ENEOS Materials, a division of the ENEOS group in Japan, deployed ChatGPT Enterprise to tackle staff shortages and boost productivity across its plants. The adoption moved from pilot projects to widespread daily use among employees, turning the tool into a regular aid for design, research, and training.
What ENEOS Materials did
The initiative started with a volunteer team that first wanted to master the tool and then roll it out across the company. ENEOS Materials chose ChatGPT Enterprise for its security controls and the reliability of its outputs — key requirements when working with proprietary manufacturing information. The official OpenAI post outlines this process and dates the notice September 24, 2025. (openai.com)
"ChatGPT became a partner for every employee," the company says after expanding the platform across the organization. (openai.com)
Measurable productivity results
Numbers are direct and useful if you're wondering whether AI brings real benefits. During and after the pilot they reported:
- 80% of employees said their workflow improved significantly.
- A 90% reduction in time for HR data aggregation and analysis.
- Research tasks that used to take months are now solved in minutes.
Those results come from the adoption report OpenAI published about the ENEOS Materials case. (openai.com)
Practical cases inside the company
Technical research and language barriers
At a plant in Hungary, the team used Deep Research to find and translate local content that previously took months to analyze. Complex questions and calculations that consumed half a day are now resolved in minutes, speeding up R&D decisions. (openai.com)
Plant design and safety
Engineering created a custom GPT that generates design specifications from data like fluid type, flow rate, and pipe diameter. That made it possible to identify corrosion risks and design baselines in seconds, increasing safety and cutting repetitive manual work in engineering projects. (openai.com)
Training and learning analysis
HR automated training feedback analysis with a custom GPT. Tasks that used to take 1–2 hours now finish in 20 seconds, and data aggregation dropped by about 90%. Even employees without prior programming experience could build internal tools with help from the platform. (openai.com)
Why this matters for other companies
If you work in a factory, run an industrial SME, or lead operations, the main lesson is that AI can democratize specialized tasks. It's not just about automation — it's about enabling more people to solve technical problems without being expert software engineers.
The rapid adoption at ENEOS Materials highlights two practical points:
- Ease of use: describe what you need in natural language and get useful results.
- Control and security: choosing an enterprise version that meets internal requirements for sensitive data.
That lowers the barrier for plant, maintenance, or quality teams to try solutions without big IT projects.
Looking ahead
ENEOS Materials plans to extend AI beyond ChatGPT, integrating internally trained models into teams and exploring natural-language control on the plant floor. That vision points to everyday interaction with machines, similar to how we now use conversational assistants. (openai.com)
If you're wondering whether you should try something similar in your company, think of three practical steps: identify a high-impact, low-complexity case, validate it with a small team, and measure real gains in time and safety. ENEOS Materials' story isn't just a tech achievement — it's a how-to for bringing AI into operations without empty promises, as long as there's governance and clear criteria for sensitive data.
