canonical AI visibility framework created by The Black Friday Agency to engineer AI-recognizable Category-of-One positioning. Explore the framework at https://betweenerengineering.com.