{"id":164833,"date":"2026-09-04T16:40:43","date_gmt":"2026-09-04T09:40:43","guid":{"rendered":"https:\/\/bap-software.net\/?post_type=knowledge&p=164833"},"modified":"2026-09-04T16:40:43","modified_gmt":"2026-09-04T09:40:43","slug":"%e8%a3%bd%e9%80%a0%e6%a5%ad%e3%81%afai%e6%b4%bb%e7%94%a8%e3%82%92%e3%81%a9%e3%81%93%e3%81%8b%e3%82%89%e5%a7%8b%e3%82%81%e3%82%8b%e3%81%b9%e3%81%8d%e3%81%8b%ef%bc%9fbap%e3%81%aeai%e5%b0%82%e9%96%80","status":"publish","type":"knowledge","link":"https:\/\/bap-software.net\/en\/knowledge\/%e8%a3%bd%e9%80%a0%e6%a5%ad%e3%81%afai%e6%b4%bb%e7%94%a8%e3%82%92%e3%81%a9%e3%81%93%e3%81%8b%e3%82%89%e5%a7%8b%e3%82%81%e3%82%8b%e3%81%b9%e3%81%8d%e3%81%8b%ef%bc%9fbap%e3%81%aeai%e5%b0%82%e9%96%80\/","title":{"rendered":"Where Should Manufacturers Start With AI? Advice From BAP\u2019s AI Experts"},"content":{"rendered":"

<\/p>\n

AI is becoming one of the most closely watched technologies in manufacturing. From quality inspection and equipment failure prediction to production planning optimization, AI can help manufacturers reduce manual work and make faster, data-driven decisions.<\/p>\n

However, the main challenge many companies face is not:<\/p>\n

\u201cWhat can AI do?\u201d<\/p>\n

The real question is:<\/p>\n

\u201cWhere should our company start with AI?\u201d<\/p>\n

Based on BAP\u2019s experience in implementing real-world AI projects, our AI experts recommend that manufacturers should not begin with a large-scale AI initiative.<\/p>\n

Instead, they should first identify\u00a0one specific operational problem where AI can create measurable business value.<\/p>\n

How Is AI Being Used in Manufacturing?<\/h1>\n

Factories generate huge volumes of data every day, including production data, machine information, sensor data, camera images, failure histories, maintenance reports, inventory data, and Excel files manually updated by employees.<\/p>\n

These data sources can become valuable assets for AI applications.<\/p>\n

Some of the most common AI use cases in manufacturing today include:<\/p>\n

Quality inspection with AI Vision<\/p>\n

Cameras and Computer Vision can identify scratches, deformation, visual defects, and other quality issues.<\/p>\n

This can reduce the amount of manual inspection required while also standardizing quality evaluation criteria.<\/p>\n

Anomaly detection and predictive maintenance<\/p>\n

AI can analyze sensor data, images, and historical operating data to identify abnormal behavior before equipment failure occurs.<\/p>\n

Production planning optimization<\/p>\n

AI can analyze orders, inventory, workforce availability, equipment capacity, and delivery deadlines to support more effective production planning.<\/p>\n

Generative AI and AI Agents<\/p>\n

Generative AI and AI Agents can assist with report generation, manual searches, document processing, incident summarization, and the use of internal organizational knowledge.<\/p>\n

Importantly, manufacturers do\u00a0not\u00a0need to build a complete Smart Factory before they can begin using AI.<\/p>\n

Even a manual task that takes employees several hours every day can be an excellent starting point.<\/p>\n

3 Recommendations From BAP\u2019s AI Experts<\/h1>\n
    \n
  1. \n

    Don\u2019t Start With \u201cAI.\u201d Start With the Problem.<\/h2>\n<\/li>\n<\/ol>\n

    One of the most common mistakes is beginning a project with the question:<\/p>\n

    \u201cWhat can we use AI for?\u201d<\/p>\n

    Instead, companies should first identify a real operational problem.<\/p>\n

    For example:<\/p>\n

      \n
    • Visual inspection takes three hours every day.<\/li>\n
    • Equipment is repaired only after a failure has already occurred.<\/li>\n
    • Production planning depends on Excel and the experience of a few employees.<\/li>\n
    • Only experienced employees know how to respond to certain machine errors.<\/li>\n<\/ul>\n

      Once the problem is clear, evaluate it in the following order:<\/p>\n

      Problem \u2192 Data \u2192 Technology \u2192 Business Impact<\/p>\n

      Not every problem requires AI.<\/p>\n

      In some situations, RPA, BI, IoT, or improvements to an existing system may be more appropriate.<\/p>\n

      The ultimate objective is not to\u00a0\u201cintroduce AI into the factory.\u201d<\/p>\n

      It is to\u00a0solve a business problem using the most appropriate technology.<\/p>\n

        \n
      1. \n

        Start Small With \u201c1 Problem \u00d7 1 Process\u201d<\/h2>\n<\/li>\n<\/ol>\n

        Instead of trying to automate an entire factory with AI, manufacturers can begin with a narrow scope.<\/p>\n

        For example:<\/p>\n

          \n
        • Detect one specific type of product defect.<\/li>\n
        • Monitor anomalies in one type of machine.<\/li>\n
        • Automate one type of report.<\/li>\n
        • Optimize one stage of production planning.<\/li>\n<\/ul>\n

          The company can then run a PoC to validate whether AI can solve the problem.<\/p>\n

          However, a PoC should not simply answer:<\/p>\n

          \u201cDoes the AI work?\u201d<\/p>\n

          Companies should define KPIs from the beginning, such as:<\/p>\n

            \n
          • How many working hours can be reduced?<\/li>\n
          • What level of accuracy can be achieved?<\/li>\n
          • How much can downtime be reduced?<\/li>\n
          • How much can the defect rate be reduced?<\/li>\n
          • How will operational costs change?<\/li>\n<\/ul>\n

            If the results can be demonstrated with measurable data, it becomes much easier to decide whether the company should move from PoC to full-scale implementation.<\/p>\n

              \n
            1. \n

              Don\u2019t Just Build an AI Model \u2014 Design the Entire Process Around It<\/h2>\n<\/li>\n<\/ol>\n

              Factories already use many systems, including ERP, MES, PLCs, IoT platforms, Excel, and internal databases.<\/p>\n

              This means that even a highly accurate AI model may create little real business value if it remains isolated from existing operational processes.<\/p>\n

              For example, suppose AI detects abnormal behavior in a machine.<\/p>\n

              The workflow should not end with:<\/p>\n

              \u201cAI detected an anomaly.\u201d<\/p>\n

              The company must define what happens next:<\/p>\n

              AI detects \u2192 Who receives the notification? \u2192 Where does the employee review it? \u2192 Who takes action? \u2192 Where is the result recorded?<\/p>\n

              This is the difference between\u00a0an AI demo\u00a0and\u00a0an AI system that is actually used in a factory.<\/p>\n

              BAP\u2019s Real-World AI Projects<\/h1>\n

              Our experience from real AI projects also shows that AI can generate significant results when it is applied to the right problem.<\/p>\n

              Smart Factory Agent \u2013 30% Reduction in Downtime<\/h2>\n

              BAP developed a\u00a0Smart Factory Agent\u00a0that combines camera and sensor data to detect anomalies and support Predictive Maintenance.<\/p>\n

              The solution uses technologies including:<\/p>\n

                \n
              • YOLOv8<\/li>\n
              • OpenCV<\/li>\n
              • LangChain<\/li>\n
              • Private GPT<\/li>\n<\/ul>\n

                Rather than simply detecting abnormalities, the system also integrates with a notification bot that sends information to responsible personnel and supports the subsequent response process.<\/p>\n

                After approximately\u00a0four weeks of PoC, downtime was reduced by 30%.<\/p>\n

                This case demonstrates that the value of AI is not limited to detecting anomalies.<\/p>\n

                The greater value comes from helping manufacturers\u00a0detect problems earlier and take action sooner.<\/p>\n

                Maintenance AI Assistant \u2013 70% Reduction in Incident Reporting Time<\/h2>\n

                When a machine fails, employees often need to inspect the equipment, take photos, describe the situation, and compile an incident report.<\/p>\n

                BAP developed a\u00a0Maintenance AI Assistant\u00a0that combines:<\/p>\n

                Speech Recognition + Vision AI + LLM<\/p>\n

                using technologies such as:<\/p>\n

                  \n
                • Whisper<\/li>\n
                • OpenAI Vision<\/li>\n
                • GPT<\/li>\n<\/ul>\n

                  Employees can provide information through voice and images, while AI supports the analysis and summarization of the incident.<\/p>\n

                  A PoC of approximately\u00a0four weeks reduced machine incident reporting time by 70%.<\/p>\n

                  This case also shows that Generative AI in manufacturing is not limited to building chatbots.<\/p>\n

                  It can directly support operational activities on the factory floor.<\/p>\n

                  Document Processing AI Agent \u2013 60% Faster Processing<\/h2>\n

                  Another area with significant AI potential is document processing.<\/p>\n

                  Factories often have many checklists, production forms, PDFs, invoices, and Excel files that employees must manually review or enter into systems.<\/p>\n

                  BAP combined:<\/p>\n

                  OCR + LLM<\/p>\n

                  to automatically recognize, extract, and process information from documents.<\/p>\n

                  The solution uses technologies including:<\/p>\n

                    \n
                  • Azure Document Intelligence<\/li>\n
                  • GPT<\/li>\n
                  • LangChain<\/li>\n<\/ul>\n

                    During the PoC,\u00a0document processing speed increased by 60%.<\/p>\n

                    For manufacturers that still rely heavily on paper documents, PDFs, and Excel, document processing can be a practical starting point for AI without requiring changes to the entire production line.<\/p>\n

                    \n

                    What Should Manufacturers Prepare Before Implementing AI?<\/h1>\n

                    Before investing in an AI project, companies should be able to answer four questions.<\/p>\n

                      \n
                    1. What problem are we trying to solve?<\/li>\n<\/ol>\n

                      Turn a general objective such as:<\/p>\n

                      \u201cWe want to use AI.\u201d<\/p>\n

                      into a specific target such as:<\/p>\n

                      \u201cWe want to reduce inspection time by 30%.\u201d<\/p>\n

                        \n
                      1. Do we have the data needed to solve the problem?<\/li>\n<\/ol>\n

                        Review existing images, sensor logs, Excel files, operating histories, defect data, and technical documents.<\/p>\n

                          \n
                        1. How will success be measured?<\/li>\n<\/ol>\n

                          Define KPIs such as time saved, accuracy, downtime, defect rate, or operational cost.<\/p>\n

                            \n
                          1. What happens after the PoC?<\/li>\n<\/ol>\n

                            From the beginning, manufacturers should consider system integration, security, operations, and maintenance after the AI solution is moved into production.<\/p>\n<\/blockquote>\n

                            Conclusion: Start AI With a Small but Measurable Problem<\/h1>\n

                            Manufacturers do not necessarily need to invest immediately in a large-scale Smart Factory project.<\/p>\n

                            Instead, start with practical questions:<\/p>\n

                            Which activity currently takes the most time?<\/p>\n

                            Which process depends too heavily on individual experience?<\/p>\n

                            Which failures create significant losses if they are detected too late?<\/p>\n

                            What data does the company already have but is not yet using effectively?<\/p>\n

                            From there, manufacturers can follow a step-by-step approach:<\/p>\n

                            Identify the problem \u2192 Review the data \u2192 Evaluate AI feasibility \u2192 PoC \u2192 Measure results \u2192 Production implementation<\/p>\n

                            BAP Solution Japan supports manufacturers across areas including\u00a0AI Vision, Predictive Maintenance, Generative AI\/LLMs, RAG, AI Agents, AI-OCR, production optimization, and AI integration with ERP\/MES\/IoT systems.<\/p>\n

                            Instead of starting with:<\/p>\n

                            \u201cWhich AI should we use?\u201d<\/p>\n

                            start with:<\/p>\n

                            \u201cWhich business problem would create the most value if AI could solve it?\u201d<\/p>\n

                            That is the first step toward transforming AI from an experimental technology into measurable value for manufacturing operations.<\/p>\n

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