The AI-Service Warranty Lifecycle Management Think Tank Sponsored Showcase Series:
Transforming reactive quality diagnostics into a continuous, data-driven loop that improves margins, reduces time-to-resolution, and strengthens brand trust.
Vehicle functionality is now driven by software, with updates and improvements introduced in weeks rather than years. Field quality processes are increasingly being challenged to match this pace, as reliance on Repair Orders (ROs) and warranty claims introduces an inherent time lag in how issues are understood and addressed.
This session explores the shift from a “wait-for-failure” model toward a more continuous approach to quality, spanning from production through real-world vehicle operation.
This transition begins with the foundation: data. OEMs generate vast amounts of information across telemetry, warranty, service, and production systems, but it remains fragmented. Establishing a unified, normalized data layer that connects and contextualizes vehicle, component, and service data is the first step toward enabling reliable quality insights.
Only then can AI be applied effectively to help identify patterns, prioritize emerging risks, and make vehicle intelligence accessible across after-sales, quality, engineering, and production teams.
This enables a more effective quality loop:
- Shift from post-claim visibility toward earlier identification of systemic risks
- Accelerate root cause analysis across fleets and components
- Enable targeted countermeasures and feed validated insights back into engineering and production
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