How AI improves quality assurance and operational reliability (www.techradar.com)

AI-powered quality assurance is being reframed from a back-office cost into a strategic foundation for trust, compliance and operational resilience. Monty Kothiwale argues that AI-driven QA now underpins high-stakes decisions—from M&A due diligence (flagging hidden technical debt or compliance risks in target systems) to regulated industries like aviation and finance (continuous anomaly detection that prevents scheduling failures, safety risks and regulatory breaches). By acting as “risk insurance,” AI QA can prevent costly retrofits, fines and reputational damage while accelerating innovation and shortening release cycles. Technically, this shift relies on four scalable practices: predictive QA modeling (using historical defect and usage data to forecast failure points), continuous assurance pipelines integrated into CI/CD, risk-weighted prioritization that ranks issues by business impact rather than just severity, and adaptive learning loops that refine models with production data. The approach also extends to ESG and governance—validating sustainability pipelines and auditing automated decision systems—and is evolving toward self-healing systems, explainable QA, cross-border compliance engines and built-in ethics/bias checks. The net effect: QA becomes a proactive, auditable control that enables faster, safer scaling of AI-enabled systems.