
Emuni has published an interview with Sankei Manufatec about using generative AI.
The manufacturer faces the challenge of passing on skilled workers' expertise.
They are using 15 years of technical data—about 5,000 records—in this effort.
What happened
Emuni, a Tokyo-based AI company, published a user interview with Sankei Manufatec, an Osaka-based manufacturer of automotive repair parts, detailing their joint effort to apply generative AI.
Why it matters
The interview addresses a common manufacturing challenge: passing down skilled workers' know-how and tacit knowledge as veteran engineers age. Sankei Manufatec is tackling this by using AI to leverage 15 years of accumulated technical data—about 5,000 records—while also improving AI literacy and developing custom AI.
What to watch
The article outlines a phased approach: starting with AI literacy training, then general-purpose AI for efficiency, and finally developing proprietary AI. It also highlights the outcomes and future outlook of this initiative.
Ask the AI about this article →
The interview highlights a practical response to a pressing issue in Japanese manufacturing: the aging workforce and the risk of losing decades of accumulated expertise. Sankei Manufatec, which produces repair parts for cars, has been collecting technical data for 15 years, amounting to about 5,000 records. Rather than letting that knowledge fade, the company is working with Emuni to build a bridge to the next generation through AI. The approach is deliberately incremental, starting with basic AI literacy so employees are comfortable with the tools, then moving to general-purpose AI for everyday efficiency gains, and eventually building custom AI tailored to the company's own needs. This staged strategy likely reduces resistance and allows lessons learned at each step to inform the next. The published interview is meant to share this journey with other manufacturers facing similar challenges, underscoring Emuni's role in supporting AI adoption in the sector. For a business reader, it offers a concrete example of how an established manufacturing firm is using AI not to replace its veteran workforce, but to capture and reuse what they know.
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