{"id":5005,"date":"2026-07-16T19:08:03","date_gmt":"2026-07-16T10:08:03","guid":{"rendered":"https:\/\/smilesoftware.org\/?p=5005"},"modified":"2026-07-16T19:08:04","modified_gmt":"2026-07-16T10:08:04","slug":"%e7%94%9f%e6%88%90ai%e3%81%ae%e5%b0%8e%e5%85%a5%e6%99%82%e3%81%ab%e4%bc%81%e6%a5%ad%e3%81%8c%e7%9b%b4%e9%9d%a2%e3%81%99%e3%82%8b5%e3%81%a4%e3%81%ae%e8%aa%b2%e9%a1%8c","status":"publish","type":"post","link":"https:\/\/smilesoftware.org\/en\/%E7%94%9F%E6%88%90ai%E3%81%AE%E5%B0%8E%E5%85%A5%E6%99%82%E3%81%AB%E4%BC%81%E6%A5%AD%E3%81%8C%E7%9B%B4%E9%9D%A2%E3%81%99%E3%82%8B5%E3%81%A4%E3%81%AE%E8%AA%B2%E9%A1%8C\/","title":{"rendered":"Five Challenges Companies Face When Implementing Generative AI"},"content":{"rendered":"<div class=\"wp-block-group smile-post-content smile-ai-challenges-post\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><header class=\"smile-article-header\"><div><span class=\"smile-category-pill\">Blog<\/span><span class=\"smile-date\">2026-07-12<\/span><\/div><h1 class=\"smile-article-title\">Five Challenges Companies Face When Implementing Generative AI<\/h1><p class=\"smile-excerpt\">This article outlines five common challenges in adopting generative AI and practical approaches for moving implementation forward on the front line.<\/p><\/header>\n\n\n<div class=\"wp-block-group smile-article-toc-source\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\"><nav class=\"smile-toc\" aria-label=\"Table of Contents\"><h2 class=\"smile-toc__title\">Table of Contents<\/h2><div class=\"smile-toc__grid\"><a class=\"smile-toc__item\" href=\"#section-1\"><b class=\"smile-toc__number\">01<\/b><span class=\"smile-toc__label\">Why Generative AI Adoption Often Stalls at the Front Line, Even as Implementation Gains Momentum<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-2\"><b class=\"smile-toc__number\">02<\/b><span class=\"smile-toc__label\">Challenge 1: Implementation begins before the objectives are clearly defined<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-3\"><b class=\"smile-toc__number\">03<\/b><span class=\"smile-toc__label\">Challenge 2: Concerns about information leaks and data management<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-4\"><b class=\"smile-toc__number\">04<\/b><span class=\"smile-toc__label\">Challenge 3: Difficulty determining how to evaluate output quality<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-5\"><b class=\"smile-toc__number\">05<\/b><span class=\"smile-toc__label\">Challenge 4: Adoption does not take root on the front line, limiting usage to only some teams<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-6\"><b class=\"smile-toc__number\">06<\/b><span class=\"smile-toc__label\">Challenge 5: Rules and responsibilities are not clearly established<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-7\"><b class=\"smile-toc__number\">07<\/b><span class=\"smile-toc__label\">How to move forward and overcome these challenges<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-8\"><b class=\"smile-toc__number\">08<\/b><span class=\"smile-toc__label\">Summary<\/span><\/a><a class=\"smile-toc__item\" href=\"#section-9\"><b class=\"smile-toc__number\">09<\/b><span class=\"smile-toc__label\">FAQ<\/span><\/a><\/div><\/nav><\/div><\/div>\n\n\n\n<div id=\"section-1\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">01<\/div><h2 class=\"smile-section__title\">Why Generative AI Adoption Often Stalls at the Front Line, Even as Implementation Gains Momentum<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01.png\" alt=\"\u751f\u6210AI\u5c0e\u5165\u304c\u9032\u3080\u4e00\u65b9\u3067\u3001\u306a\u305c\u73fe\u5834\u3067\u6b62\u307e\u308a\u3084\u3059\u3044\u306e\u304b\" class=\"wp-image-5007\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-01-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>Generative AI is increasingly being used across a wide range of business tasks, including document creation, summarization, inquiry handling, and search support. However, many companies begin implementation only to stall at the pilot stage.<\/p>\n\n\n\n<p>This is because successful adoption depends not only on the tool\u2019s performance, but also on how it is integrated into workflows, how information is managed, how results are evaluated, and what internal rules are in place. In corporate use, simply adopting a tool because it seems convenient rarely leads to lasting adoption; it is essential to define the purpose and design the operating model.<\/p>\n\n\n\n<p>This article clearly outlines five common challenges companies often face when implementing generative AI and how to overcome them.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-2\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">02<\/div><h2 class=\"smile-section__title\">Challenge 1: Implementation begins before the objectives are clearly defined<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02.png\" alt=\"\u8ab2\u984c1\uff1a\u76ee\u7684\u304c\u66d6\u6627\u306a\u307e\u307e\u5c0e\u5165\u304c\u59cb\u307e\u308b\" class=\"wp-image-5008\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-02-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>One of the most common mistakes is beginning implementation without a clear understanding of why generative AI is being used.<\/p>\n\n\n\n<p>For example, if generative AI is introduced simply because it is a topic of discussion internally or because competitors have started using it, the connection to real business challenges will be weak. As a result, frontline teams are likely to feel unsure about what to use it for or unable to see clear results even when they do use it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Common examples<\/h3>\n\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--policy\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 8h20l10 10v38H18z\"\/><path d=\"M38 8v12h10\"\/><path d=\"M24 32h18\"\/><path d=\"M24 42h14\"\/><path d=\"m24 22 4 4 8-8\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Meeting minutes automation is considered before identifying which manual tasks are creating the workload<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">The company wants to use AI for handling inquiries, but has not set targets for response quality.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--operations\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"9\"\/><path d=\"M32 8v8\"\/><path d=\"M32 48v8\"\/><path d=\"m15 15 6 6\"\/><path d=\"m43 43 6 6\"\/><path d=\"M8 32h8\"\/><path d=\"M48 32h8\"\/><path d=\"m15 49 6-6\"\/><path d=\"m43 21 6-6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Rushing company-wide implementation can leave priorities unclear across user departments.<\/h3><\/div><\/article><\/div>\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Approach to Response<\/h3>\n\n\n\n<p>Before introducing generative AI, it is important to clearly define which business process and which specific task you want to improve, and by how much. By translating this into concrete objectives, such as reducing the time needed to draft proposals or speeding up the initial search for internal FAQ information, it also becomes easier to evaluate results.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-3\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">03<\/div><h2 class=\"smile-section__title\">Challenge 2: Concerns about information leaks and data management<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03.png\" alt=\"\u8ab2\u984c2\uff1a\u60c5\u5831\u6f0f\u3048\u3044\u3068\u30c7\u30fc\u30bf\u7ba1\u7406\u3078\u306e\u4e0d\u5b89\" class=\"wp-image-5009\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-03-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>Information security and data management are major reasons why companies take a cautious approach to adopting generative AI.<\/p>\n\n\n\n<p>If it is unclear how entered information will be handled, whether confidential information can be submitted, or how far integration with external services is permitted, employees cannot use these tools with confidence. In workflows that handle customer information, contract details, design data, or HR information in particular, expanding use without clear rules is risky.<\/p>\n\n\n\n<p>The OECD AI Principles also emphasize privacy, safety, and accountability alongside fairness and transparency. Similarly, the NIST AI Risk Management Framework states that the use of AI requires ongoing risk management.<\/p>\n\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Common examples<\/h3>\n\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--policy\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 8h20l10 10v38H18z\"\/><path d=\"M38 8v12h10\"\/><path d=\"M24 32h18\"\/><path d=\"M24 42h14\"\/><path d=\"m24 22 4 4 8-8\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Employees enter internal documents directly into public AI services.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Without defined categories of data that can be used, each department makes inconsistent decisions.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--operations\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"9\"\/><path d=\"M32 8v8\"\/><path d=\"M32 48v8\"\/><path d=\"m15 15 6 6\"\/><path d=\"m43 43 6 6\"\/><path d=\"M8 32h8\"\/><path d=\"M48 32h8\"\/><path d=\"m15 49 6-6\"\/><path d=\"m43 21 6-6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">The tool is introduced without first reviewing the vendor\u2019s terms of use or data training policy<\/h3><\/div><\/article><\/div>\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Approach to Response<\/h3>\n\n\n\n<p>In the early stages of implementation, it is safest to clearly define what information may be entered, what is prohibited, and which cases require approval, then begin with a limited scope of use. It is also essential to manage logs, set access permissions, and review the contractual terms of the services being used.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-4\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">04<\/div><h2 class=\"smile-section__title\">Challenge 3: Difficulty determining how to evaluate output quality<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04.png\" alt=\"\u8ab2\u984c3\uff1a\u51fa\u529b\u54c1\u8cea\u3092\u3069\u3046\u8a55\u4fa1\u3059\u308b\u304b\u5206\u304b\u308a\u306b\u304f\u3044\" class=\"wp-image-5010\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-04-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>Generative AI is useful, but it does not always provide accurate answers. It may produce plausible but incorrect responses or explanations with weak supporting evidence. Therefore, companies need to determine not only whether it can be used, but whether its output is reliable enough for business use.<\/p>\n\n\n\n<p>In practice, however, evaluation criteria are often not defined, so decisions tend to be made based on each person\u2019s individual judgment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Common examples<\/h3>\n\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--policy\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 8h20l10 10v38H18z\"\/><path d=\"M38 8v12h10\"\/><path d=\"M24 32h18\"\/><path d=\"M24 42h14\"\/><path d=\"m24 22 4 4 8-8\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">No one has defined what level of accuracy is required for summaries.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Inconsistent response tone and prohibited expressions<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--operations\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"9\"\/><path d=\"M32 8v8\"\/><path d=\"M32 48v8\"\/><path d=\"m15 15 6 6\"\/><path d=\"m43 43 6 6\"\/><path d=\"M8 32h8\"\/><path d=\"M48 32h8\"\/><path d=\"m15 49 6-6\"\/><path d=\"m43 21 6-6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Although it was well received in the PoC, it requires too much rework in actual operations to deliver meaningful results<\/h3><\/div><\/article><\/div>\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Approach to Response<\/h3>\n\n\n\n<p>It is important to define evaluation criteria for each use case in advance, such as accuracy, reproducibility, ease of updates, effort required for revisions, and user satisfaction. The required quality level will vary depending on whether a person will perform the final review or the goal is to increase the rate of automated processing.<\/p>\n\n\n\n<p>Especially when using it for external documents or customer communications, it is more practical to include a human review process in the workflow.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-5\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">05<\/div><h2 class=\"smile-section__title\">Challenge 4: Adoption does not take root on the front line, limiting usage to only some teams<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05.png\" alt=\"\u8ab2\u984c4\uff1a\u73fe\u5834\u306b\u5b9a\u7740\u305b\u305a\u3001\u6d3b\u7528\u304c\u4e00\u90e8\u306b\u3068\u3069\u307e\u308b\" class=\"wp-image-5011\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-05-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>Generative AI will not be adopted simply because it has been introduced. In many cases, only certain employees become proficient in using it, while adoption fails to spread across the wider organization.<\/p>\n\n\n\n<p>Behind this are adoption challenges such as employees not knowing how to use the tools, the tools not being integrated into business workflows, and a lack of input examples or templates. Simply granting access to a tool does not guarantee that it will be naturally adopted in day-to-day operations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Common examples<\/h3>\n\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--policy\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 8h20l10 10v38H18z\"\/><path d=\"M38 8v12h10\"\/><path d=\"M24 32h18\"\/><path d=\"M24 42h14\"\/><path d=\"m24 22 4 4 8-8\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Training was held only once, with no follow-up afterward.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Prompt creation is left to individual employees, so prompts are not structured for reuse<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--operations\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"9\"\/><path d=\"M32 8v8\"\/><path d=\"M32 48v8\"\/><path d=\"m15 15 6 6\"\/><path d=\"m43 43 6 6\"\/><path d=\"M8 32h8\"\/><path d=\"M48 32h8\"\/><path d=\"m15 49 6-6\"\/><path d=\"m43 21 6-6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Without clear criteria for switching from existing workflows, teams ultimately revert to conventional methods<\/h3><\/div><\/article><\/div>\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Approach to Response<\/h3>\n\n\n\n<p>To encourage adoption in day-to-day operations, it is effective to design usage scenarios for each department. For example, sales teams might use it to draft proposal outlines, general affairs teams to prepare internal announcements, and development teams to organize specifications. Showing concrete use cases makes it easier for employees to understand how it can be applied.<\/p>\n\n\n\n<p>It is also helpful to create templates for commonly used prompt examples, prohibited use cases, and review criteria, making it easier to reduce reliance on individual staff members.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-6\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">06<\/div><h2 class=\"smile-section__title\">Challenge 5: Rules and responsibilities are not clearly established<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06.png\" alt=\"\u8ab2\u984c5\uff1a\u30eb\u30fc\u30eb\u3084\u8cac\u4efb\u5206\u62c5\u304c\u6574\u3063\u3066\u3044\u306a\u3044\" class=\"wp-image-5012\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-06-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>When using generative AI in business operations, companies need to define who is responsible for making which decisions; otherwise, operations can become inconsistent.<\/p>\n\n\n\n<p>For example, if it remains unclear whether the information systems department will create usage rules, whether department heads will approve them, where the legal team will conduct reviews, or who will respond to system failures or incorrect outputs, confusion is likely to arise after implementation.<\/p>\n\n\n\n<p>The NIST AI RMF also emphasizes that AI risk management is not something an organization can define once and consider complete; it must be reviewed continuously at an organizational level. Because generative AI evolves rapidly, relying on initial rules alone for long-term operation can be difficult in some situations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Common examples<\/h3>\n\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--policy\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 8h20l10 10v38H18z\"\/><path d=\"M38 8v12h10\"\/><path d=\"M24 32h18\"\/><path d=\"M24 42h14\"\/><path d=\"m24 22 4 4 8-8\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Without a usage request or approval process, departments move ahead with their own independent use.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Accountability is unclear when output errors occur.<\/h3><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--operations\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><circle cx=\"32\" cy=\"32\" r=\"9\"\/><path d=\"M32 8v8\"\/><path d=\"M32 48v8\"\/><path d=\"m15 15 6 6\"\/><path d=\"m43 43 6 6\"\/><path d=\"M8 32h8\"\/><path d=\"M48 32h8\"\/><path d=\"m15 49 6-6\"\/><path d=\"m43 21 6-6\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">No defined approach for audits or recordkeeping<\/h3><\/div><\/article><\/div>\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Approach to Response<\/h3>\n\n\n\n<p>Even when starting small, a basic operating structure is necessary. By briefly defining the usage policy, applicable tasks, approvers, review responsibilities, log handling, and review timing, it becomes easier to scale later.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-7\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">07<\/div><h2 class=\"smile-section__title\">How to move forward and overcome these challenges<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07.png\" alt=\"\u8ab2\u984c\u3092\u4e57\u308a\u8d8a\u3048\u308b\u305f\u3081\u306e\u9032\u3081\u65b9\" class=\"wp-image-5013\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-07-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>To make generative AI adoption successful, it is more practical to start with a focused set of business processes and roll it out in stages rather than deploying it across the entire company all at once.<\/p>\n\n\n\n<h3 class=\"wp-block-heading smile-subheading\">Example approach<\/h3>\n\n\n<div class=\"smile-step-roadmap\"><article class=\"smile-step-roadmap__item\"><span class=\"smile-step-roadmap__index\">01<\/span><div><h4 class=\"smile-step-roadmap__title\">Clarify the business challenges and narrow the scope to one or two target processes<\/h4><p class=\"smile-step-roadmap__detail\"><\/p><\/div><\/article><article class=\"smile-step-roadmap__item\"><span class=\"smile-step-roadmap__index\">02<\/span><div><h4 class=\"smile-step-roadmap__title\">Define the expected benefits<\/h4><p class=\"smile-step-roadmap__detail\"><\/p><\/div><\/article><article class=\"smile-step-roadmap__item\"><span class=\"smile-step-roadmap__index\">03<\/span><div><h4 class=\"smile-step-roadmap__title\">Define the scope of permitted input data and prohibited data<\/h4><p class=\"smile-step-roadmap__detail\"><\/p><\/div><\/article><article class=\"smile-step-roadmap__item\"><span class=\"smile-step-roadmap__index\">04<\/span><div><h4 class=\"smile-step-roadmap__title\">Establish evaluation criteria and review procedures<\/h4><p class=\"smile-step-roadmap__detail\"><\/p><\/div><\/article><article class=\"smile-step-roadmap__item\"><span class=\"smile-step-roadmap__index\">05<\/span><div><h4 class=\"smile-step-roadmap__title\">Test it in the field, then improve prompts and operations<\/h4><p class=\"smile-step-roadmap__detail\"><\/p><\/div><\/article><article class=\"smile-step-roadmap__item\"><span class=\"smile-step-roadmap__index\">06<\/span><div><h4 class=\"smile-step-roadmap__title\">Expand to additional departments after confirming the impact<\/h4><p class=\"smile-step-roadmap__detail\"><\/p><\/div><\/article><\/div>\n\n\n<p>This approach makes it easier to identify practical use cases while keeping excessive investment and unexpected risks under control.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-8\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">08<\/div><h2 class=\"smile-section__title\">Summary<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08.png\" alt=\"\u307e\u3068\u3081\" class=\"wp-image-5014\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-08-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n\n<p>Companies commonly face the following five key challenges when implementing generative AI.<\/p>\n\n\n<div class=\"smile-summary-flow\"><article class=\"smile-summary-flow__item\"><span class=\"smile-summary-flow__index\">01<\/span><p class=\"smile-summary-flow__body\">Implementation Begins Without Clear Objectives<\/p><\/article><article class=\"smile-summary-flow__item\"><span class=\"smile-summary-flow__index\">02<\/span><p class=\"smile-summary-flow__body\">Concerns about information leaks and data management<\/p><\/article><article class=\"smile-summary-flow__item\"><span class=\"smile-summary-flow__index\">03<\/span><p class=\"smile-summary-flow__body\">It is difficult to evaluate output quality.<\/p><\/article><article class=\"smile-summary-flow__item\"><span class=\"smile-summary-flow__index\">04<\/span><p class=\"smile-summary-flow__body\">Difficult to embed in day-to-day operations<\/p><\/article><article class=\"smile-summary-flow__item\"><span class=\"smile-summary-flow__index\">05<\/span><p class=\"smile-summary-flow__body\">Rules and responsibilities have not been clearly defined<\/p><\/article><\/div>\n\n\n<p>These issues cannot be solved by the capabilities of generative AI alone. It is important to align business process design, operating rules, evaluation criteria, and internal structures.<\/p>\n\n\n\n<p>SMILE supports phased implementation, from organizing business challenges and evaluating systemization to development and operation.<\/p>\n\n\n\n<p>Companies considering DX or the use of AI should start with small operational improvements.<\/p>\n<\/div><\/div>\n\n\n\n<div id=\"section-9\" class=\"wp-block-group smile-section\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\"><div class=\"smile-section__head\"><div class=\"smile-section__number\">09<\/div><h2 class=\"smile-section__title\">FAQ<\/h2><\/div>\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09.png\" alt=\"FAQ\" class=\"wp-image-5015\" srcset=\"https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09.png 1672w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09-300x169.png 300w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09-1024x576.png 1024w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09-768x432.png 768w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09-1536x864.png 1536w, https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-section-09-18x10.png 18w\" sizes=\"auto, (max-width: 1672px) 100vw, 1672px\" \/><\/figure>\n\n\n<div class=\"smile-evidence-panels\"><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--automation\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M32 12v8\"\/><path d=\"M32 44v8\"\/><path d=\"M12 32h8\"\/><path d=\"M44 32h8\"\/><circle cx=\"32\" cy=\"32\" r=\"12\"\/><path d=\"m24 24 16 16\"\/><path d=\"m40 24-16 16\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Q1. Should we roll out generative AI across the entire company right away?<\/h3><p>Not necessarily. Starting small with tasks where results are easy to see and risks are easier to manage is more likely to reduce the chance of failure.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--policy\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M18 8h20l10 10v38H18z\"\/><path d=\"M38 8v12h10\"\/><path d=\"M24 32h18\"\/><path d=\"M24 42h14\"\/><path d=\"m24 22 4 4 8-8\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Q2. If we are concerned about information leaks, what should we decide first?<\/h3><p>It is easier to move forward by organizing policies around four areas: what types of information may be entered, what information is prohibited, criteria for selecting services to use, and log management.<\/p><\/div><\/article><article class=\"smile-evidence-panel\"><div class=\"smile-evidence-panel__mark\"><span class=\"smile-evidence-panel__icon\" aria-hidden=\"true\"><svg class=\"smile-ui-icon smile-ui-icon--quality\" viewbox=\"0 0 64 64\" aria-hidden=\"true\" focusable=\"false\" stroke=\"currentColor\"><path d=\"M16 34 28 46 50 18\"\/><path d=\"M14 14h28\"\/><path d=\"M14 24h18\"\/><path d=\"M14 50h36\"\/><\/svg><\/span><\/div><div class=\"smile-evidence-panel__body\"><h3 class=\"smile-evidence-panel__title\">Q3. How should we evaluate the quality of generative AI?<\/h3><p>It is important to define evaluation criteria that fit the intended use in advance, such as accuracy, reproducibility, reduced rework, and time savings.<\/p><\/div><\/article><\/div><\/div><\/div>\n\n\n<section class=\"smile-article-cta\"><div class=\"smile-article-cta__label\">SMILE Support<\/div><h2>Start a Consultation on Advancing GX and DX<\/h2><p>We work with you to design an approach tailored to your company\u2019s situation, from assessing the current state to implementing systems and improving operations.<\/p><ul class=\"smile-list\"><li>You can clarify current operational and data-related challenges.<\/li><li>You can design implementation steps that fit your company's structure.<\/li><li>You can also build a framework that remains easy to operate after implementation.<\/li><li>You can use data to verify results and make improvements.<\/li><li>You can start exploring AI and IoT applications in the areas where they are most needed.<\/li><\/ul><a class=\"smile-article-cta__button\" href=\"\/en\/contact\/\">Contact Us<\/a><\/section><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Generative AI can be applied across a wide range of tasks, including document...&nbsp;<a href=\"https:\/\/smilesoftware.org\/en\/%E7%94%9F%E6%88%90ai%E3%81%AE%E5%B0%8E%E5%85%A5%E6%99%82%E3%81%AB%E4%BC%81%E6%A5%AD%E3%81%8C%E7%9B%B4%E9%9D%A2%E3%81%99%E3%82%8B5%E3%81%A4%E3%81%AE%E8%AA%B2%E9%A1%8C\/\" class=\"\" rel=\"bookmark\">Read More &raquo;<span class=\"screen-reader-text\">Five Challenges Companies Face When Implementing Generative AI<\/span><\/a><\/p>","protected":false},"author":2,"featured_media":5006,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"_themeisle_gutenberg_block_has_review":false,"_ti_tpc_template_sync":false,"_ti_tpc_template_id":"","footnotes":""},"categories":[32],"tags":[],"class_list":["post-5005","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"rttpg_featured_image_url":{"full":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image.png",1672,941,false],"landscape":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image.png",1672,941,false],"portraits":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image.png",1672,941,false],"thumbnail":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image-150x150.png",150,150,true],"medium":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image-300x169.png",300,169,true],"large":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image-1024x576.png",1024,576,true],"1536x1536":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image-1536x864.png",1536,864,true],"2048x2048":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image.png",1672,941,false],"trp-custom-language-flag":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image-18x10.png",18,10,true],"neve-blog":["https:\/\/smilesoftware.org\/wp-content\/uploads\/2026\/07\/redmine-1433-141033-featured-image-930x620.png",930,620,true]},"rttpg_author":{"display_name":"\u7b20\u539f \u840c","author_link":"https:\/\/smilesoftware.org\/en\/author\/chinh-man\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/smilesoftware.org\/en\/category\/blog\/\" rel=\"category tag\">\u30d6\u30ed\u30b0<\/a>","rttpg_excerpt":"\u751f\u6210AI\u306f\u3001\u6587\u66f8\u4f5c\u6210\u3001\u8981\u7d04\u3001\u554f\u3044\u5408\u308f\u305b\u5bfe\u5fdc\u3001\u691c\u7d22\u652f\u63f4\u306a\u3069\u3001\u5e45&hellip;&nbsp;Read More &&hellip;","_links":{"self":[{"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/posts\/5005","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/comments?post=5005"}],"version-history":[{"count":26,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/posts\/5005\/revisions"}],"predecessor-version":[{"id":5155,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/posts\/5005\/revisions\/5155"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/media\/5006"}],"wp:attachment":[{"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/media?parent=5005"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/categories?post=5005"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/smilesoftware.org\/en\/wp-json\/wp\/v2\/tags?post=5005"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}