Automotive manufacturers are moving artificial intelligence and machine learning deeper into production, with early users reporting gains in uptime, throughput and productivity.
A new whitepaper prepared by the Center for Automotive Research, a Michigan.-based non-profit automotive research organization, with Rockwell Automation, a Milwaukee-based industrial automation and digital transformation company, examines how AI, machine learning and automation are being used across automotive, tire and battery manufacturing.
Manufacturers reported unplanned downtime reductions of up to 50% in select applications. The whitepaper also cites overall equipment effectiveness improvements of about 5% and throughput gains of 5% to 7% from real-time production analytics.
“Manufacturers are being asked to do more with less while managing greater complexity,” said James Glasson, vice-president, global industry — automotive, tire and advanced mobility at Rockwell Automation. “The combination of automation and AI is helping teams identify issues earlier, reduce downtime and improve performance across plants. The difference now is how effectively companies scale these capabilities.”
Automotive manufacturers already use extensive automation in stamping, body, paint, welding and final assembly. Newer applications are moving into electronics assembly, validation, production coordination, logistics, predictive maintenance and in-line inspection.
Mixed production of internal combustion, hybrid and battery-electric vehicles has increased the number of production variables on assembly lines. Higher electronics content has also added calibration steps, validation requirements and potential failure points.
AI-enabled vision systems are being used for paint and surface inspection, automated electronics validation, in-line anomaly detection on body panels and traceability systems that connect process parameters to specific vehicle or component builds.
According to the whitepaper, manufacturers with full traceability can identify the root cause of a field quality issue and the affected vehicle population in hours rather than weeks.
The supplier findings show an uneven field. Large Tier 1 suppliers with global operations and dedicated manufacturing engineering resources are generally further along in deployment. Mid-sized and smaller suppliers face a more difficult path.
The gap between what leading automakers expect and what many suppliers can currently deliver is described as “observable and growing.”
Some automakers are also including automation capability in sourcing decisions for select commodity categories, alongside cost, quality history and capacity.
Autoliv, a Stockholm, Sweden-based automotive safety supplier that makes airbags, seatbelts and related safety systems, is cited as one example of sustained automation investment. Its direct labour productivity improvement rose from about 4% in 2023 to more than 8% in 2024 and more than 9% in 2025. Durable goods manufacturing productivity growth was 2.7% in 2025.
