English 
Japanese 
Introducing generative AI to greatly reduce work burden! Pursuing food safety and quality
Daiken Shokai Co., Ltd.
Issues before implementation
- There was strong resistance to AI within the company, with people thinking it was "difficult" or "not relevant to me", and so the introduction of AI did not progress.
- Employee questions were concentrated on specific staff members, resulting in a heavy burden for them to answer.
- The time spent on creating documents such as translations and summaries was so great that there was not enough time to focus on "job design"
- Sales role-playing and preparation for business negotiations were mainly done face-to-face, and it was difficult to respond to unexpected questions.
Effects after implementation
- By improving the AI literacy of all employees, the collection and verification of information on food hygiene management has become faster.
- The mindset of "ask the AI first" has taken root within the company, and inquiries to staff have decreased significantly
- The time required for translation and summarization has been reduced from 10 minutes to 3 minutes, achieving a 70% reduction in time.
- Sales role-playing using generative AI makes it possible to train in a variety of scenarios
table of contents
Company Introduction
Daiken Shokai Co., Ltd. is a food design company that provides multifaceted consulting services, including extending expiration dates and improving factory safety from the perspective of food hygiene.
By comprehensively "designing" food quality, including microbial suppression, suggesting raw materials, and providing guidance on line cleaning, they work with manufacturers and food factories to contribute to the provision of safe food.
Another advantage is that we focus on developing new recipes and conducting inspections and tests, allowing us to propose optimal solutions tailored to the needs of each customer.
A sense of challenge
Before the introduction of generative AI, most of the work was highly dependent on individual staff, and inquiries were concentrated on one person in charge. Language tasks such as translation and summarization took time, and there were times when it was difficult to allocate sufficient resources to the core work of proposing and verifying food hygiene management.
Another issue was that the sales role-playing mainly consisted of face-to-face interviews with hypothetical questions, meaning that new and young employees had limited opportunities to develop the ability to respond to a wide range of questions.
Benefits of using generative AI
Document creation time has been significantly reduced. By entrusting language tasks such as translation and summarization to AI, tasks that previously took 10 minutes can now be completed in 3 minutes, reducing document creation time by approximately 70%.
The burden of responding to inquiries has also been greatly reduced, and as employees have established the habit of "first posting questions on ChatGPT" when they have questions, questions to specific staff members have decreased. As a result, staff members can now focus more on their original duties, such as proposing and verifying food hygiene management.
Furthermore, the quality of the sales role-playing has also improved. Because the generation AI automatically generates anticipated questions and answers for the sales negotiating partner, even new and junior employees can acquire practical sales skills while being exposed to multifaceted questions.
In addition, the training has lowered the barrier to using AI, creating a culture of employees sharing how to use it. AI can now be used to quickly collect and analyze the latest information on food safety and hygiene management, which has ultimately led to improved proposal capabilities.
Future outlook
In the future, the company plans to introduce a catalog search chatbot to further improve operational efficiency. By digitizing the many product catalogs currently stored on paper and using an in-house chatbot linked to a generation AI to enable high-speed searches and responses, it is expected that the company will be able to more quickly propose the most suitable raw materials and sanitary supplies.
Additionally, there are plans to apply this technology to automated ordering systems, in which AI will analyze order information and enter it directly into the system, further reducing the burden of repetitive tasks.
Furthermore, the company is also considering challenging itself with new business models, such as having AI learn from the knowledge it has accumulated in-house about hygiene management, microbial control, and recipe development, and then developing AI consulting services for external parties.
