{"id":164907,"date":"2026-09-12T17:42:03","date_gmt":"2026-09-12T10:42:03","guid":{"rendered":"https:\/\/bap-software.net\/?post_type=knowledge&p=164907"},"modified":"2026-09-13T20:33:38","modified_gmt":"2026-09-13T13:33:38","slug":"__trashed","status":"publish","type":"knowledge","link":"https:\/\/bap-software.net\/en\/knowledge\/__trashed\/","title":{"rendered":"\u3010AI in Manufacturing\u3011If the Most Experienced Person in Your Factory Leaves Tomorrow, How Much of Their Knowledge Can AI Preserve?"},"content":{"rendered":"

<\/p>\n

What if the most experienced person in your factory leaves tomorrow?<\/p>\n

Imagine an engineer with 20 years of experience resigning.<\/p>\n

A production line breaks down, a machine starts showing abnormal signs, or a defect that occurred years ago suddenly reappears. The manual may not be enough to explain exactly how to handle the situation.<\/p>\n

In these moments, companies often have to \u201cask the veteran.\u201d<\/strong><\/p>\n

But what happens if that person is no longer at the factory?<\/p>\n

Image<\/a><\/p>\n

The problem is not simply losing one employee. A company may also be losing decades of experience stored in one person\u2019s mind.<\/p>\n

I. Where Does Your Factory\u2019s Knowledge Actually Live?<\/h1>\n

Not all knowledge is documented in SOPs or manuals.<\/p>\n

A significant amount of expertise is accumulated through years of hands-on experience:<\/p>\n

    \n
  • How to troubleshoot machine failures.<\/li>\n
  • Warning signs that appear before a breakdown.<\/li>\n
  • Root causes of defects.<\/li>\n
  • How to adjust machines under different circumstances.<\/li>\n
  • Incidents that happened in the past but were never documented.<\/li>\n
  • Practical knowledge that only experienced employees know.<\/li>\n<\/ul>\n

    This is known as Tacit Knowledge<\/strong>.<\/p>\n

    The challenge is that tacit knowledge is extremely difficult to transfer and can disappear when experienced employees leave the company.<\/p>\n

    II. How Much Can AI Replace in Manufacturing?<\/h1>\n

    The answer is: there is no fixed percentage.<\/strong><\/p>\n

    AI cannot become an engineer with decades of hands-on experience overnight. However, AI can help companies digitize and make accessible part of the knowledge that previously existed only in people\u2019s minds.<\/p>\n

    For example, a company can bring together:<\/p>\n

    Manuals + SOPs + incident reports + maintenance records + quality reports + troubleshooting history<\/strong><\/p>\n

    \u2193<\/p>\n

    AI Knowledge Base<\/h3>\n

    When an employee asks:<\/p>\n

    \n

    \u201cMachine A is experiencing Error X. Have we encountered a similar case before?\u201d<\/p>\n<\/blockquote>\n

    AI can search the available data and provide similar cases, possible causes, previous solutions, and relevant documents.<\/p>\n

    AI does not replace engineers. AI makes engineers\u2019 experience more accessible to the entire organization.<\/strong><\/p>\n

    \"\"<\/p>\n

    III. From \u201cAsk the Veteran\u201d to \u201cAsk AI\u201d<\/h1>\n

    Traditional model:<\/p>\n

    New employee \u2192 Doesn\u2019t know \u2192 Asks senior employee \u2192 Senior provides guidance<\/strong><\/p>\n

    AI-enabled model:<\/p>\n

    New employee \u2192 Asks AI \u2192 AI searches knowledge \u2192 Suggests possible actions \u2192 Human verifies<\/strong><\/p>\n

    This can be particularly valuable when companies face:<\/p>\n

      \n
    • Labor shortages.<\/li>\n
    • Experienced employees approaching retirement.<\/li>\n
    • Difficulties training new employees.<\/li>\n
    • Knowledge scattered across multiple systems.<\/li>\n
    • Long search times when troubleshooting issues.<\/li>\n<\/ul>\n

      Of course, decisions related to safety, quality, and actual operations still require human verification and accountability.<\/p>\n

      IV. Where Should a Factory Start with AI?<\/h1>\n

      Companies do not necessarily need to launch a large-scale AI project from day one.<\/p>\n

      A better approach may be to start with a specific use case:<\/p>\n

      Step 1: Collect Data<\/h3>\n

      Manuals, SOPs, incident reports, maintenance records, quality reports, etc.<\/p>\n

      Step 2: Build an AI Knowledge Base<\/h3>\n

      Allow employees to search for information using natural language.<\/p>\n

      Step 3: Validate<\/h3>\n

      Let engineers and domain experts verify AI-generated results.<\/p>\n

      Step 4: Measure<\/h3>\n

      Track improvements in information search time, troubleshooting time, and new employee training.<\/p>\n

      The solution can then be expanded into AI Agents, Predictive Maintenance, Quality Inspection, or AI \u00d7 MES\/ERP<\/strong>.<\/p>\n

      Suggested Internal Links:<\/strong><\/p>\n

        \n
      • AI Agents for Enterprise<\/li>\n
      • MES \u00d7 AI<\/li>\n
      • Manufacturing DX<\/li>\n
      • AI Quality Inspection<\/li>\n<\/ul>\n

        V. BAP Helps Manufacturing Companies Apply AI<\/h1>\n

        BAP Solution Japan provides AI\/DX and software development solutions<\/strong>, supporting companies from PoC to development and operation<\/strong>.<\/p>\n

        Potential applications include:<\/p>\n

          \n
        • Generative AI<\/li>\n
        • AI Knowledge Search<\/li>\n
        • AI Agents<\/li>\n
        • AI \u00d7 Existing Systems<\/li>\n
        • AI \u00d7 OCR<\/li>\n
        • AI \u00d7 Data<\/li>\n
        • AI for Manufacturing<\/li>\n<\/ul>\n

          Instead of starting with a complex system, BAP can work with your company to identify the most suitable use case<\/strong>, develop a PoC, and evaluate its effectiveness before scaling.<\/p>\n

          \"\"<\/p>\n

          Internal Links:<\/strong><\/p>\n

          \u2192 BAP AI Solutions
          \n\u2192 BAP Manufacturing \/ DX Solutions
          \n\u2192 AI Development & Consulting<\/p>\n

          VI. The Important Question Is Not \u201cWhat Percentage of People Can AI Replace?\u201d<\/h1>\n

          If the most experienced person in your factory leaves tomorrow, ask yourself:<\/p>\n

          \n

          \u201cWhat percentage of their knowledge will remain within the company?\u201d<\/strong><\/p>\n<\/blockquote>\n

          If the answer is \u201cvery little,\u201d it may be time to start digitizing your organizational knowledge<\/strong>.<\/p>\n

          AI does not necessarily need to replace the most experienced person in your factory.<\/p>\n

          AI can help ensure that their knowledge does not leave the factory when they do.<\/strong><\/p>\n

          VII. What Challenges in Your Factory Can AI Solve?<\/h1>\n

          BAP helps companies identify the right use cases and implement Generative AI, AI Knowledge Search, AI Agents, and AI\/DX solutions<\/strong>, from PoC to real-world deployment.<\/p>\n

          Talk to BAP to identify the AI use case with the greatest potential to deliver tangible business value for your company.<\/p>","protected":false},"author":45,"featured_media":0,"template":"","meta":{"_acf_changed":false},"tags":[],"blog-cat":[],"class_list":["post-164907","knowledge","type-knowledge","status-publish","hentry"],"acf":[],"yoast_head":"\n(Ti\u1ebfng Vi\u1ec7t) AI trong ng\u00e0nh s\u1ea3n xu\u1ea5t: AI c\u00f3 th\u1ec3 thay th\u1ebf bao nhi\u00eau % kinh nghi\u1ec7m c\u1ee7a k\u1ef9 s\u01b0?<\/title>\n<meta name=\"description\" content=\"(Ti\u1ebfng Vi\u1ec7t) N\u1ebfu k\u1ef9 s\u01b0 gi\u00e0u kinh nghi\u1ec7m ngh\u1ec9 vi\u1ec7c, nh\u00e0 m\u00e1y c\u00f3 th\u1ec3 m\u1ea5t \u0111i l\u01b0\u1ee3ng l\u1edbn tri th\u1ee9c. 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