Case Study: Housing Industry x AI Advisor - How to Create 'On-Site AI Utilization'
Zept LLC, providing the 'External CAIO' advisory service, has launched a personalized AI training program for Forest Co., Ltd., a homebuilder in Himeji, starting February 2026. By building custom AIs for each employee based on task auditing, they have established a practical model for in-house AI development in the housing industry.
📋 Article Processing Timeline
- 📰 Published: April 25, 2026 at 00:53
- 🔍 Collected: April 24, 2026 at 16:31
- 🤖 AI Analyzed: April 24, 2026 at 22:04 (5h 32m after Collected)
Zept LLC (Himeji City, Hyogo; Representative: Koshi Enoki), provider of the 'External CAIO' AI management advisory service, conducted AI implementation training for Forest Co., Ltd. (Himeji City) starting in February 2026. Simultaneously, they began supporting in-house AI adoption as External CAIO advisors. Through 'personalized AI training for each employee' based on task auditing, they have transformed operations such as SNS management, labor relations, and subsidy applications into 'on-site AI utilization,' releasing a practical model for in-house AI development in the housing industry.
Forest Co., Ltd., a community-based custom homebuilder in Himeji, has started its internal AI implementation measures. Forest is a unique builder that insists on using Himeji-grown timber and the 'WB Construction Method' for earthquake resistance, energy efficiency, and health. They are also active in social and environmental initiatives, such as SDGs and certification as a company promoting women's success.
Zept LLC, also based in Himeji, handled the AI training. Zept provides AI consulting, app development, and web production, with a record of supporting over 230 companies.
The hallmark of this training is its 'personalized design tailored to each employee's work.' By designing AI utilization starting from specific field tasks like SNS and subsidy applications, they achieved a state where AI could be used starting the very next week after training.
■ Background: The Wall of 'On-Site Translation'
In SMEs, especially in the housing and construction industries, the barrier to AI introduction is high. Zept identified three reasons why AI training fails to stick: 1) AI is not used in the field after training, 2) diverse tasks make it hard to prioritize, and 3) security/rules halt progress. The success of AI depends on how well it is 'translated' to field operations.
■ Solution: The 3-Step Personalized Model
1. Task Auditing: Visualizing tasks, time, frequency, and individual dependencies.
2. Individual Curriculum Design: Building custom AIs (My Gem) for each employee.
3. AI Advisory Meetings: Connecting management and the field for continuous support.
■ Specific Implementation Examples
1. SNS Operations (Marketing): Achieved analysis of viral posts and automated generation of scripts, images, and texts, allowing staff to focus on creative strategy.
2. Labor/Subsidies (General Affairs): Transformed complex subsidy requirements into instant AI answers and automated info collection via GAS, resolving individual dependency.
■ Results: Beyond efficiency to 'Structural Change'
This implementation has led to organizational changes: improved organizational durability (less reliance on specific people), standardization of tasks, and creation of strategic time for high-value work.
Representative Koshi Enoki commented: '90% of AI introduction is decided by the "translation to the field." At Forest, we started with task auditing and designed AI for each employee. As a result, it was usable on-site from the week after training. AI should function as a team member, not just a tool.'
Forest Co., Ltd., a community-based custom homebuilder in Himeji, has started its internal AI implementation measures. Forest is a unique builder that insists on using Himeji-grown timber and the 'WB Construction Method' for earthquake resistance, energy efficiency, and health. They are also active in social and environmental initiatives, such as SDGs and certification as a company promoting women's success.
Zept LLC, also based in Himeji, handled the AI training. Zept provides AI consulting, app development, and web production, with a record of supporting over 230 companies.
The hallmark of this training is its 'personalized design tailored to each employee's work.' By designing AI utilization starting from specific field tasks like SNS and subsidy applications, they achieved a state where AI could be used starting the very next week after training.
■ Background: The Wall of 'On-Site Translation'
In SMEs, especially in the housing and construction industries, the barrier to AI introduction is high. Zept identified three reasons why AI training fails to stick: 1) AI is not used in the field after training, 2) diverse tasks make it hard to prioritize, and 3) security/rules halt progress. The success of AI depends on how well it is 'translated' to field operations.
■ Solution: The 3-Step Personalized Model
1. Task Auditing: Visualizing tasks, time, frequency, and individual dependencies.
2. Individual Curriculum Design: Building custom AIs (My Gem) for each employee.
3. AI Advisory Meetings: Connecting management and the field for continuous support.
■ Specific Implementation Examples
1. SNS Operations (Marketing): Achieved analysis of viral posts and automated generation of scripts, images, and texts, allowing staff to focus on creative strategy.
2. Labor/Subsidies (General Affairs): Transformed complex subsidy requirements into instant AI answers and automated info collection via GAS, resolving individual dependency.
■ Results: Beyond efficiency to 'Structural Change'
This implementation has led to organizational changes: improved organizational durability (less reliance on specific people), standardization of tasks, and creation of strategic time for high-value work.
Representative Koshi Enoki commented: '90% of AI introduction is decided by the "translation to the field." At Forest, we started with task auditing and designed AI for each employee. As a result, it was usable on-site from the week after training. AI should function as a team member, not just a tool.'