A Generative AI-Based Framework for Proactive Quality Assurance and Auditing

Generative Artificial Intelligence (AI) is transforming quality management (QM) and auditing by expanding automation, supporting data-driven decisions, and enabling more personalized stakeholder interaction. However, its adoption also raises concerns related to system robustness, operational resilience, and regulatory compliance, including potential deviations from Critical-to-Quality (CTQ) requirements, gaps in traceability, and misalignment with established quality standards. This paper proposes a structured conceptual framework for proactive, generative AI-enabled QM and auditing, organized into three functional domains: supplier performance, in-process control, and post-market feedback. The framework shows how generative AI can: 1) strengthen supplier oversight via automated documentation and early risk identification; 2) improve in-process control through real-time anomaly detection and Statistical Process Control (SPC)–based triage; and 3) enhance post-market surveillance using predictive analytics for warranty clustering and prioritized Corrective and Preventive Action (CAPA) preparation. To ensure compliance and auditability, the framework incorporates policy-based constraints, human-in-the-loop checkpoints, and end-to-end digital traceability. Verification was performed through a proof-of-concept case study spanning discrete manufacturing and process-based production environments, comparing a conventional quality workflow with a generative AI-augmented alternative. Expert assessment indicated that the generative AI-assisted workflow achieved better performance on key criteria, including documentation completeness, defect detection, process stability, governance and time efficiency. The obtained results suggest that the proposed framework can support a shift from reactive quality control towards predictive and preventive improvement while preserving alignment with quality standards and organizational quality objectives.