One automotive OEM recently avoided $150-$200 million in warranty costs on a single quality issue, not through validation testing before launch, but by surgical detection through fleet telemetry of a high-voltage battery (HVB) cell imbalance, after only 2 consumer claims. The case: across an exposed new EV model population of over 10,000 vehicles, early anomaly detection allowed the OEM to identify the defective VINs and deploy surgical countermeasures rather than a blanket fix on the whole fleet. The result: Over 98% warranty cost avoidance. This session examines how shifting left, from reactive, claims-based monitoring to live digital twin-based agentic quality intelligence (AQI) could help operationalize a systematic. AI and ML driven approach for modern after sales quality teams and enable similar, consistent warranty cost avoidance. Investigations correlates multi-sensorial live data streams with manufacturing metadata, and enables after sales teams to use purpose-built AI Agents to investigate faster and isolate defective vehicles from healthy look-alikes. When it comes to operations, specialized agents generate a ranked hypothesis on likely root causes. Every decision gate has reasoning and probability, which gives engineers the ability to review each hypothesis, validate against physical inspection or bench testing, and make the final call, guided by the operational principle 'AI proposes, engineer disposes.' Actionable Takeaways Standardize signals: Use Covesa VSS, a standardized signal model that gives internal teams and cross-OEM benchmarking a shared vocabulary before building any ML/AI anomaly-detection models on top of it. Unify data foundation across teams: Engineering, warranty, and quality teams often work from fragmented, siloed views of the same vehicle population. Democratizing data is the key to ensuring that AI has the full picture across the board and can give each team the information that is relevant to them within the quality process. Elevate the engineer-in-the-loop: Adopt an operational framework where specialized agents help to proactively automate high-volume forensic analysis and report generation, while engineers retain final authority at all decision gates. Operationalize investigations: Let AI natural language conversations save 1000s of wasted training hours. Engineers know what they are looking for; AI and ML can help them find it quickly. Quantify pre-claim detection impact: Continuous, multi-modal telemetry and live digital twins power AI-driven investigations, cutting root-cause analysis time from months or weeks to days.