1. Issue: Growth in client-specific sales data makes changes harder to detect

The accounting department of a cloud services company operated with a three-person team and had already systemized processes ranging from importing accounting data to generating monthly client-specific sales summaries and month-over-month comparisons. These routine operations were running stably, and the aggregated results were regularly shared with executive management and the sales department.

However, as the number of clients increased, the client-specific sales report with month-over-month comparisons grew to several hundred rows. Even when accounting staff manually reviewed the report, it became difficult to determine which clients required closer attention.

In actual monthly review operations, the team could still track sales amounts and month-over-month changes for a few major clients. On the other hand, for medium-sized clients, even significant month-over-month fluctuations tended to be buried within the large report, making it difficult to identify and share important changes with the sales department and executive management.

2. Solution: Use AI to Generate Narrative Explanations of Key Sales Changes

The process owner added an AI agent after the aggregation step so that, in addition to the numerical report, the details of sales changes are output as text.

Specifically, the AI agent was given a prompt to “briefly explain notable sales changes based on the difference data between the previous month and the current month.” This allows the AI to extract major changes and distinctive trends from the difference data. Each time the monthly process is executed, the AI summarizes changes in client-specific sales in narrative form.

AI highlights major client sales changes instantly before reviewing detailed reports.

3. Customers Case Study

4. Other Business Applications

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