Nissan Turns Factory AI Into a Comeback Strategy

Nissan is deploying artificial intelligence simultaneously inside its factories to cut production downtime and worker injuries, as well as inside its vehicles to reach autonomous driving at scale.

The company bets that AI infrastructure is the fastest path back to growth.

At its Canton, Mississippi, plant, which employs 3,200 workers and builds the Frontier pickup and Altima sedan, Nissan runs AI systems that monitor production in real time and identify ways for employees to be more efficient and safe.

AI monitors worker movement and flags dangerous bending angles before injuries occur, the Clarion Ledger reported Tuesday (June 9).

“We can then talk with them and show them the proper angle to use, and that keeps them healthy, which is good for them, but it also allows them to stay at work without getting injured,” Nissan Process Engineer Chi Amaechi said, per the report.

The program is expected to expand across the facility, the report said.

The factory work runs alongside a broader strategic shift. In April, Nissan announced a long-term vision called “Mobility Intelligence for Everyday Life,” targeting AI-enabled driver assistance technology in 90% of its vehicle lineup long term, as well as next-generation autonomous driving capability for its new Elgrand luxury minivan by the end of fiscal year 2027.

Two Problems, One Bet

Unplanned downtime and workplace injuries are two of the most direct cost drivers in large-scale manufacturing. A line stoppage at a plant that produces 400,000 vehicles annually, like the one in Mississippi, carries an immediate production loss measured in vehicles per hour. Worker injuries generate lost labor, workers’ compensation costs and regulatory exposure.

Nissan’s AI system deployment reflects how broadly the company is applying the technology across its operations.

Nissan and Acerta partnered in 2022 to develop an AI tool designed to anticipate engine component failures. The predictive maintenance application flags equipment stress before it causes failure.

What’s newer is the worker movement monitoring, which uses cameras to identify ergonomic risk in real time rather than after an injury occurs. The program has been a success, and the company expects to expand it, the Clarion Ledger reported.

The Vehicle AI Strategy

Nissan’s Mobility Intelligence vision puts AI at the center of the company’s product roadmap in two ways, according to its April announcement. Nissan AI Drive targets autonomous driving capability, with the Elgrand minivan set to carry next-generation ProPILOT with end-to-end autonomous technology by the end of fiscal year 2027. Nissan AI Partner targets the in-cabin experience, connecting the vehicle to a driver’s daily routine through context-aware assistance.

Additionally, Nissan partnered in March with Uber and London-based AI startup Wayve, which was backed in February by more than $1 billion in funding from SoftBank, Nvidia, Microsoft, Uber, Nissan, Mercedes-Benz, Stellantis and others. The companies will develop robotaxis in Japan, with a pilot deployment in Tokyo by late 2026. The vehicles will operate on the Uber network with safety drivers present during the first phase.

Nissan became the first global automaker to commit to embedding Wayve’s AI directly into production vehicles in December.

The Recovery Context

The AI push is also a turnaround play. During an earnings announcement last month, Nissan reported a net loss of 533.1 billion yen (about $3.3 billion) for fiscal year 2025, with global sales totaling 3.15 million units.

In May 2025, Nissan announced a restructuring plan that included consolidating its vehicle production plants and reducing its workforce. During the earnings announcement, Nissan CEO Ivan Espinosa said the company is seeing progress in its financial performance under the plan.

“At the same time, we set our long-term direction with Mobility Intelligence for everyday life,” he said. “We have moved beyond recovery and are entering a phase of growth.”

Nissan aims to sell 1 million vehicles annually in the United States by fiscal year 2030, Bloomberg reported in April, up from 926,000 units in calendar year 2025. It also plans to reduce the number of models from 56 to 45 and streamline 80% of its volume.

Reaching that target 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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