
AI randomly assigns inquiries to balance workload and improve team morale.
1. Issue: Uneven Task Assignment
An IT company uses Augmented Reality (AR) technology to deliver interactive services for education and training. To handle customer inquiries related to their cloud services, the company has a dedicated team made up of one leader and five members. Customers submit their inquiries through a web form, and the team responds via email.
However, the current task assignment system for inquiry responses has become uneven, leading to a sense of unfairness within the team. Members who are assigned a heavier workload complain of being overburdened, while those with fewer assignments express anxiety about not being trusted. This imbalance is creating a negative impact on team morale and overall efficiency.
This sense of unfairness can lead to serious team operational problems. It risks lowering team members’ motivation, decreasing productivity, and increasing the risk of staff turnover. Therefore, it’s urgent to implement measures to address and eliminate this unfairness.
2. Solution: AI-Powered Random Assignment
The team leader, the process owner, realized they were unconsciously assigning a disproportionate number of tasks to veteran members who appeared more experienced and available.
To address this, they’ve decided to eliminate the manual “1. Assign Responder” step, previously handled by the leader. In its place, a new, AI-driven process has been introduced.
Under this new process, “x1. Select Responder by AI,” the AI will randomly choose one email address from the five team members. The member associated with the selected email address will then be automatically set as the responder for the “2. Draft Response” step in the subsequent “x2. Set Responder” stage.
Before




View details of the workflow diagram
s1. Inquiry Reception
The customer initiates an inquiry by filling out a web form, providing their email address and the details of their question.
1. Assign Responder
The team leader decides which member will be responsible for drafting the response to the inquiry.
2. Draft Response
The member chosen in the “Assign Responder” step creates the draft response to the customer’s inquiry.
3. Review
The drafted response from the “Draft Response” step is reviewed. This likely involves checking for accuracy, completeness, tone, and adherence to company guidelines.
2′. Rework Response
If issues are identified during the “Review” step, the response is sent back for revisions based on the feedback.
m1. Send Response
Once the response is finalized (after review and any necessary rework), an email containing the answer is sent to the email address the customer provided in the inquiry form.
After




View details of the workflow diagram
s1. Inquiry Reception
The customer initiates an inquiry by filling out a web form, providing their email address and the details of their question.
x1. Select Responder by AI
From the five team members’ email addresses, the AI randomly selects one. This is the core of the new fairness initiative, removing manual bias from the assignment.
x2. Set Responder
The member corresponding to the email address chosen in “x1. Select Responder by AI” is automatically set as the processor for the “2. Draft Response” step. This ensures a seamless transition from AI selection to task execution.
2. Draft Response
The member chosen in the “Assign Responder” step creates the draft response to the customer’s inquiry.
3. Review
The drafted response from the “Draft Response” step is reviewed. This likely involves checking for accuracy, completeness, tone, and adherence to company guidelines.
2′. Rework Response
If issues are identified during the “Review” step, the response is sent back for revisions based on the feedback.
m1. Send Response
Once the response is finalized (after review and any necessary rework), an email containing the answer is sent to the email address the customer provided in the inquiry form.


3. Customers Case Study
Honda Motor Co., Ltd.
Used for Company-wide Approval Flows such as general affairs applications, training recepti…
View MoreToho Gas Network Co., Ltd. (Negotiation and Design Section)
500 Applications Per Month Completely Digitized! Reduced Labour and Eliminated Printing and…
View MoreMITSUBISHI PENCIL CO., LTD.
Moving Complex Business that had been processed by Notes to the cloud workflow. Approximate…
View More4. Other Business Applications
We will enable AI-based reviews for the following tasks:
Creative Assignments
Deliverable Reviews
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