{"id":2654,"date":"2026-10-06T12:03:06","date_gmt":"2026-10-06T03:03:06","guid":{"rendered":"https:\/\/digirise.ai\/?post_type=case&p=2654"},"modified":"2026-10-06T15:47:51","modified_gmt":"2026-10-06T06:47:51","slug":"showadosama","status":"publish","type":"case","link":"https:\/\/digirise.ai\/en\/case\/showadosama\/","title":{"rendered":"Meeting minutes reduced from 3 hours to 30 minutes: How a 50-year-old wholesale company doubled its weekly time savings in six months."},"content":{"rendered":"

Showado Co., Ltd. conducted a generative AI training program called "Corporate Reskilling" for 10 employees from March to August 2026. The tool used was Microsoft 365 Copilot. According to the average response to the participant survey, the amount of time saved per week increased from 2.12 hours to 4.67 hours, a 2.2-fold increase. At the results presentation, an example was shared in which the time spent on taking minutes for a single business negotiation was reduced from 3 hours to 30 minutes.<\/p>\n\n\n\n

In the first half of the training, the amount of time saved per week stagnated at around 1-2 hours. But it started to pick up in the second half. From "learning" to "making it a part of everyday life"\u2014we'll show you how a 50-year-old wholesale company that delivers confectionery and general merchandise to the three prefectures of the Hokuriku region managed to make it a permanent part of their operations.<\/p>\n\n\n\n

Company Introduction<\/h2>\n\n\n
\n
\"\u682a\u5f0f\u4f1a\u793e\u662d\u548c\u5802<\/figure>\n<\/div>\n\n\n

Showado Co., Ltd.December 1975 (Showa 50)<\/strong>This wholesale company was founded in Fukui City. In addition to wholesaling and retailing confectionery, food products, beverages, general merchandise, stationery, and alcoholic beverages, it also operates a beauty business. Besides its head office in Fukui, it has offices in Ishikawa (Nonoichi City) and Toyama (Toyama City), and its business area covers the three prefectures of the Hokuriku region. It has 90 employees (as of December 2024). It celebrated its 50th anniversary in December 2025.<\/p>\n\n\n\n

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Showado Co., Ltd. Official Website (www.showado.jp<\/a>)<\/figcaption><\/figure>\n\n\n\n

The company's stated mission is"Bringing a little happiness to 'playgrounds' around the world."<\/strong>We define all places where people gather seeking a playful spirit\u2014amusement arcades, amusement facilities, after-school programs, schools, and various events\u2014as "playgrounds," and our mission is to deliver happiness to each and every person through the prizes offered there.<\/p>\n\n\n\n

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The company defines "prizes" as "everyday rewards" that create small moments of happiness.www.showado.jp<\/a>)<\/figcaption><\/figure>\n\n\n\n

Each and every product we handle"Prize"<\/strong>\u2014These are "everyday rewards" that create small moments of happiness. Therefore, there are many types of products, and tasks such as registering products in the master data, managing inventory, and estimating gift sets occur on a daily basis. The participants in this training were employees from the sales department, sales division, product division, logistics division, and management division\u2014each department responsible for this work.<\/p>\n\n\n\n

Issues before introducing generative AI<\/h2>\n\n\n\n

The purpose of introducing the training program was to accelerate the improvement of operational efficiency throughout the company. Management expressed a desire to "accelerate the pace of improving operational efficiency within the company" and "make sales analysis easier to understand," while employees on the ground requested "to promote overall company efficiency and increase overall resources."<\/p>\n\n\n\n

In the work inventory conducted at the start of the training, we identified the tasks of the 12 people who filled out the sheets, and the results were as follows:1,211.5 hours per month (302.9 hours per week over a 4-week period)<\/strong>We've gained a clearer picture of the workload. Time was concentrated in the following areas: sales, product and quotation administration (39.5 hours per week), general administrative tasks (32.5 hours per week), email and inquiry handling (32.2 hours per week), document and material creation (24.1 hours per week), and data aggregation and analysis (22.4 hours per week).<\/p>\n\n\n\n

However, even after the training began, the application of the skills learned did not increase significantly. The average weekly reduction in time was 1.36 hours for the first workshop, 2.00 hours for the second, 1.14 hours for the third, and 2.12 hours for the fourth. This indicates that the skills learned were not being fully applied to practical work, and progress stalled at around 1-2 hours per week.<\/p>\n\n\n\n

The specific tasks that were most burdensome are reflected in the participants' descriptions. "There were many omissions and duplicates because I had to enter each item every time" (weekly shift schedule), "There were errors and omissions in the information on registration request forms and quotations, and it was time-consuming to check JAN codes and product names" (product master registration), "I couldn't create it because I couldn't write VBA at all" (data import sheet). As is typical for a wholesale business, a lot of time was being consumed by preparing forms and master data.<\/p>\n\n\n\n

Implementation details - A unique initiative by Showado Co., Ltd.<\/h2>\n\n\n\n
  • <\/span>A 6-month program consisting of 9 sessions.<\/strong>
    The program begins with a startup meeting, followed by beginner training, four workshops, a follow-up meeting, and two practical application support meetings. The structure is designed to support participants not just during the learning phase, but throughout the entire process of using the skills in their work.<\/li>
  • <\/span>Theme setting starting with a business process inventory.<\/strong>
    We didn't start with "What can AI do?" but rather with "Which tasks are taking up the most time?" We used the manual tasks of reconciliation, inventory management, and aggregation that we observed during inventory counts as the subject matter for our workshop.<\/li>
  • <\/span>Creating an environment where Copilot can be used within business applications.<\/strong>
    In May 2026, midway through the training program, an environment was established where Copilot could be used within Word, Excel, PowerPoint, and Teams. Participants could immediately apply what they learned in the workshops to their daily work. Incidentally, the third workshop received the highest evaluation of all four sessions.<\/li>
  • <\/span>Departmental work tailored to the wholesale industry<\/strong>
    In the first support meeting, participants were divided into departments and sections (Sales Department, Sales Section, Product Section, Logistics Section, and Administration Section) and worked with Copilot in Excel to cover topics such as "matching two lists," "inventory reorder point alerts," and "member management tables." Procedure manuals, departmental prompt collections, and industry case studies were also distributed.<\/li>
  • <\/span>Turn it into an asset for the company at the results presentation.<\/strong>
    The second support meeting focused on presenting results. Eight participants presented slides detailing how they use the system in their daily work, including challenges, application methods, results, and actual prompts. Individual innovations were shared on the spot, becoming valuable company assets.<\/li><\/ul>\n\n\n\n

    Benefits of using generative AI<\/h2>\n\n\n\n

    Among the examples shared at the results presentation, the biggest change was seen in the sales department's meeting minutes. By recording meetings, using AI to create minutes, and then reviewing and checking for any missed tasks before the next meeting, the time required to create a single minute has been reduced from three hours to just 30 minutes.<\/p>\n\n\n\n

    Time required per meeting to create meeting minutes<\/span>The value presented by the person at the results presentation.<\/span><\/div>
    BEFORE<\/div>
    3 hours<\/div><\/div><\/div>
    AFTER<\/div>
    half an hour<\/div><\/div><\/div>
    about 83%<\/span><\/div>
    Reduction rate per meeting minute<\/div><\/div>
    Approximately 2.2double<\/span><\/div>
    Increase in weekly reduction in hours<\/div><\/div>
    8given name<\/span><\/div>
    Number of people who presented case studies at the results presentation meeting<\/div><\/div><\/div>
    Total weekly time savings from the 6 respondents<\/span>28time<\/span><\/span>(Average of 4.67 hours per person)<\/span><\/div><\/div><\/div>\n\n\n\n

    In the same individual's case, proposal creation time decreased from 5 hours to 2 hours (a reduction of 60%), and email content creation time was reduced to about half of what it was previously. Across all participants, the average weekly reduction in time increased from 2.12 hours to 4.67 hours, and the number of AI-assisted tasks increased from 2.12 to 2.67. Looking at the average for respondents,The increase in time saved per task (approximately 1.0 hour to approximately 1.75 hours) is greater than the increase in the scope of work (approximately 1.3 times).<\/strong>That has been the defining characteristic of the past six months. It appears that not only has the number of usable tasks increased, but the effectiveness of each individual task has also improved.<\/p>\n\n\n\n

    Changes in key performance indicators (average for respondents at each point in time)<\/p>

    Time saved per week<\/div>
    2.12 hours<\/span>→<\/span>4.67 hours<\/span><\/div>
    Approximately 2.2 times<\/span><\/div><\/div>
    Number of jobs using AI<\/div>
    2.12 items<\/span>→<\/span>2.67 items<\/span><\/div>
    Approximately 1.3 times<\/span><\/div><\/div>
    Time saved per task<\/div>
    Approximately 1.0 hour<\/span>→<\/span>Approximately 1.75 hours<\/span><\/div>
    Approximately 1.75 times<\/span><\/div><\/div><\/div>\n\n\n\n

    The application of the technology has spread to each department.<\/h3>\n\n\n\n
    Department<\/th>Tasks now handled by AI<\/th><\/tr><\/thead>
    Sales Department<\/td>Meeting minutes\/meeting notes\/proposal refinement\/customer email wording<\/td><\/tr>
    Management section<\/td>Draft structure of approval documents \/ Legal review and correction of inconsistencies in contract terminology \/ VBA generation \/ Business process flow charts, role assignment charts, and procedure manuals<\/td><\/tr>
    Product section<\/td>Investigating and verifying JAN codes and product information during product master registration \/ Converting scanned paper documents into instruction manuals<\/td><\/tr>
    Sales Department<\/td>Automatic inventory list retrieval (XLOOKUP) \/ Creation of promotional posters<\/td><\/tr>
    Logistics section<\/td>Automating the entry of weekly shift schedules<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    While document creation dominates the top rankings, the inclusion of product master data, inventory lists, and shift schedules is a result unique to the wholesale industry. The scope of application is expanding from standardized document creation to industry-specific specialized tasks.<\/p>\n\n\n\n

    The frequency of use has also changed. At the time of the second support meeting for users,Four out of six respondents use Copilot at least once a week.<\/strong>Two of them answered that it is "essential for work every day."<\/p>\n\n\n\n

    * The figures are self-reported values from the participants. The weekly reduction in time and the number of tasks utilizing AI are the averages from the 4th workshop (8 respondents) and the AI utilization support meeting \u2461 (6 respondents). Since the respondents were not the same, the figures are presented as comparisons between group averages. The time required for meeting minutes, proposals, and emails are the values presented by the individuals themselves at the results presentation. The task inventory data is based on 12 participants as of May 2026.<\/div>\n\n\n\n

    Looking at the flow of events over the course of a day, the changes are as follows:<\/p>\n\n\n\n

    Before implementation | My day was filled with creation work.<\/div>
    • <\/span>I'm transcribing the meeting minutes from scratch, reviewing the audio recordings and notes.3 hours<\/b>)<\/li>
    • <\/span>You put together the proposal yourself, from the structure to the writing.5 hours<\/b>)<\/li>
    • <\/span>The weekly shift schedule requires each item to be entered each time, which can lead to omissions or overlaps.<\/li>
    • <\/span>Registering products in the product master involves checking and verifying each item individually, including its JAN code and product name.<\/li><\/ul><\/div>
      After implementation | Time spent creating changes to time spent making decisions.<\/div>
      • <\/span>The recording is converted into meeting minutes using AI, and a review and task omission check are performed before the next meeting.half an hour<\/b>)<\/li>
      • <\/span>Improve the accuracy of the structure and writing by working with AI.2 hours<\/b>)<\/li>
      • <\/span>By engaging in dialogue, we transform it into an automated input format.No more omissions or overlaps.<\/b><\/li>
      • <\/span>The information is presented with reference sites, making it easy to verify.The number of confirmed cases itself has increased.<\/b><\/li><\/ul><\/div><\/div>\n\n\n\n

        These changes are also reflected in the presentation materials from the results presentation meeting. First, let's look at the changes in those who had no experience writing functions.<\/p>\n\n\n\n

        Since I wasn't very familiar with functions, I created a dialogue-based explanation of what I wanted to do with the AI, and then modified the parts that needed automatic input. This eliminated omissions and duplicates, improving efficiency and accuracy.<\/p>From the presentation materials of the results presentation meeting<\/cite><\/blockquote>\n\n\n\n

        The report wasn't so much about learning how to use the tools, but rather about reaching areas that were previously inaccessible. The same kind of change is happening on the document creation side as well.<\/p>\n\n\n\n

        We were able to transform improvement plans that had previously been stuck in the conceptual stage into a state where they could be considered and proposed.<\/p>From the presentation materials of the results presentation meeting<\/cite><\/blockquote>\n\n\n\n

        They said they were now able to transform the improvement ideas they had in their heads into formal proposals, workflow diagrams, role assignment charts, and requirements lists. And after six months, their entire approach to AI had changed.<\/p>\n\n\n\n

        AI is like a "thinker for you." Combining precise instructions with final confirmation improves both quality and speed.<\/p>From the presentation materials of the results presentation meeting<\/cite><\/blockquote>\n\n\n\n

        "AI is the main player in the work, but 'verification and correction' is the new bottleneck for humans"\u2014this was summarized at the results presentation. The next point of discussion is not how to leave it to AI and be done with it, but how to reduce the time spent on verification. Perhaps the greatest achievement is that we have come this far in just six months.<\/p>\n\n\n\n

        Future outlook<\/h2>\n\n\n\n

        Now that we are beginning to see results, the next point of discussion is to bridge the gap in individual utilization and create a system for widespread adoption. In the final report, we share the following points:<\/p>\n\n\n\n

        • <\/span>It plays a role in promoting AI.<\/strong>
          At the results presentation, members of the administrative department presented business design-level results such as business process flow diagrams and requirements lists. One option is to have such individuals take the lead and conduct monthly internal sharing meetings and update the prompt collection.<\/li>
        • <\/span>Individualized support tailored to your usage<\/strong>
          While some people use it every day, others only use it a few times a month. The next step will be providing individualized support to help them find opportunities to use it in their specific tasks.<\/li>
        • <\/span>Designing operations for on-site and logistics systems.<\/strong>
          The most time-consuming area in our business process inventory was field work and travel (71.3 hours per week). Since AI cannot handle this area alone, we should start by creating procedure manuals and organizing records.<\/li>
        • <\/span>How to deal with the next obstacle<\/strong>
          "What kind of prompts will produce good results?", "We're dealing with a huge amount of data and we're having trouble managing it," "What should we do first when creating product catalogs that need to be updated every month?". The nature of the challenges is shifting from "We don't understand AI" to "We want to change this process in this way."<\/li><\/ul>\n\n\n\n

          Through a six-month initiative, Showado Co., Ltd. progressed from the stage of "learning" generation AI to the stage of "using it in daily operations." One of the results achieved during this process is that meeting minutes were reduced from three hours to 30 minutes, and the average weekly time saved by respondents increased by approximately 2.2 times.<\/p>\n\n\n\n

          "Delivering small moments of happiness to 'playgrounds' around the world"\u2014supporting this mission are the unseen daily tasks, such as product master data, inventory lists, and shift schedules. Now, 50 years after its founding, the company is entering the next stage with the addition of a new tool: generative AI.<\/p>","protected":false},"featured_media":2652,"template":"","meta":{"_acf_changed":false},"implementation":[9],"employees":[25],"location":[115],"industry":[97],"class_list":["post-2654","case","type-case","status-publish","has-post-thumbnail","hentry","implementation-re-skilling","employees-25","location-115","industry-97"],"acf":[],"_links":{"self":[{"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/case\/2654","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/case"}],"about":[{"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/types\/case"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/media\/2652"}],"wp:attachment":[{"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/media?parent=2654"}],"wp:term":[{"taxonomy":"implementation","embeddable":true,"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/implementation?post=2654"},{"taxonomy":"employees","embeddable":true,"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/employees?post=2654"},{"taxonomy":"location","embeddable":true,"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/location?post=2654"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/digirise.ai\/en\/wp-json\/wp\/v2\/industry?post=2654"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}