Beyond the Data Lake: Bridging the Tacit Context Gap in AI-Driven Decision Making

Drawing on recurring operational failure patterns across heavy industry, process manufacturing, advanced logistics, and global supply chains: confusing an abundance of digitized telemetry with a complete representation of physical business reality. Drawing on recurring operational failure patterns across heavy industry, process manufacturing, advanced logistics, and global supply chains, this article outlines the "Tacit Context Gap", the critical divide between algorithmic pattern matching and unmeasured aspects of operational reality. To reduce the risk of failures arising from correlation fallacies, sensor boundary limits, and accountability voids, executive teams must move past the "Data as Truth" mindset. This paper presents a Four-Pillar Decision Model and an operational playbook that grounds machine intelligence in physical first principles, tacit field context, and accountable human governance.