China Automotive AI: An Executive Visit Framework

A factory inspection model and an in-vehicle driving feature require different evidence. Structure an automotive AI visit around one workstream at a time, with the responsible engineering or operating team present. A manufacturing demonstration cannot establish road-safety performance, and a vehicle presentation says little about the economics of a plant deployment.

What to evaluate

Workflow trace

Follow one use case from data and model to engineering or operating decision.

Physical integration

Inspect sensors, software, vehicle systems, factories and maintenance dependencies.

Safety governance

Review validation, human authority, updates, incidents and regulatory boundaries.

Transfer economics

Test supplier, infrastructure, market and lifecycle assumptions for the target organisation.

Worked evaluation exercise

The following is an illustrative assessment exercise, not a reported customer result.

For a proposed paint-inspection exercise, compare the system with the current quality process using the same defect categories. Count missed defects, false rejects, rework time and throughput separately. Ask how a new paint colour or lighting change affects the model and who approves recalibration. If the delegation also studies driver assistance, arrange a separate discussion of operating boundaries and intervention records; do not combine those findings into a single undifferentiated AI score.

Questions for the operating team

• Which decision changed because of the AI system?
• How is performance revalidated after a software update?
• Which dependencies would not transfer to another market?

Warning signs

• Vehicle demonstration presented without operating evidence.
• Safety responsibility left with the algorithm.
• Local infrastructure assumptions omitted.

Planning the visit

Use these questions to scope an industry-focused China AI program. Agree which records and operating workflows can be examined before confirming meetings; host participation and access require confirmation. Our research method explains how we structure the brief and follow-up.

References and scope

These references provide policy or risk-management context. They do not independently verify a host's performance or the illustrative exercise above.

• NIST AI Risk Management Framework
• Shanghai AI and manufacturing plan