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snowflake-responsive-component-top-padding-medium"},"text":{"id":"text-c913255d33","text":"\u003Cp\u003E10か国と6つの業界に細分化したハイライトを分析しています。主なポイントは次のとおりです。 \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003Eカナダの組織は、顧客のユースケースに積極的に注力しており、54%が生成AIをカスタマーサービスとサポートに適用している（世界全体では48%） \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eフランスの組織は、生成AIのROI達成に苦戦しており、ROIを定量化できている割合は32%にとどまっている（世界全体では49%）\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E金融サービス企業は、業務効率化よりも財務パフォーマンスの向上に関心を持つ傾向が強くなっている（44%が採用における最重要推進要因として挙げているのに対し、全業界平均は29%）\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E製造企業は、従業員のスキルや経験に関する課題を挙げる割合が高くなっている（41%、平均は34%）\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EROI、エージェントの導入状況、シャドーAIの浸透度などの地域や業界ごとの詳細なハイライトについては、ぜひレポートの全文をご確認ください。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-extra-small"}},":itemsOrder":["title_v2","text"]}},":itemsOrder":["container_copy","container_copy_copy","container_copy_copy_","container_copy_copy__1099993454","container_copy_copy__222148415","container_copy_copy__1360230345"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"form","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"marketo_v2_copy":{"id":"marketo-v2-06b4d95ce1","marketoForm":{"edit":false,"successUrl":"form.onSuccess(function(values, followUpUrl) {\nlocation.href = \"https:\\/\\/www.snowflake.com\\/ja\\/lp\\/radical-roi-generative-ai\\/thankyou\\/\";\nreturn false;\n});","formId":"55723","hidden":null,"script":null,"values":null},"title":{"id":"title","type":"heading3","lines":["今すぐダウンロード"],":type":"snowflake-site/components/title-v2"},"formConfigured":true,"marketoConfigured":true,"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com",":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["marketo_v2_copy"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["flexible_column_cont"]},"container_1545902728":{"additionalClasses":"methodology","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-669383694a","appliedCssClassNames":"ui-background-05 snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"container":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-f7fa5681e5","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-c7d2535303","propertiesId":"methodology","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"extra-large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-e53339f4fa","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-0d457e874a","type":"heading3","lines":["調査手法"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-25cadd3e9e","text":"\u003Cp\u003E本レポートの作成にあたり、Informa TechTarget傘下のOmdiaは、2025年8月13日から2025年9月17日にかけて包括的なオンラインのアンケート調査を実施しました。すべての回答者は、従業員数が500人以上の企業に所属しています。回答者の内訳は、従業員数5,000人以上の組織が約34%、従業員数1,000〜4,999人の組織が49%、従業員数500〜999人の組織が17%となっています。 \u003C/p\u003E\n\u003Cp\u003E本レポートおよび元の調査は、企業組織による生成AIテクノロジーの活用実態について、より深い理解を得ることを意図しています。本調査は多くの点において2025年の調査およびレポートの継続であり、LLMのユースケース、メリット、課題、ROI、将来への期待などを幅広く取り上げています。さらに、組織がビジネスプロセスのさらなる自動化に向けてエージェント型AIソリューションの活用を進めるなかで、新たな領域にも踏み込んでいます。こうしたインサイトの多くは、本番環境で実際に生成AIを活用している組織を対象に調査を行うことで初めて得られるものです。そのため、本調査は本番環境で生成AIを活用してビジネスプロセスの強化や実行を行っている組織を対象としています。 \u003C/p\u003E\n\u003Cp\u003Eしかしながら、本調査では組織における生成AIの採用状況を観測することもできました。調査開始時の3,479人の回答者のうち、59%が、自組織で多数のビジネスユースケース（39%）または少数の初期ユースケース（20%）ですでに生成AIを使用していると報告しています。さらに、自組織でソリューションを導入する計画も関心もないと報告した回答者はわずか2%でした。\u003C/p\u003E\n\u003Cp\u003Eこうした調査結果は、1年前に収集されたデータと極めて高い一貫性を示しており、生成AIが現在に至るまで、新たなエンタープライズテクノロジーに特有の「急成長、停滞、回復」という典型的な採用サイクルに陥っていないことが窺えます。採用を後押ししている2つの要因として、ユーザーが日常のデジタル体験に生成AIを統合していることと、企業がさまざまなユースケースから顕著なインパクトを得ていることが挙げられます。生成AIはこれまでに、ユーザーの日常生活に広範な影響を及ぼし、企業の従業員の業務効率や生産性に対しても大きなインパクトをもたらしてきました。そのため、過度な期待が現実を上回った場合によく見られる、利用や投資の縮小を回避できていると考えられます。\u003C/p\u003E\n\u003Cp\u003E調査に最後まで回答した2,050名の事業部門の内訳は、IT/サイバーセキュリティ（49%）、ソフトウェア開発（16%）、データ運用（9%）、その他の事業部門（マーケティング、カスタマーサポート、製造など、25%）となっています。調査対象の要件を満たすには、回答者が自組織の将来のAI購入において影響力を持つと報告している必要がありました。Cレベルのエグゼクティブからシニアレベルの個人貢献者まで、幅広い層が含まれています。国別の内訳は、米国（41%）、カナダ（7%）、英国（7%）、フランス（7%）、ドイツ（7%）、オーストラリアとニュージーランド（7%）、日本（7%）、シンガポール（7%）、インド（7%）となっています。サンプルサイズの誤差範囲は、95%の信頼水準で±2ポイントです。本レポートで使用されている図表に示す合計は、数の丸め方により100%にならないことがあります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"}},":itemsOrder":["title_v2","text"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["flexible_column_cont"]}},":itemsOrder":["container"]}},":itemsOrder":["container","container_1545902728"]},"markup_editor":{"id":"markup-editor-578ef48f1d","title":"Page 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