Schema Volatility Propagation in AI-Driven Data Architecture Pipelines

Abstract

Artificial intelligence-driven data architecture pipelines increasingly operate on heterogeneous and evolving datasets where structural consistency is essential for reliable prediction. Existing studies in medical imaging, microbiology, and public health analytics show that machine learning models are sensitive to schema changes, yet the propagation of these changes across pipeline stages remains poorly understood. This study addresses this gap by examining how schema perturbations spread through preprocessing, feature extraction, encoding, and model inference layers. A formalized pipeline framework with schema perturbation operators and quantitative drift metrics is introduced to evaluate propagation dynamics across representative datasets. Results reveal nonlinear amplification of structural inconsistencies and identify critical transformation stages where errors accumulate most rapidly. The findings demonstrate that schema volatility can significantly degrade predictive performance, particularly in long-lived and compliance-sensitive AI systems. The study highlights the limitations of conventional mitigation approaches and emphasizes the need for schema-aware adaptive architectures capable of real-time detection and correction of structural inconsistencies, improving reliability in healthcare, microbiological surveillance, and telemedicine applications.