Nissan is cutting its vehicle product development time from 55 to 26 months by integrating artificial intelligence and digital tools across its design, testing, and manufacturing phases. The Japanese automaker is adopting accelerated development tactics from its Chinese partner, Dong Feng, to respond faster to shifting market demands and government policies.
The company recently applied this methodology to the Nissan N7 electric vehicle, which launched in 2025. Developed alongside Dong Feng, the N7 required only 26 months to reach the market instead of the traditional five-year cycle. Nissan plans to apply this same rapid development process to up to 90 percent of its new models in fiscal 2026.
Ivan Espinosa, president of Nissan Motor, explained the strategy in a recent interview with Nikkei Asia. "A big part of this is built on AI capabilities and the use of new tools, more digital tools in the design phase, in the testing phase, in the manufacturing phase," Espinosa said.
Standardizing components for speed
To sustain this accelerated pace, Nissan will standardize its chassis and core components. This approach relies on a shared parts pool and a flexible platform adaptable across multiple vehicle segments. The company plans to launch five new SUVs and pickup trucks under its main and Infiniti brands, plus a Mitsubishi Motors OEM production model, by fiscal 2028.
According to Espinosa, Chinese automakers currently dictate the industry's trajectory. "China is as of now setting the industry standards of the future in terms of technology, in terms of cost competitiveness, and in terms of development time," he said. He added that Nissan's next step is to learn from these practices and export that know-how globally.
Applying AI to vehicle intelligence
This shift toward rapid iteration mirrors broader industry trends in AI for Product Development, where teams rely on modular architectures to accelerate time to market. Nissan also intends to upgrade its Intelligent Mobility suite so that up to 90 percent of its models feature autonomous driving and positioning intelligence.
For teams managing similar technological transitions, an AI Learning Path for Product Managers offers frameworks for integrating automation into complex development cycles. Separately, Espinosa addressed the ongoing collaboration with Honda. Talks remain constructive, with both companies meeting regularly to standardize semiconductors and components. Espinosa called this a basic starting point, adding that the scope of their partnership could grow depending on future discussions.
Why this matters for product development professionals
Product teams can no longer rely on five-year development cycles to remain competitive. Nissan's shift proves that integrating digital tools early in the design and testing phases can cut timelines in half. Teams that adopt modular architectures and AI-assisted workflows will dictate market speed, while those clinging to legacy processes will struggle to meet shifting consumer and regulatory demands.
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