98.6% Reduction in Authority Changes, 80% Efficiency in Inquiry Response - The Full Scope of "AI Truly Used on Site" Business Transformation Achieved by Remolabo Revealed
Remolabo, which operates a remote work practice school, has disclosed case studies of business transformation utilizing generative AI and business automation tools. They achieved a 98.6% reduction in authority changes, 80% efficiency in inquiry response, and a 52% reduction in shift adjustment time, accelerating the practical application of AI in the workplace.
📋 Article Processing Timeline
- 📰 Published: April 30, 2026 at 20:00
- 🔍 Collected: April 30, 2026 at 11:32
- 🤖 AI Analyzed: April 30, 2026 at 12:39 (1h 7m after Collected)
Aiming for seamless careers for women, Remolabo Co., Ltd. (Shibuya-ku, Tokyo; Representative Director: Masakuni Saeki; hereinafter 'the Company'), which operates 'Remolabo,' a remote work practice school that supports working styles adaptable to life stage changes, will disclose case studies of business transformation in school operations utilizing generative AI and business automation tools.
■ Why Remolabo's business transformation is different from ordinary DX
Since achieving a 40% reduction in customer support operations using generative AI in 2024, the Company has further expanded its efforts. As of 2026, it has evolved to a phase of redesigning entire workflows with AI, rather than just improving 'parts' of operations.
Typical business efficiency improvements tend to be limited to 'point' improvements, automating specific tasks. However, this alone often leaves the overall flow of operations unchanged, leading to bottlenecks elsewhere. Therefore, the Company aimed for 'line' improvements, redesigning the entire business flow so that information is automatically organized, analyzed, and shared when entered, leading to further improvements. The Company believes that the important thing is not to introduce systems, but to accumulate improvements that are tailored to the actual conditions on site.
Furthermore, many of these measures were developed and implemented by personnel without IT or programming experience, indicating that generative AI has reached a stage where it can achieve practical business improvements even without specialized knowledge.
■ Transformation Cases
◆ Case 1: Authority changes that took approximately 9 business days at each hiring/retirement now take 1 hour.
Workload reduction rate: Up to 98.6% (71.4 hours → 1 hour)
In operations with several hundred staff, each time there was a hiring, retirement, or transfer, tasks involved opening shared folders one by one, determining access rights, and manually entering email addresses to grant or remove permissions. When these tasks accumulated, the person in charge often spent almost several days solely on this work.
After introducing a system that combines AI and Google's automation functions, adding or deleting a single row in a management sheet and pressing a button automatically sets/removes all folder permissions according to affiliation. A list of "who can access which folder" is also automatically updated, preventing forgotten permission removals for retirees and missed grants for new members.
Without significantly changing existing Google Drive operations, the response time per instance was reduced by up to 98.6%. A system was put in place to manage permissions accurately and quickly, where "postponing due to busyness" is not allowed.
◆ Case 2: Shift adjustment time reduced by 52%
27 hours/month → 13 hours/month (approx. 52% reduction)
Remolabo staff come from various backgrounds, and optimal shifts must be created each time under different conditions, such as last-minute changes due to personal circumstances, requests for short working hours, maximum consecutive working days, and balance of skills and experience. The person in charge had to repeatedly "think, adjust, and summarize" for this task, spending 27 hours each month.
Currently, members enter their preferences via Google Form, and an administrator simply presses a button to automatically generate shifts reflecting various conditions. The system also visualizes understaffed times and days and suggests additional adjustment candidates considering skill balance, making it instantly clear "which slots are short and who to ask next."
This improvement maintains the traditional "submission, confirmation, sharing" flow, reducing the burden of handling complexity rather than eliminating complexity itself.
◆ Case 3: Inquiry response efficiency up to 80% - "Escalation Message Creation Bot"
20-30 minutes per case → Up to 80% reduction
Creating reply messages for complex inquiries from students required 20-30 minutes per case, from understanding the situation to structuring the text.
However, now, AI trained on past response history automatically identifies inquiries as "complaint," "consultation," or "question" and instantly searches for similar cases. Since it learns from the responses of excellent staff as knowledge, the AI automatically generates the background investigation, chronological organization, and optimal text structure. This allows the person in charge to complete the task by simply reviewing and correcting the AI-generated draft.
■ Further Expanding Transformation - From "Point" to "Entire Workflow" in Business
In addition to the three cases above, Remolabo is also working on AI-fication of the entire on-site workflow.
◆ Marketing analysis automation
By simply inputting a target URL into internal chat, AI performs competitive and marketing analysis and shares and stores reports. This automated the manual task of collecting "what appeals are currently working."
◆ Cross-searchable videos and interview records
AI cross-searches the content of videos on the site, making it easy to find relevant information within the video content.
■ Why Remolabo's business transformation is different from ordinary DX
Since achieving a 40% reduction in customer support operations using generative AI in 2024, the Company has further expanded its efforts. As of 2026, it has evolved to a phase of redesigning entire workflows with AI, rather than just improving 'parts' of operations.
Typical business efficiency improvements tend to be limited to 'point' improvements, automating specific tasks. However, this alone often leaves the overall flow of operations unchanged, leading to bottlenecks elsewhere. Therefore, the Company aimed for 'line' improvements, redesigning the entire business flow so that information is automatically organized, analyzed, and shared when entered, leading to further improvements. The Company believes that the important thing is not to introduce systems, but to accumulate improvements that are tailored to the actual conditions on site.
Furthermore, many of these measures were developed and implemented by personnel without IT or programming experience, indicating that generative AI has reached a stage where it can achieve practical business improvements even without specialized knowledge.
■ Transformation Cases
◆ Case 1: Authority changes that took approximately 9 business days at each hiring/retirement now take 1 hour.
Workload reduction rate: Up to 98.6% (71.4 hours → 1 hour)
In operations with several hundred staff, each time there was a hiring, retirement, or transfer, tasks involved opening shared folders one by one, determining access rights, and manually entering email addresses to grant or remove permissions. When these tasks accumulated, the person in charge often spent almost several days solely on this work.
After introducing a system that combines AI and Google's automation functions, adding or deleting a single row in a management sheet and pressing a button automatically sets/removes all folder permissions according to affiliation. A list of "who can access which folder" is also automatically updated, preventing forgotten permission removals for retirees and missed grants for new members.
Without significantly changing existing Google Drive operations, the response time per instance was reduced by up to 98.6%. A system was put in place to manage permissions accurately and quickly, where "postponing due to busyness" is not allowed.
◆ Case 2: Shift adjustment time reduced by 52%
27 hours/month → 13 hours/month (approx. 52% reduction)
Remolabo staff come from various backgrounds, and optimal shifts must be created each time under different conditions, such as last-minute changes due to personal circumstances, requests for short working hours, maximum consecutive working days, and balance of skills and experience. The person in charge had to repeatedly "think, adjust, and summarize" for this task, spending 27 hours each month.
Currently, members enter their preferences via Google Form, and an administrator simply presses a button to automatically generate shifts reflecting various conditions. The system also visualizes understaffed times and days and suggests additional adjustment candidates considering skill balance, making it instantly clear "which slots are short and who to ask next."
This improvement maintains the traditional "submission, confirmation, sharing" flow, reducing the burden of handling complexity rather than eliminating complexity itself.
◆ Case 3: Inquiry response efficiency up to 80% - "Escalation Message Creation Bot"
20-30 minutes per case → Up to 80% reduction
Creating reply messages for complex inquiries from students required 20-30 minutes per case, from understanding the situation to structuring the text.
However, now, AI trained on past response history automatically identifies inquiries as "complaint," "consultation," or "question" and instantly searches for similar cases. Since it learns from the responses of excellent staff as knowledge, the AI automatically generates the background investigation, chronological organization, and optimal text structure. This allows the person in charge to complete the task by simply reviewing and correcting the AI-generated draft.
■ Further Expanding Transformation - From "Point" to "Entire Workflow" in Business
In addition to the three cases above, Remolabo is also working on AI-fication of the entire on-site workflow.
◆ Marketing analysis automation
By simply inputting a target URL into internal chat, AI performs competitive and marketing analysis and shares and stores reports. This automated the manual task of collecting "what appeals are currently working."
◆ Cross-searchable videos and interview records
AI cross-searches the content of videos on the site, making it easy to find relevant information within the video content.