From data integrity theory to practice: Operationalizing the 2026 data integrity framework with Cribl

The latest research from Precisely and Drexel University’s LeBow College of Business reveals a troubling paradox in enterprise data strategy: while 87–88% of data leaders claim they have the infrastructure, skills, and data readiness needed for AI success, 41–43% simultaneously cite these same elements as their biggest obstacles. This is not a measurement error—it is a confidence–reality gap that threatens billions of dollars in AI investments.