AI in the quality system: from inspection to prediction, and what the standard still requires
Thursday, November 19, 2026 · 13:00 UTC
With Arjun Menon, Sofia Martins and Daniel Weber 
Other time zones
About this session
Machine vision now catches defects a line inspector cannot, anomaly detection flags a drifting process before the control chart does, and generative tools draft procedures, root-cause reports and audit responses in minutes. This session looks at what that has changed in the quality system of a plant and of a service center, and what it has not: the standard still requires a defined process, a competent person accountable for the decision, and evidence that the control worked.
Two cases, one from manufacturing and one from shared services, show where AI moved a quality result and where it created a new risk the audit found first.
What you take away
- Which quality tasks AI has genuinely changed, with the measured effect
- What a quality standard still demands from a human, and how to document it
- A checklist for introducing an AI-assisted control without losing the audit trail
Who it is for
Quality managers, plant heads and service-center leads, and anyone preparing for a surveillance audit with AI tools in the process.
Speakers
Arjun leads quality across four machining plants. Over the last three years his team moved from end-of-line inspection to in-process prediction, cutting customer complaints by 38 percent while keeping IATF 16949 certification intact. He previously ran supplier quality for a Tier 1 automotive group and teaches the statistical process control module in the company academy.
Format
One speaker, one real case, forty-five minutes, then fifteen minutes for your questions. Sessions are free and open to everyone; recordings and materials go to members, and membership is free.
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