Abstract: This article examines how artificial intelligence and machine learning reshape automotive manufacturing within Industry 4.0. Reported impacts include up to a 200 percent reduction in costs and a 400 percent gain in production efficiency, with controlled studies showing about a 15 percent improvement from process optimization. The largest early wins appear in quality management through computer vision and continuous inspection, followed by predictive maintenance that cuts unplanned downtime and stabilizes throughput. Supply chain and planning benefit from demand forecasting and inventory optimization that reduce bullwhip and working capital. Adoption barriers remain meaningful, including high initial investment, integration complexity, skills gaps, and trust and explainability requirements in regulated contexts. Effective programs use a common data and MLOps backbone, prioritize short cycle use cases, link model outputs to machine and recipe actions, and track value through OEE, ppm, MTBF, lead time, and service level. The discussion outlines practical steps to scale while noting evidence limitations and the need for standardized reporting on cost of ownership and time to value.

Sara OULED LAGHZAL and EL OUADI Abdelmajid. “Managerial Drivers and Performance Outcomes of AI Adoption in Automotive Manufacturing”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161188

@article{LAGHZAL2025,
title = {Managerial Drivers and Performance Outcomes of AI Adoption in Automotive Manufacturing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161188},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161188},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Sara OULED LAGHZAL and EL OUADI Abdelmajid}
}