Nissan is simultaneously deploying artificial intelligence in its factories to reduce production downtime and worker injuries, and in its vehicles to enable large-scale autonomous driving.
The company believes that AI infrastructure is the fastest path to return to growth.
At its Canton, Mississippi, plant, which employs 3,200 workers and makes Frontier pickups and Altima sedans, Nissan is running an AI system that monitors production in real time and identifies ways for workers to become more efficient and safe.
AI will monitor workers’ movements and alert them to dangerous bending angles before injuries occur, the Clarion Ledger reported on Tuesday (June 9).
“Then we can talk to them and show them the proper angles to use. That way they stay healthy, which is good for them, but at the same time they can continue to work without getting injured,” said Chi Amaechi, a process engineer at Nissan.
The program will be expanded facility-wide, according to the report.
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Work at the factory will take place in parallel with a broader strategic shift. In April, Nissan announced a long-term vision called “Mobility Intelligence for Everyday Life.” Over the long term, Nissan aims to equip 90% of its vehicle lineup with AI-based driving assistance technology, and to equip its new luxury minivan Elgrand with next-generation autonomous driving functions by the end of fiscal 2027.
2 questions, 1 bet
Unplanned downtime and workplace injuries are two of the most direct cost drivers in large-scale manufacturing. When a line goes down at a plant like the one in Mississippi, which produces 400,000 vehicles a year, there is an immediate loss of production, measured in vehicles per hour. Worker injuries result in lost workforce, workers’ compensation costs, and regulatory risks.
Nissan’s AI system implementation reflects how broadly the company is applying the technology across its business.
Nissan and Asserta partnered in 2022 to develop an AI tool designed to predict engine component failures. Predictive maintenance applications flag equipment stress before failure occurs.
What is new is the use of cameras to monitor worker movement to identify ergonomic risks in real time rather than after an injury has occurred. The program has been a success and the company plans to expand it, the Clarion Ledger reported.
Vehicle AI strategy
According to an April announcement, Nissan’s Mobility Intelligence Vision puts AI at the center of the company’s product roadmap in two ways. Nissan AI Drive targets self-driving capabilities, and the Elgrand minivan will be equipped with next-generation ProPILOT with end-to-end self-driving technology by the end of 2027. Nissan AI partners target in-car experiences that connect the vehicle and the driver’s daily life through context-aware assistance.
Additionally, Nissan partnered with Uber and London-based AI startup Wave in March, and received more than $1 billion in funding in February from SoftBank, Nvidia, Microsoft, Uber, Nissan, Mercedes-Benz, Stellantis and others. The two companies plan to develop robotaxis in Japan and pilot them in Tokyo by the end of 2026. In the first phase, the vehicles will have a safety driver present and will operate on the Uber network.
In December, Nissan became the first global automaker to commit to integrating Wayve’s AI directly into production vehicles.
recovery context
The AI push is also a reversal. In its financial results announcement last month, Nissan reported a net loss of 533.1 billion yen (approximately $3.3 billion) for fiscal 2025, with global sales of 3.15 million vehicles.
In May 2025, Nissan announced a restructuring plan that includes consolidation of vehicle production plants and personnel reductions. During the financial results announcement, Nissan CEO Ivan Espinosa said the company is making progress in its financial performance according to plan.
“At the same time, we have set a long-term direction for mobility intelligence for everyday life,” he said. “We are moving beyond recovery and into a growth phase.”
Nissan aims to sell 1 million vehicles a year in the U.S. by fiscal year 2030, up from 926,000 in calendar year 2025, Bloomberg reported in April. There are also plans to reduce the number of models from 56 to 45 and rationalize 80% of sales.
Achieving that goal with a leaner model lineup and a restructured cost base makes AI-driven efficiency gains in manufacturing and AI-driven product differentiation on the road the same strategic bet.
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