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CoWork、エージェント型エンタープライズを牽引するナレッジワーカー向けパーソナルAIエージェントへと進化\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"position":"left",":type":"snowflake-site/components/hero-system"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-ed0823c577","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-ce72928f95","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-daf35c01b6","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"press-body","backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-746f0bac54","layout":"SIMPLE","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"image":{"id":"image-761d80bf90","alt":"Snowflake Automotive Solutions","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c5b24d44-a000-4610-bd84-647b82daa368/snowflake-cowork-summit26-blog-header.png?preferwebp=true&quality=85","lazyEnabled":true,"height":"720","width":"1680",":type":"snowflake-site/components/image"},"text_copy":{"id":"text-8117ef8614","additionalClasses":"list--blue-bullets main-pr-body","text":"\u003Cp\u003ESnowflake CoWorkの最新イノベーションにより、企業はデータの分析から意思決定、実際のアクション実行までをより迅速に進められるようになり、組織全体でAIによるインテリジェンスを業務に活用できます。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E受け身のインサイトから積極的な行動へ：\u003Cbr\u003E\r\nArtifacts、Cortex Sense、パーソナライズ機能を強化する新たなツール群により、Snowflake CoWorkは、必要なインサイトをリアルタイムで提供し、迅速かつ効果的なアクションにつなげます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EナレッジワーカーのためのAI：\u003Cbr\u003E\r\nSnowflake CoWorkにより、Synopsys、Whoop、Under Armourといった企業の担当者の業務の効率化と、インサイトをより迅速にアクションにつなげられるよう支援\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E複雑さのないカスタマイズ機能：\u003Cbr\u003E\r\nCortex TrainingによりResolve AIなどの企業は、自社独自の環境やデータを活用した業務特化型モデルを構築でき、汎用モデルと比べて、より高い精度、低いレイテンシー、優れたパフォーマンスを実現\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EAIデータクラウド企業である\u003Ca href=\"https://www.snowflake.com/ja/\"\u003ESnowflake\u003C/a\u003E（ニューヨーク証券取引所：SNOW）は本日、企業全体でのAI活用と運用をさらに拡大する新たなAIイノベーションを\u003Ca href=\"https://www.snowflake.com/en/summit/?utm_source=google&utm_medium=paidsearch&utm_campaign=na-us-en-brand-summit-epb&utm_content=go-rsa-evp-evp-summit2026&utm_term=c-g-snowflake%20summit-p-802970208700&regcode=ATTENDSUMMIT26&gad_source=1&gad_campaignid=18471954638&gbraid=0AAAAADCzRJUJ3TDZrhQlo7nHAhlPyzvER&gclid=Cj0KCQjws83OBhD4ARIsACblj18BLJTC-ScKVqAbRD6AUehXpYi3WZ3sA_u3IzJwhmasmZzX_wcuBTgaAmDPEALw_wcB\"\u003ESnowflake Summit 26\u003C/a\u003Eにおいて発表しました。\u003Ca href=\"https://www.snowflake.com/ja/product/snowflake-intelligence/\"\u003ESnowflake CoWork\u003C/a\u003E（旧称Snowflake Intelligence）の最新機能により、企業はインサイトの把握から実際の業務アクションまでをより迅速に実行できるようになります。ナレッジワーカー向けに構築されたCoWorkは、よりスマートな働き方を支援するパーソナルAIエージェントです。\u003C/p\u003E\r\n\u003Cp\u003ECoWorkの最新機能強化は、分散したデータ、ツール、ワークフローによって業務全体の文脈を把握しづらいという課題を抱えるナレッジワーカーに、より高度なAI活用を提供することを目的としています。Artifacts2、Cortex Sense6、パーソナライズ機能などの新機能により、CoWorkはデータ、コンテキスト、AIを単一のエージェント体験として統合し、ビジネス部門が安全かつ信頼できる環境でAIを活用しながら、意思決定や業務実行を進められるよう支援します。同時に、データ部門向けには、新機能「Cortex Training⁴」により、データからのインサイト抽出や大規模なモデル学習をより容易に実現できるようになります。\u003C/p\u003E\r\n\u003Cp\u003E現在、世界13,900社以上の顧客がSnowflakeに重要なデータを預けています。Snowflakeは、そのデータに対して安全かつガバナンスが確保された環境内で直接AIを適用します。また、Snowflakeは、顧客がビジネスニーズに最適なAIモデルを選択できる選択肢と柔軟性を提供します。SnowflakeのAI製品群であるSnowflake Cortex AIを通じて、Anthropic、OpenAI、Google、Meta、Mistral、DeepSeek、そして本日発表されたSpaceXAIのモデルなど、業界をリードするAIモデルにアクセス可能です。その結果、SnowflakeのAIは高い性能だけでなく、エンタープライズ規模で信頼して利用できる基盤を提供し、企業が実践的なビジネス成果と持続的な競争優位性を実現できるよう支援します。\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cp\u003ESnowflake プロダクト担当上席副社長であるChristian Kleinermanは次のように述べています。\u003C/p\u003E\r\n\u003Cp\u003E「真の競争優位性を持つ企業とは、組織内のすべての従業員にAIの力を最大限に活用させる企業です。CoWorkはすべての従業員に対して、企業内のデータやツールを横断的に活用できるパーソナルAIエージェントを提供します。これにより、インサイトの獲得、アクションの実行、コラボレーションを信頼できる安全なガバナンス環境の中で一元的に行えるようになります。目指しているのは、AIが単にインサイトを提示するだけでなく、データ、コンテキスト、エンタープライズシステムをつなぐ“コントロールプレーン”となり、企業全体のアクションを推進する未来です」\u003C/p\u003E\r\n\u003Cp\u003ESnowflake CoWorkにより、企業のインサイトを実際のアクションへ\u003Cbr\u003E\r\n高度にパーソナライズされたAI体験を実現するため、CoWorkは企業内データに対する共通理解を構築し、組織全体のビジネスコンテキストと各ユーザーの役割を関連付けることで、一人ひとりに最適化されたインサイトやアクションを提供します。これにより、ユーザースキル⁴を活用して日常業務を自動化されたワークフローへと変換できるほか、Model Context Protocol（MCP）コネクター¹を通じてGoogleドライブ、Salesforce、Slackなどの業務ツールとも直接連携できます。その結果、チームは利用しているさまざまなシステムを横断しながら、アイデアをすぐにアクションへとつなげることが可能になります。これらのMCP機能は、Snowflakeが先日発表したNatomaの買収計画を基盤とするものです。Natomaのエンタープライズ向けMCPプラットフォームは、企業システムや各種ツールをまたいで動作するAIエージェントに対し、安全な接続性、ガバナンス、ユーザーのアイデンティティに基づく認可（Identity-Aware Authorization）、そして監査性を提供し、企業環境における安全かつ統制されたAI活用を支援します。\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cp\u003Eより高度な分析を必要とする複雑な問いに対しては、Deep Research2が、企業内に存在する構造化データおよび非構造化データ全体を対象に、多段階の推論と分析を実行し、包括的な調査を行います。一般的なコンシューマー向けのAIアシスタントやリサーチツールと異なり、Deep Researchは企業内のガバナンスが適用されたデータ環境内で動作し、ドキュメントや業務データを横断的に分析することで、出典を明示した実用的なインサイトを提供します。これにより、単なる数値だけでなく、その背景にある重要な「なぜ」を理解できるようになります。\u003Ca href=\"https://www.snowflake.com/en/product/ai/ai-research/\"\u003ESnowflakeのAI Research Team\u003C/a\u003Eによって構築された、agent swarm orchestration system（エージェント群を制御する仕組み）により、Deep Researchは構造化・非構造化エンタープライズデータ全体において最先端の性能を実現しています。これにより、\u003Ca href=\"http://www.snowflake.com/en/blog/engineering/arcticswarm-hybrid-deep-research\"\u003ESnowflakeのHybrid Deep Research Benchmark\u003C/a\u003Eでは単一エージェント型システムと比較して3割以上高い性能を示しています。\u003C/p\u003E\r\n\u003Cp\u003Eすでに数千社のSnowflakeユーザーが、CoWorkを活用して業務効率化や意思決定の迅速化を進めています。Synopsys、Whoop、Under Armourもその一例です。こうした導入を背景に、CoWorkは従来の受け身な質問応答型AIを超え、状況や文脈を理解しながら、意思決定と業務実行を支援するプロアクティブなインテリジェンスへと進化しています。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003Eエンタープライズ エージェントの共通基盤を構築：\u003Cbr\u003E\r\nSnowflakeは、AIエージェントが信頼性と実用性を確保するために必要なデータ、ビジネス定義、業務知識を自動的に統合する新機能であるCortex Senseを発表しました。Cortex Senseは、Snowflake CoWorkだけでなく、高速な開発を支援するコーディングエージェント「\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-coco-ai-coding-agent-modern-data-stack/\"\u003ESnowflake CoCo\u003C/a\u003E」にも対応しており、これらの情報をエージェントが即座に活用できる共通コンテキストレイヤーとして統合します。これにより、こうした基盤を持たないエージェントと比較して、導入直後からより高い性能を発揮できます。さらに、Cortex Senseには財務や営業といったさまざまなユーザーの役割向けに最適化された事前構築済みプラグイン群も含まれています。これらのプラグインは、スキル、ビジネスロジック、MCPコネクタを組み合わせることで、企業が業務知識に基づいた実運用レベルのエージェントを迅速かつ容易に展開できるよう支援します。手動設定の負担を軽減し、新たなエージェントごとにビジネスコンテキストを再構成する必要をなくすことで、Cortex Senseは、アイデア段階から本番環境に移行するまでの時間を数週間から数日に短縮します。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003Eインテリジェンスを積極的でパーソナライズされた行動につなげる：\u003Cbr\u003E\r\nCoWorkは、マルチエージェントオーケストレーションにより、複雑なリクエストを分解し、複数の専門AIエージェントを裏側で連携・制御することで、企業の業務スピード向上を支援します。ユーザー自身がエージェントを選択・管理する必要はありません。また、User Skillsを活用することで、独自の業務フローを自動化できるほか、新たな「Skill Catalog²」により、スキルの検索、共有、再利用を容易に行えるようになります。これにより、AIは日常業務をより効果的に支援するとともに、組織全体で実証済みのソリューションや生産性向上施策を拡張できるようになります。さらにCoWorkは、ユーザーメモリ4により個人の行動を継続的に学習し、タスクのスケジュール設定や、役割に応じたタイムリーなアクション提案・レコメンドを提供します。これらは、GmailやSlackなど、既存の業務ツール上で利用できます。パーソナライズされたAI体験をあらゆる業務環境へ拡張するため、新たにCoWork iOSモバイルアプリ2も提供されます。このアプリを使用することで、ナレッジワーカーはどこからでも迅速に、データに基づいてアクションを実行できるようになります。また、CoWork Slackbot6や、新しいMicrosoft Excelアドイン6にも対応します。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003Eダッシュボードから意思決定へ：\u003Cbr\u003E\r\nArtifactsには、新たに公開可能なダッシュボード機能⁴が追加されました。ナレッジワーカーは、ガバナンスが適用された信頼性の高いリアルタイムデータを、クリック操作だけでなく自然言語による対話を通じて探索できるようになります。アナリストはCoWork上で、ナレッジワーカーが自然言語で操作できる高度なダッシュボードを作成、公開することができ、分析担当者とのやり取りの回数を削減し、意思決定を迅速化します。さらに、Artifactsを保存、共有し、共同作業を行うことが可能となり、インサイトを日常業務に組み込まれた再利用可能な知識として活用することができます。これらすべては、ガバナンスとアクセス制御ポリシーを最初から組み込んだ形で提供されます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003Eこれらのイノベーションにより、エンタープライズAIは、単に質問に答えるだけでなく、実際の業務を遂行する新たな段階へと進化します。CoWorkは、現代企業における“アクション実行基盤”として、あらゆるインサイトを、ビジネスを前進させる人や業務フローへとつなぎます。\u003Cbr\u003E\r\n\u003Cbr\u003E\r\nSynopsys エンタープライズデジタルプラットフォーム担当バイスプレジデントのRamji Jagannathan氏は次のように述べています。\u003C/p\u003E\r\n\u003Cp\u003E「Snowflakeは、Synopsysの社員がビジネス全体でデータにアクセスし、理解し、これに基づき行動するための基盤となっています。当社は、売上オペレーション、法務、財務、製品管理、ITなどの部門向けに、20以上の業務特化型のエージェントを構築しており、これはすべてSnowflakeと連携しています。なぜならば、当社のデータ、ガバナンス、ビジネスコンテキストがSnowflake上に集約されているからです。Snowflake CoWorkとCortex Agentsにより、経営層や現場担当者は、サイロ化されたポータルや分断化されたツールを行き来しながら情報を探す必要がなくなりました。ブラウザやMicrosoft Teamsから自然言語で質問するだけで、実際のエンタープライズデータに基づいた信頼性の高いインサイトを得ることができます。当初はデジタルトランスフォーメーションの取り組みとして始まったことが、現在ではSynopsysにおける働き方そのものを変革する存在へと進化し、今も組織全体で新たな活用方法を見出し続けています」\u003C/p\u003E\r\n\u003Cp\u003EWHOOP アナリティクス担当バイスプレジデントのMatt Luizzi氏は次のように述べています。\u003C/p\u003E\r\n\u003Cp\u003E「Snowflake CoWorkによって、WHOOPチームのデータ活用のあり方を根本から見直すことができました。これまで専門アナリストへの依頼や手作業による対応が必要だった業務が、今では数百人の従業員に対してリアルタイムで利用可能になっています。定型的な問い合わせ対応を自動化し、各部門に信頼性の高いインサイトへの直接アクセスできるようになったことで、業務上の非効率を削減するとともに、データチームはより付加価値の高い業務に集中できるようになりました。その結果、意思決定の迅速化、業務運用の効率化、WHOOPにおける次世代AI活用を支える拡張性の高い基盤を実現しています」\u003C/p\u003E\r\n\u003Cp\u003ECortex Trainingにより、より高性能・低コスト・高速なAIモデルを実現\u003C/p\u003E\r\n\u003Cp\u003ESnowflakeは、Cortex AIを拡張し、企業が基盤モデルをより容易にカスタマイズ・学習できるよう支援するCortex Trainingを発表しました。フルマネージドGPU環境を活用しながら、基盤モデルのカスタマイズや学習をより容易に実行できるようになります。Cortex Trainingにより、企業はQwenやMistralファミリーなどのオープンウェイトの基盤モデルを、自社の業務領域や、データ、コスト要件に合わせて最適化することにより、GPU容量の不足への対応や低稼働による高額なGPU所有コストの課題を心配することなく利用できるようになります。またCortex Trainingにより、データを外部環境へ移動したり、分散化されたインフラストラクチャを自ら管理したりすることなく、データが存在するSnowflake上で直接モデルの学習を行うことができます。さらに、初期の顧客評価によると、同じGPU予算で最大2倍の学習ジョブを実行できます。これにより、企業は統合されたガバナンスを維持しつつ、AIソリューションの精度向上と応答速度の改善を大規模かつコスト効率よく実現できる大きな機会を得ることができます。\u003C/p\u003E\r\n\u003Cp\u003E本番環境におけるソフトウェア運用向けAIを提供するResolve AIは、独自の学習データを活用した強化学習（RL）による業務特化型モデルの構築を目的に、今後2年間で数百万ドル規模の投資を行い、Cortex Trainingを採用しました。Resolve AIは、これまで複数のプラットフォームにまたがって構築していたインフラを、Cortex Trainingによって安全かつガバナンスが確保された環境内に統合することで、モデル学習を効率化し、開発スピードを加速しています。\u003C/p\u003E\r\n\u003Cp\u003EResolve AIは、マルチエージェントハーネス、シミュレーション環境、評価ワークフローを基盤とした、本番環境向けの業務特化型AIエージェントを提供しています。AIモデルを強化学習によって高度化する中で、データや実行環境の管理権限を犠牲にすることなく、トレーニングや推論におけるGPU負荷の高い処理を実行できるインフラストラクチャが求められています。Resolve AIは拡張可能なコンピュートの抽象化によってパフォーマンスを実現しつつ、顧客がシステム、データ、デプロイ環境を完全に管理できるようにすることで、これを両立します。Cortex Trainingは、強化学習ワークロードを継続的に実行するガバナンスの効いたインフラストラクチャをResolve AIに提供し、本番環境のエンジニアリングチームにとって最も重要である推論タスクにおいて、汎用モデルを上回る性能を持つモデルを構築し、改善できるようにします。また、Snowflake自身もResolve AIの顧客であり、Snowflakeのエンジニアリング部門では、Resolve AIを既存のエージェント型ワークフローに取り込み、本番システムの大規模な運用管理を実現しています。これにより、より高速で信頼性の高いAI体験を企業に提供しています。\u003C/p\u003E\r\n\u003Cp\u003EResolve AI 創業者兼CEOのSpiros Xanthos氏は次のように述べています。\u003C/p\u003E\r\n\u003Cp\u003E「汎用APIモデルは今後も進化を続けるでしょう。しかし、それらを構築するだけでは限界があります。複雑なソフトウェアシステムの運用・管理には、特有の障害モデル、推論パターンそして最高水準の精度基準があります。Resolve AIは、専用の学習インフラストラクチャ、大規模なシミュレーション環境、そして実際の運用ワークフローに基づく評価により、このような制約を克服しています。Cortex Trainingは、こうしたResolve AI研究開発を大規模に推進する上で中核となる存在です」\u003C/p\u003E\r\n\u003Cp\u003Eガバナンスの効いたエンタープライズデータのより近くで学習を行うことで、Snowflakeは実験段階から本番運用へ移行するまでのプロセスを簡素化します。その結果、高コストな最先端APIモデルへの依存から脱却し、既存のデータ・運用基盤と同じガバナンス環境上で、より低コストかつ高精度な業務特化型モデルを活用できるようになります。\u003C/p\u003E\r\n\u003Cp\u003E詳細情報：\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003ESnowflake CoWorkの最新情報についてはこの\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-cowork-personal-work-agent\"\u003Eブログ投稿\u003C/a\u003Eをお読みください。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003ESnowflake Cortex AIの新たなイノベーションの全容についてはこの\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-for-ai-enterprise-ai-platform\"\u003Eブログ投稿\u003C/a\u003Eをお読みください。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003ESnowflake Summit 26で公開されるすべてのイノベーションや発表については\u003Ca href=\"https://www.snowflake.com/ja/news/\"\u003ESnowflakeのニュースルーム\u003C/a\u003Eをご覧ください。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.linkedin.com/company/snowflake-computing/\"\u003ELinkedIn\u003C/a\u003E、X（\u003Ca href=\"https://x.com/Snowflake?_fsi=GoSftacn\"\u003E本社公式\u003C/a\u003E・\u003Ca href=\"https://x.com/Snowflake_Japan\"\u003E日本公式\u003C/a\u003E）でも最新の情報や発表内容を発信しています。#SnowflakeSummitをフォローしてご覧ください。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Csup\u003E1. \u003C/sup\u003ESnowflake製品は現在一般提供中です。\u003Cbr\u003E\r\n\u003Csup style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E2.\u003C/sup\u003E Snowflake製品はまもなく一般提供開始されます。\u003Cbr\u003E\r\n\u003Csup style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E3. \u003C/sup\u003ESnowflake製品は現在パブリックプレビュー中です。\u003Cbr\u003E\r\n\u003Csup style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E4.\u003C/sup\u003E Snowflake製品はまもなくパブリックプレビューが開始されます。\u003Cbr\u003E\r\n\u003Csup style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E5. \u003C/sup\u003ESnowflake製品は現在プライベートプレビュー中です。\u003Cbr\u003E\r\n\u003Csup style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003E6. \u003C/sup\u003ESnowflake製品はまもなくプライベートプレビューが開始されます。\u003C/p\u003E\r\n\u003Cp\u003E将来予想に関する記述\u003C/p\u003E\r\n\u003Cp\u003E本プレスリリースには、明示的または黙示的な将来予想に関する記述（forward-looking statements）が含まれています。これには、(i) 当社の将来の業績、目標または財務状況、(ii) 当社の事業戦略、計画、事業機会または優先事項、(iii) 開発中または一般提供前のものを含む新規または強化された製品、サービスおよび技術提供のリリース、採用および利用、(iv) 市場規模および成長、市場動向ならびに競争環境に関する事項、(v) 人工知能（AI）、エンタープライズAI革命、Snowflake Cortex AI、Snowpark、Snowflakeマーケットプレイス、AIデータクラウド、および特定業界や製品カテゴリー向けAIデータクラウドに関する当社のビジョン、戦略および期待される効果（Aデータクラウドの期待されるメリットやネットワーク効果を含む）、ならびに (vi) パブリッククラウドプラットフォームやAIモデルを含む第三者製品およびプラットフォームとの当社の製品、サービスおよび技術提供の統合性、相互運用性および提供可能性に関する記述が含まれます。\u003C/p\u003E\r\n\u003Cp\u003Eこれらの将来予想に関する記述は、多くのリスク、不確実性および前提条件の影響を受けるものであり、その詳細については、Snowflakeが米国証券取引委員会（SEC）に提出する四半期報告書（Form 10-Q）および年次報告書（Form 10-K）の「Risk Factors（リスク要因）」をはじめとする各記載をご参照ください。\u003C/p\u003E\r\n\u003Cp\u003Eこれらのリスク、不確実性および前提条件により、実際の結果は、将来予想に関する記述で示唆または予測された内容と大きく異なる可能性があります。したがって、将来予想に関する記述を将来の出来事に関する予測として過度に信頼しないようお願いいたします。\u003C/p\u003E\r\n\u003Cp\u003E©2026 Snowflake Inc. All rights reserved. Snowflake、Snowflakeのロゴ、および本書に記載されているその他すべてのSnowflakeの製品、機能、サービス名は、米国およびその他の国におけるSnowflake Inc.の登録商標または商標です。本書で言及または使用されているその他すべてのブランド名またはロゴは、識別目的でのみ使用されており、各所有者の商標である可能性があります。Snowflakeが、必ずしもかかる商標所有者と関係を持ち、または出資や支援を受けているわけではありません。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-large"},"experiencefragment":{"id":"experiencefragment-73cb9bc8a1","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/ja/site/press/press---about-snowflake/press---about-snowflake/jcr:content","configured":true,"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn 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