AI- AND ML-DRIVEN PREDICTIVE QUALITY ORCHESTRATION FOR U.S. HEALTHCARE AND HRM SYSTEMS: ENHANCING TEST INTELLIGENCE, DEFECT FORECASTING, AND COMPLIANCE OPTIMIZATION IN AGILE DEVOPS ENVIRONMENTS

Abstract

This study investigates the impact of AI- and ML-driven Predictive Quality Orchestration (PQO) on enhancing test intelligence, defect forecasting, and compliance optimization within Agile DevOps environments applied to U.S. healthcare and Human Resource Management (HRM) systems. Employing a quantitative, predictive analytical approach, data were collected from five major organizations integrating AI-enabled DevOps practices. Using machine learning algorithms; Random Forest (RF), Long Short-Term Memory (LSTM), and Gradient Boosting Machine (GBM) the study developed and validated predictive models for quality orchestration. Results revealed that GBM achieved the highest predictive performance (accuracy = 94.5%, ROC-AUC = 0.96), while healthcare systems demonstrated superior test coverage, lower defect density, and faster resolution rates compared to HRM systems. Regression analysis confirmed significant positive relationships between AI Model Complexity, Data Quality Index, and Agile Process Maturity with key performance outcomes. Post-implementation, compliance deviation reduced by 61%, and audit readiness improved by 25.9%. These findings underscore that PQO not only improves software reliability and compliance assurance but also establishes a self-learning framework that continuously optimizes performance in critical, regulated environments. The study concludes that integrating AI-driven orchestration into DevOps pipelines is a strategic pathway to achieving sustainable, intelligent, and compliant software ecosystems.

Ähnliche Arbeiten