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aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-2c0a4f26bf","text":"\u003Ch3\u003E\u003Ci\u003E現在、高性能なAIモデルが広く利用可能になっています。しかし、競合他社が複製できないのは、自社だけが提供できるビジネスコンテキストと、自社が保持する学習内容です。\u003C/i\u003E\u003C/h3\u003E\n\u003Cp\u003EAIが使用するすべての事実が正確であっても、意思決定が間違っている場合、より優れたモデルを採用してもこの種の失敗は解決しません。\u003C/p\u003E\n\u003Cp\u003E2週間前にプレミアムスキーを購入し、その後、それに合うブーツのページを3回再訪した架空の優良顧客、Mayaについて考えてみましょう。彼女のコンバージョン予測確率は高いため、企業の\u003Cb\u003EAIは彼女が閲覧したブーツの20%オフのプロモーションを送信します\u003C/b\u003E。\u003C/p\u003E\n\u003Cp\u003Eしかし、AIにはこの一連のイベントのシーケンスを取り巻くコンテキストが見えていません。スキーは破損した状態で到着し、交換品の発送は遅れており、品質に関する問い合わせは未解決のままです。企業のポリシーでは、このような状況において、クレームが未解決の間はブーツコレクションを割引対象にすべきではなく、収益性の高い顧客維持を優先すべきであると規定されています。\u003C/p\u003E\n\u003Cp\u003E企業全体としてはこれらすべてを把握していますが、意思決定の時点でそれらの事実がAIに伝達されていなかったため、このケースではAIが誤った選択をしてしまいました。\u003C/p\u003E\n\u003Ch3\u003E問題はコンテキスト\u003C/h3\u003E\n\u003Cp\u003E上記の例では、AIのレコメンデーションは、未解決のサービス障害、プレミアムラインの価格の完全性、悪い体験の最中にある顧客関係の価値といった周囲の状況を無視し、即時のコンバージョン（つまり、Mayaに次の購入をさせること）を最適化しました。\u003C/p\u003E\n\u003Cp\u003Eコンテキストこそが、AIを一般的なアシスタントから、自社のブランド、顧客、ビジネス、そして達成しようとしている成果に関する専門家へと変えるものです。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-c8936c9de0","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"持続的な競争優位性としてのコンテキスト","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1884924545":{"id":"blog-text-59233fb497","text":"\u003Cp\u003E\u003Cbr\u003E\n意思決定が加速するにつれて、コンテキストの欠如はより危険になります。人間のスピードであれば、タイミングの悪いキャンペーンが1つ生成され、マーケティング担当者が週次レビューで気づく程度かもしれません。しかしAIのスピードでは、誰もそのパターンに気づかないうちに、同じ盲点が何千もの誤った意思決定を引き起こす可能性があります。\u003C/p\u003E\n\u003Cp\u003Eこれこそがマーケティングリーダーを悩ませるべき問題ですが、AI予算に関する議論は依然としてモデルの選択に支配されています。\u003C/p\u003E\n\u003Ch3\u003E高性能なモデルの幅広い普及（誰でも利用可能）\u003C/h3\u003E\n\u003Cp\u003E品質、スピード、専門性、コストは依然として重要ですが、現在ではほとんどの企業が高性能なモデルにアクセスできます。\u003C/p\u003E\n\u003Cp\u003E競合他社が迅速に再現するのに苦労するのは、企業が顧客をどのように評価し、成長と利益率のバランスをとり、ブランドを保護し、長年のテスト結果を解釈するかという背景にある、蓄積されたビジネス上の判断です。\u003C/p\u003E\n\u003Cp\u003Eこれこそが\u003Cb\u003Eコンテキストの優位性\u003C/b\u003Eです。つまり、企業固有のビジネス理解を各意思決定にもたらし、その結果から将来の状況に向けて学習することです。\u003C/p\u003E\n\u003Ch3\u003Eコンテキストにおける顧客360への投資\u003C/h3\u003E\n\u003Cp\u003Eほとんどのマーケティング組織にとって、ジャーニーは既存の顧客360への投資から始まり、そこから外側へと拡大していきます。Mayaの状況を例に挙げると、このような投資には次のような利点があります。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E顧客360による永続的な顧客の真実の保持：\u003C/b\u003EMayaの場合、彼女の優良顧客としてのステータスや最近のスキー購入と、現在のブーツページでのアクティビティ、企業との関係価値、および彼女への連絡の許可を結び付けます。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E強化された顧客360による状況の説明：\u003C/b\u003Eサービスコールやオンラインレビューなどの非構造化データソースから、Mayaが不満を抱いており、緊急に交換品を必要としていること、そしてすでにリゾートに到着しているもののスキーができない状態であることがわかります。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eマーケティングコンテキストによる目的と運用ロジックの追加：\u003C/b\u003Eマーケティング部門はプレミアムブーツのキャンペーンが実施中であることを知っていますが、サービスの回復を優先します。プロモーションは一時停止する必要があります。ここでの成功とは、Mayaを顧客として維持することであり、過去のテストでは、サービス障害の発生後は回復を優先する対応の方が良い結果をもたらすことが示唆されています。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eエンタープライズコンテキストによるマーケティング外からの信頼できる事実の提供：\u003C/b\u003Eサービス、コマース、および製品システムにより、Mayaのクレームがまだ未解決であること、交換品が利用可能であること、および保証が適用されることが確認されます。ブランド、財務、および法務チームが、企業の対応方法を決定します。これはある現実を示しています。マーケティングコンテキスト、あるいは単一の部門のコンテキストだけでは不十分であるということです。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E接続されたエンタープライズコンテキストによる状況全体の活用：\u003C/b\u003E情報は、すでにそれを所有し維持しているチームやシステムに保持されます。マーケティング部門は、個別のコピーを作成して維持することなく、必要なときに関連する事実を使用できます。意思決定の時点で、適切なサイズのサブセットが、マーケター、アプリケーション、またはエージェントが実際に必要とする概要になります。次に行うべき最適なアクションは、もはや推測ではなくなります。問題を解決し、関係を保護し、適切なタイミングで再びエンゲージメントを図ります。\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1049856448":{"id":"blog-text-53a3e989db","text":"\u003Ch3\u003E持続的な競争優位性としてのコンテキスト\u003C/h3\u003E\n\u003Cp\u003Eコンテキストが持続的な優位性となる理由を、7つの原則で説明します。\u003C/p\u003E\n\u003Ch4\u003E関連性を生み出すコンテキスト\u003C/h4\u003E\n\u003Cp\u003E事実は始まりにすぎません。このケースでは、Mayaがスキーを購入し、破損した商品を受け取り、現在も未解決の問い合わせがあるという事実が確認されます。\u003C/p\u003E\n\u003Cp\u003Eこれらを合わせると、\u003Cb\u003E真実、意味、意図、記憶\u003C/b\u003E、そして\u003Cb\u003E境界\u003C/b\u003Eが、AIに状況の有用な理解を与えるコンテキストの構成要素となります。\u003C/p\u003E\n\u003Cp\u003Eビジネス上の意味により、枠組みがクロスセル（ブーツ）からサービスの回復へと変わります。意図により収益性の高い顧客維持が目標となり、記憶により過去の回復優先テストで企業が学んだことが引き出されます。そして境界により、関連する抑制（広告やキャンペーン）、承認、および同意のルールが適用されます。\u003C/p\u003E\n\u003Ch4\u003E未活用のコンテキストが生み出す機会\u003C/h4\u003E\n\u003Cp\u003Eマーケティング部門は長年、最もカウントしやすいシグナルの最適化に費やしてきました。クリック数、購入数、キャンペーンの反応は依然として重要ですが、それらが何かが起こった\u003Ci\u003E理由\u003C/i\u003Eを説明することはめったにありません。\u003C/p\u003E\n\u003Cp\u003Eブランドや製品に対する顧客の視点などの情報は、オンラインレビュー、チャットのトランスクリプト、ソーシャルメディアの投稿など、いわゆる非構造化データに保存されていることがよくあります。現在、組織はトランザクションの記録だけではわからないインサイトを、これらのデータセットから取得できるようになっています。\u003C/p\u003E\n\u003Cp\u003EAIは、これまで利用が困難だった情報を、実用的なビジネスシグナルに変える機会を生み出します。\u003C/p\u003E\n\u003Ch4\u003E適切なコンテキストが生み出す精度\u003C/h4\u003E\n\u003Cp\u003Eすべてにアクセスできることが、コンテキスト戦略ではありません。すべてをAIに投入して最善の結果を期待するのは簡単です。\u003C/p\u003E\n\u003Cp\u003Eこれまで見てきたように、コンテキストが少なすぎるとAIは盲目になります。しかし、多すぎると、古い情報、矛盾する情報、または不要な情報で意思決定に負担がかかり、コストとリスクが増加する一方で、関連するシグナルを見つけるのが難しくなります。ロングコンテキスト言語モデルに関する\u003Ca href=\"https://arxiv.org/abs/2307.03172\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E調査\u003C/a\u003Eでも、モデルがコンテキストウィンドウの制限に達する前であっても、関連情報が長い入力の中に埋もれていると、その情報の信頼性が低下する可能性があることがわかっています。Mayaの場合、AIは彼女の顧客履歴全体、すべてのサービストランスクリプト、またはすべてのブランドドキュメントを必要としません。AIが必要とするのは、彼女のサービスに関する問い合わせがまだ未解決であるという事実など、この特定の意思決定を実質的に変更する、現在のいくつかの事実とルールです。\u003C/p\u003E\n\u003Cp\u003Eしたがって、コンテキストレイヤーは、\u003Cb\u003E意思決定に必要な、最小限で十分な、関連性のある、最新の、信頼できる、承認されたコンテキスト\u003C/b\u003Eを組み立てる必要があります。\u003C/p\u003E\n\u003Cp\u003Eまた、「最新」が常に「リアルタイム」を意味するわけではないことに注意することも重要です。マーケティングの同意は変更を直ちに反映する必要があるかもしれませんが、承認されたブランドポリシーは数か月にわたって有効なままになる場合があります。\u003C/p\u003E\n\u003Ch4\u003E共有コンテキストが生み出す連携\u003C/h4\u003E\n\u003Cp\u003EMayaの状況が間違った方向に進んだのは、企業が全体像を断片的にしか把握していなかったためです。マーケティング部門は彼女のエンゲージメントを確認し、サービスチームは破損した配送のケースを調べ、ブランド部門はこのシナリオではブーツコレクションが割引から保護されていることを知っていました。\u003C/p\u003E\n\u003Cp\u003E各チームはそれぞれの境界内で情報に基づいた意思決定を行うことができましたが、他の部門からコンテキストを取り込まなければ、どのシステムも適切なエンドツーエンドの意思決定を行うことはできませんでした。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003Eしたがって、AIはサイロを排除するのではなく、サイロを自動化していることがよくあります\u003C/b\u003E。\u003C/p\u003E\n\u003Cp\u003E共有コンテキストにより、すべてのチームが自部門の制御を放棄することなく、単一の顧客の状況とビジネス成果を中心にこれらのシステムを連携させることができます。\u003C/p\u003E\n\u003Ch4\u003Eガバナンスの効いたコンテキストが生み出す信頼\u003C/h4\u003E\n\u003Cp\u003EAIシステムには、許可されたアクセス、明確なアクションのしきい値、および監査可能な記録を備えた、信頼できる唯一の情報源が必要です。\u003C/p\u003E\n\u003Cp\u003Eこれらの制御は、AIがアクションを起こした後にのみ結果を検査するのではなく、AIの意思決定の前および最中に利用可能なコンテキストを形成する必要があります。制御が前者を行う場合、ガバナンスにより、人間がすべてのレコメンデーションに関与することなく、より大きな自律性が可能になります。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003Eガバナンスは、信頼できる自律性のためのオペレーティングシステムです\u003C/b\u003E。\u003C/p\u003E\n\u003Ch4\u003E所有されたコンテキストが生み出す制御\u003C/h4\u003E\n\u003Cp\u003E制御とは、テクノロジーの変化に合わせて選択肢を維持することを意味します。\u003C/p\u003E\n\u003Cp\u003Eエンタープライズがコンテキストを制御していれば、時間をかけて構築されたビジネス理解を移行させることができます。モデルやアプリケーションを変更しても、組織がすでに知っていることを再構築する必要はありません。\u003C/p\u003E\n\u003Cp\u003E閉じ込められたコンテキストは異なる動作をします。基盤となるデータをエクスポートすることはできても、その周囲に構築されたインテリジェンスは、それを作成したプラットフォームに結び付けられたままになる可能性があります。ベンダーが他のシステムによるそのコンテキストの使用許可方法を変更すると、アクセスも変更される可能性があります。\u003C/p\u003E\n\u003Cp\u003Eそこに学習が蓄積されればされるほど、ビジネスが学習したことの一部を失うことなく離れることは難しくなります。\u003C/p\u003E\n\u003Ch4\u003E複利的な優位性を生み出すコンテキスト\u003C/h4\u003E\n\u003Cp\u003Eすべての意思決定は証拠を生み出します。AIがアクションを起こし、顧客が反応し、ビジネス上の成果が続き、人間がレコメンデーションを承認、拒否、または修正する場合があります。\u003C/p\u003E\n\u003Cp\u003Eその証拠は、コンテキスト基盤に戻る必要があります。\u003C/p\u003E\n\u003Cp\u003EMayaのケースでは、有用な情報は単にプロモーションが抑制されたことだけではありません。企業は、サービスリカバリが関係を保護したかどうか、彼女が後で割引なしでブーツを購入したかどうか、そして人間の介入が結果を改善したかどうかを学習する必要があります。\u003C/p\u003E\n\u003Cp\u003Eこれらの結果は、次に類似した意思決定を行う際の記憶となります。時間が経つにつれて、組織はどの介入が高価値の関係を維持するか、どのオファーが追加の需要を生み出すか、そして人間の判断がAIのレコメンデーションを継続的に改善する場所を学習できます。\u003C/p\u003E\n\u003Cp\u003EAIを使用するだけでは、学習上の優位性は生まれません。アクション、結果、および修正が、企業が再利用できるコンテキストとして戻ってくるときに、その優位性は複利的に増加します。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E上記で説明した最初の6つの原則は、今日の意思決定を改善します。この7番目の原則は、それ以降のすべての意思決定を改善します\u003C/b\u003E。\u003C/p\u003E\n\u003Ch3\u003Eエンタープライズコンテキストレイヤーはどこに構築すべきでしょうか。\u003C/h3\u003E\n\u003Cp\u003Eこれは誰もが抱いている本当の疑問です。通常、選択肢はモデルプロバイダー、（マーケティング）アプリケーション、またはデータプラットフォームのいずれかです。\u003C/p\u003E\n\u003Cp\u003Eモデルプロバイダーとマーケティングアプリケーションはどちらも有用なインテリジェンスを提供しますが、どちらもエンタープライズコンテキストの理想的で永続的な基盤となるようには設計されていません。\u003C/p\u003E\n\u003Cp\u003Eモデル内にレイヤーを構築すると、そのモデルファミリーへの依存関係が生じる可能性があります。アプリケーション全体に分散させると、別の問題が発生する可能性があります。各システムが独自の定義、ポリシー、および記憶を開発し、何も調整されなくなり、同じ指標が3つの異なるソリューションにつながるまでになります。\u003C/p\u003E\n\u003Cp\u003E実際には、ガバナンスの効いた重心はエンタープライズ基盤に属しており、そこでは意味が一貫性を保ち、ポリシーが一度だけ適用され、承認されたモデルとアプリケーション全体で学習が蓄積されます。\u003C/p\u003E\n\u003Ch3\u003Eコンテキストのためのガバナンスの効いたデータおよびAI基盤としてのSnowflake\u003C/h3\u003E\n\u003Cp\u003ESnowflake AIデータクラウドは、次の3つの実践的な方法でこのようなモデルをサポートします。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E意思決定の瞬間に利用可能な適切なコンテキスト：\u003C/b\u003Eマーケティング部門は、何が起こったかを説明する非構造化された証拠を含め、意思決定の背後にあるビジネスデータを、ガバナンスの効いた1つの基盤に取り込むことができます。\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon-context/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESnowflake Horizon Context\u003C/a\u003Eを使用すると、AIはその基盤を使用し、コンテキストの起源を保持し、一貫したビジネス上の意味を適用できます。エージェントは、指標を集計できる場合とできない場合を把握するなど、推測ではなく信頼できる定義に基づいて推論できます。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E選択したエコシステム全体での再利用性：\u003C/b\u003Eセマンティックビューを使用すると、ビジネス上の意味を一度定義し、Snowflakeの社内外でチームが使用するツールにその意味を持ち込むことができます。互換性のあるツールはすべて、各インターフェイスで再構築する必要なく、同じガバナンスの効いたロジックから機能できます。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E複利効果：\u003C/b\u003ESnowflakeは定義とリネージを管理できますが、ビジネスが何を学習すべきかを決定することはできません。マーケティング部門は、マーケターがAIのレコメンデーションを変更した場合を含め、各AIの意思決定の後に何が起こったかを把握し、その証拠を再利用可能なコンテキストに戻す必要があります。Snowflakeは、その記憶にガバナンスの効いた基盤と一貫した意味を提供します。\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_2094103794":{"id":"blog-text-aa6c73a973","text":"\u003Ch3\u003EMayaの例に戻る\u003C/h3\u003E\n\u003Cp\u003Eこの例では、不完全なコンテキストにより、AIはMayaに20%の割引オファーを送信します。 \u003C/p\u003E\n\u003Cp\u003Eしかし、接続され、ガバナンスの効いたコンテキストを使用すると、同じモデルがサービスリカバリの瞬間を認識し、問題を解決し、関係を保護し、適切なタイミングで彼女に再びアプローチします。\u003C/p\u003E\n\u003Cp\u003E彼女の反応は、同様の立場にある次の顧客のコンテキストになります。\u003C/p\u003E\n\u003Cp\u003Eモデルは変化します。コンテキストは制御下に置かれ、意思決定のたびにスマートになる必要があります。\u003C/p\u003E\n\u003Cp\u003EAIがすでに大量に行っている1つの意思決定から始め、現在AIが認識できない重要な事実をすべて文書化します。それがコンテキストのロードマップです。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003Eマーケティングリーダー向け：\u003C/b\u003E\u003Ca href=\"https://www.snowflake.com/en/solutions/departments/marketing/\"\u003ESnowflakeのマーケティング部門向けAIデータクラウドの詳細を見る\u003C/a\u003E。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003Eデータおよびテクノロジーリーダー向け：\u003C/b\u003E\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/en/blog/horizon-context-governed-context/\" target=\"_blank\"\u003ESnowflake Horizon Contextがガバナンスの効いたビジネスコンテキストを収集、強化、アクティブ化する方法をご確認ください\u003C/a\u003E。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","quote_item","blog_text_1884924545","blog_text_1049856448","blog_text_2094103794"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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