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Intelligenceの初のエンタープライズ展開です。年末までに、このアシスタントはすでに33万件以上の質問に回答し、ユーザーの作業スピードと意思決定の改善を支援しました。\u003C/p\u003E\n\u003Cp\u003E過去数か月にわたって、私たちは、販売データの構造化、非構造化コンテンツの知識検索、エージェントの動作テスト、指示の改良など、エンタープライズAIアシスタントの構築の技術的基盤について、開発者に焦点を当てた投稿をいくつか公開してきました。これらの投稿は有益でしたが、技術的な質問だけではありません。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003Eプロジェクトの人員はどのように配置されましたか？\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E開発のタイムラインはどのようなものでしたか？\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E信頼を失墜させることなく、どのようにロールアウトできたか。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E実際に効果があった変更管理戦術は何ですか？\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eローンチ後、どのような成果がありましたか？\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003Eこの記事では、チーム編成、ローンチフェーズ、スコープ戦略、そしてMVPから信頼できるエンタープライズケイパビリティへのスケーリングなど、非技術的な要素に焦点を当てます。\u003C/p\u003E\n\u003Ch2\u003E開発チームとローンチフェーズ\u003C/h2\u003E\n\u003Cp\u003E私たちは2025年2月下旬に、断片化したツール、ウェブページ、ドライブの中からGTMチームが適切なドキュメントを見つけられるようにRAGベースのナレッジアシスタントを構築するという、狭い目標でスタートしました。当時、プロジェクトには専任のデータサイエンティストが1人でした。\u003C/p\u003E\n\u003Cp\u003E2025年5月後半までに、Snowflake Intelligenceを使用してGTMのAIアシスタントをフル活用することを決定しました。ナレッジコンテンツのインデックス作成だけでなく、構造化された販売データとマーケティングデータの統合も可能にします。このシフトによって品質と信頼性のハードルが変わり、私たちはチームを3～4人のデータサイエンティストに拡大し、専任のプロダクトマネージャーを追加しました。\u003C/p\u003E\n\u003Cp\u003E初日から、品質とユーザーの信頼を\u003Cb\u003EP（-1）\u003C/b\u003Eとして扱いました。ユーザーインタビューとAIプロジェクトの過去の経験により、以下が明らかになりました。最初の印象で定着率が決まります。初期のエクスペリエンスが信頼性に欠けるものである場合、その再構築には、最初から準備するよりも劇的に多くの労力が必要になります。\u003C/p\u003E\n\u003Cp\u003Eそのため、私たちは段階的なロールアウト戦略を選択しました。小規模で開始し、品質とワークフローを検証し、拡大します。\u003C/p\u003E\n\u003Cp\u003Eローンチフェーズの概要は以下のとおりです。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-a7ba44eeb6","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--d60c33c2-b9c6-4714-86e9-9b7ddbf5ee83/pilot-1.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"pilot-1","height":"438","width":"1468",":type":"snowflake-site/components/image"},"blog_text_2105309366":{"id":"blog-text-5cc98b22c5","text":"\u003Cp\u003Eヒント：よくある失敗モードは、エージェントが一貫して品質基準を満たす前に広範な使用に突進してしまうことです。AIエージェントのプロトタイピングは簡単です。何千ものユーザーに信頼できるエージェントをローンチするのは至難の業です。早期に信頼を獲得するための時間とスペースをチームに与えれば、後でその効果を回収できます。\u003C/p\u003E\n\u003Ch2\u003EMVP(最小限の機能)から、機能拡張へ\u003C/h2\u003E\n\u003Cp\u003Eエンタープライズ環境は複雑です。Snowflakeは、セールスおよびマーケティング組織全体で、ワークフローやニーズの異なる15以上のペルソナをサポートしています。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003Eアカウントエグゼクティブ\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eソリューションエンジニア\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ESDR\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eプロダクトマーケティングマネージャー\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eパートナー/アライアンスマネージャー\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eプロフェッショナルサービススペシャリスト\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eセールスおよびマーケティングのリーダーシップ\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eキャンペーンマネージャー\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eその他…\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003Eどの製品もそうですが、私たちは「すべてを結びつけ、ユーザーがそれを把握してくれることを期待する」というトラップを回避しました。代わりに、私たちは意図的に、最も迅速に価値を提供できるペルソナを絞ったMVP(最小限の機能実装)から始めました。\u003C/p\u003E\n\u003Cp\u003Eオーディエンスの規模とペルソナごとの開発工数に基づき、\u003Ci\u003EAE、SE、SDR\u003C/i\u003Eに焦点を当てたGAローンチを実施しました。これら3つのグループは、\u003Ci\u003E6,000人のユーザーターゲットオーディエンスの約50%\u003C/i\u003Eを占めており、範囲を維持しながら早期の効果を最大化できました。\u003C/p\u003E\n\u003Cp\u003EGA後、私たちは意図的に拡張しました。コアオーディエンスの信頼性を損なうことなく、追加のペルソナや機能を一歩ずつ解放していきます。\u003C/p\u003E\n\u003Cp\u003E以下に、時間の経過とともにエージェントの機能が拡張された例を例示します。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGAの範囲：\u003C/b\u003Eナレッジアシスタント＋Salesforceのコアデータ、製品の使用状況、財務データ。\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGA後の最初の月：\u003C/b\u003Eバグ修正と同じソースにおいての範囲と深度の拡大。\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eその後の波：\u003C/b\u003Eマーケティングデータ、通話のトランスクリプトやEメール、パートナーシップデータ、ウェブ検索サポートなどの新しいデータソース。\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E2025年9月中旬にAIアシスタントの提供を開始した時点では、6つのセマンティックビューと48個のテーブル、約1,400のカラムで構成されていました。GA後、40以上の新機能/データソースをリリースし、データレイヤーを10のセマンティックビュー、64のテーブル、1,750以上のカラムに拡張しました。\u003C/p\u003E\n\u003Cp\u003Eヒント：あまりに範囲が広いことが、エンタープライズAIイニシアチブが停滞する最も一般的な理由の一つです。まず、エージェントが答える質問の数が少なくても、正しく答える範囲から始めます。早期障害後に定着率を取り戻すよりも、信頼の確立後に機能を拡張する方が容易です。\u003C/p\u003E\n\u003Ch2\u003Eユーザーアクティベーションと変更管理\u003C/h2\u003E\n\u003Cp\u003Eエンタープライズソリューションの導入を成功させるには時間がかかり、多くの場合、丸３か月かそれ以上かかります。他の製品ロールアウトと同様に、ユーザーは当然ながら、イノベーター、アーリーアダプター、アーリーマジョリティ、レイトマジョリティ、ラガードといった採用曲線に沿って下降します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1962251299":{"id":"image-4b20461273","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--866b281f-bbd7-43e6-8b5f-717bab7c3925/pilot-2.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"pilot-2","height":"684","width":"1134",":type":"snowflake-site/components/image"},"blog_text_1827519857":{"id":"blog-text-1047c5fcee","text":"\u003Cp\u003E\u003Ci\u003EEverett Rogersによるイノベーションの拡散理論。\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003Eパイロットフェーズとベータフェーズでは、新しいツールを試してみたいと考える上位のユーザーからエンゲージメントを得られる傾向があります。GAになり、数千ユーザーまで拡大できれば、現実は変化します。ユーザー定着が十分でない場合は、遅れて採用するユーザーが現れ、期待が高まり、不満が表面化します。\u003C/p\u003E\n\u003Cp\u003Eこれが多くのAIイニシアチブが失速する原因です。つまり、テクノロジーが機能しないからではなく、ユーザーがAIを完全に受け入れないからです。\u003C/p\u003E\n\u003Ch3\u003Eユーザーに実際に試してもらう\u003C/h3\u003E\n\u003Cp\u003E毎日のワークフローの一部としてAIアシスタントを使用することは、大きな習慣変化を意味します。ほとんどのユーザーは、ソリューションが\u003Ci\u003E劇的に改善\u003C/i\u003E（10倍高速、10倍簡単）、または以前はできなかったことを実現しない限り、乗り換えようとはしません。既存ツールと機能が拮抗しているだけで十分であることは稀です。特に、レイト・アダプターの間ではなおさらです。\u003C/p\u003E\n\u003Cp\u003Eとはいえ、最高のプロダクトであっても、そもそも試してみなければユーザーを獲得することはできません。\u003C/p\u003E\n\u003Cp\u003Eでは、ユーザーがAIアシスタントを\u003Ci\u003E試し\u003C/i\u003Eて\u003Ci\u003E戻ってき続ける\u003C/i\u003Eようにするには、どうすればよいでしょうか。\u003C/p\u003E\n\u003Ch3\u003Eまず品質を保証し、粘り強さく最適化する\u003C/h3\u003E\n\u003Cp\u003E私たちのアプローチは、ローンチフェーズに合わせて慎重かつ順序立ったものでした。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_361604255":{"id":"image-0b8790ba1d","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ff86056b-5048-48af-83ee-966ef92ee613/pilot-3.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"pilot-3","height":"318","width":"1306",":type":"snowflake-site/components/image"},"blog_text_1428120683":{"id":"blog-text-51ba90316d","text":"\u003Cp\u003Eパイロットフェーズでは、\u003Ci\u003E品質と信頼性\u003C/i\u003Eのみに注力しました。私たちの主な目標は、正確性、信頼性、基本的な有用性を保証することでした。品質は\u003Cb\u003EP（-1）\u003C/b\u003Eです。\u003C/p\u003E\n\u003Cp\u003Eベータ版では、品質が証明された後、\u003Ci\u003E機能の完全性と粘り強さ\u003C/i\u003Eに重点を移しました。次のような疑問が浮かびました。\u003Ci\u003Eこのエージェントは、ユーザーが毎週繰り返し使いたくなるような現実的な問題を解決するか。\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003Eその結果、自信がつきました。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E92% 以上：ベータ版ユーザー\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E70%を超える週間アクティブユーザー（WAU）維持率\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003Eそのメトリクスから、スケーリングする価値のある製品があることがわかりました。\u003C/p\u003E\n\u003Ch3\u003E勢いと組織の興奮を醸成する\u003C/h3\u003E\n\u003Cp\u003EGAフェーズでは、私たちの主な質問はシンプルになりました。\u003Ci\u003EユーザーはGTM AI Assistantを認識しているでしょうか。また、実際に試しているのでしょうか。\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E導入を促進するために、Snowflakeはセールスイネーブルメントチームと緊密に連携し、以下の基本事項を確実に実施しました。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E社内向け製品専用ページ\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E明確なユーザーガイドとハウツードキュメント\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eベストプラクティスを共有するためのトップユーザーの短い動画\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eフィードバックや機能リクエストのためのSlack専用チャネル\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003Eそこから積極的にモメンタムを構築しました。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E個々のチームとのEメールやライブデモのローンチ\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E全員参加のミーティングやチームミーティングで、セールスエグゼクティブや当社のCEOが定期的に言及する\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E週次の採用レポートをセールスリーダーと共有し、チーム内の参加を促進\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003Eイネーブルメント、可視性、リーダーシップのサポートの組み合わせが、非常に重要であることがわかりました。\u003C/p\u003E\n\u003Ch3\u003E結果として：大規模な導入の継続\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_76636164":{"id":"image-7a2ee82e2a","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--1f57faef-e252-4122-9a54-fb7d55b0cbe2/pilot-4.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"pilot-4","height":"722","width":"1288",":type":"snowflake-site/components/image"},"blog_text_1129566895":{"id":"blog-text-f7ee81501c","text":"\u003Cp\u003E\u003Ci\u003E統制のとれたアクティベーションと変更 管理を通じて、GA後の3か月間にGTM AIアシスタントを大幅の定着に導きました。\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003Eすべての努力が実を結びました。GA後2週間以内に、ローンチされたユーザーの約\u003Cb\u003E25%\u003C/b\u003Eが少なくとも1回、このエージェントを試していました。その数は年末までに、\u003Cb\u003E全体で77%\u003C/b\u003E近く、当社の主要ペルソナ（AE、SE、SDR）全体では\u003Cb\u003E90%以上\u003C/b\u003Eに増加しました。\u003C/p\u003E\n\u003Cp\u003E私たちにとって、これは重要な教訓となりました。\u003Cb\u003EエンタープライズAIのスケーリングは、技術的な課題だけではありません。製品のアクティベーションや変更管理の問題も発生します。\u003C/b\u003E\u003C/p\u003E\n\u003Ch2\u003Eローンチ後のプロダクト思考\u003C/h2\u003E\n\u003Cp\u003E変化は双方にあります。\u003C/p\u003E\n\u003Cp\u003EエンタープライズAIエージェントによる市場投入ワークフローの変革は、ユーザーだけでなく、エージェントを構築するデータチームにとっても大きな転換です。\u003C/p\u003E\n\u003Cp\u003E従来、データチームは、データパイプラインの構築、モデルのトレーニング、ダッシュボードのローンチなど、比較的抑制されたプロジェクトに取り組むことが常であり、ローンチ後の進化は限定的であることが多くありました。展開後は、通常は増分メンテナンスのみが必要です。\u003C/p\u003E\n\u003Cp\u003EAIエージェントは根本的に異なります。\u003C/p\u003E\n\u003Cp\u003EエンタープライズAIアシスタントは\u003Ci\u003E生きたプロダクト\u003C/i\u003Eですユーザーとの継続的なインタラクション、新しいデータへの適応、変化するワークフローへの進化は、プラットフォームやモデルの急速なイノベーションに追随するものとなっています。そのためには、プロダクト思考、システムレベルの設計、長期的なチームコラボレーションへのシフトが必要です。\u003C/p\u003E\n\u003Ch3\u003EGA後のチームの適応\u003C/h3\u003E\n\u003Cp\u003EGAローンチの成功を受けて、チームとしての取り組みを進化させる必要がありました。\u003C/p\u003E\n\u003Cp\u003Eほぼ即時に、以下に直面しました。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003Eユーザーからのインバウンド機能リクエストの急増\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E即応性と信頼性に関する期待の高まり\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E反復の迅速化と信頼の維持を両立する必要性の高まり\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E基盤となるAIプラットフォームの継続的な変化により、初期の設計意思決定を見直す必要が生じた\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E同時に、品質の低下も起こりませんでした。バグへの迅速な対処、リグレッションの防止、改善策は、ユーザーが信頼するエクスペリエンスを不安定にさせることなく提供する必要があります。\u003C/p\u003E\n\u003Cp\u003E製品のスケーリングは、運用モデルのスケーリングを意味していました。\u003C/p\u003E\n\u003Ch3\u003Eローンチ後に投資したこと\u003C/h3\u003E\n\u003Cp\u003E過剰な負担を避け、勢いを持続させるために、私たちは以下の領域に早くから投資しました。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eアジャイルな働き方：\u003C/b\u003Eバグ、機能リクエスト、長期的な改善のバランスを取るために、取り込み、トリアージ、優先順位付けのフレームワークを明確にしたスプリントベースの開発に移行しました。\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E補完的なスキルセットによるチームの拡大：\u003C/b\u003Eデータサイエンスだけでなく、ロードマップのニーズとシステムの複雑さに合わせてアナリティクスエンジニアリングとバックエンドエンジニアリングのキャパシティを追加しました。\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E自動化されたテストとCI/CD：\u003C/b\u003EGAからまもなく、私たちは自動化されたテストフレームワークとCI/CDパイプラインに投資し、高い品質を維持しながらも展開スピードを改善しました。\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eプラットフォームの健全性と進化に専念する時間：\u003C/b\u003Eリファクタリング、アーキテクチャの進化、プラットフォームのアップグレードは、すべてのスプリントで明示的なキャパシティを確保して、長期的な技術的負債を防止しました。\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003E重要なポイント\u003C/h3\u003E\n\u003Cp\u003EエンタープライズAIエージェントのローンチは、ゴールではありません、出発点です。\u003C/p\u003E\n\u003Cp\u003E長期的な成功のためには、AIエージェントをプロジェクトではなくプロダクトとして扱う必要があります。オーナーシップ、反復、運用の厳格化、継続的な投資が必要です。この変化を受け入れたチームは、ユーザーの期待とAI機能の両方が進化するなかで、責任を持ってスケーリングを行い、変化に適応し、価値を提供し続けることができるという点で、はるかに優位な立場にあります。\u003C/p\u003E\n\u003Ch2\u003E効果とROI\u003C/h2\u003E\n\u003Cp\u003Eこの時点で、次のような疑問があるかもしれません。\u003Ci\u003E実際の結果はどうだったか。投資する価値はあったか。\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E細かく見ていきましょう。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_777003060":{"id":"image-efa0c74a0e","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ff81418b-1c73-4c26-9dbf-3fd18589ee19/pilot-5.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"pilot-5","height":"736","width":"1302",":type":"snowflake-site/components/image"},"blog_text_560111879":{"id":"blog-text-b84b0b71f5","text":"\u003Cp\u003Eローンチ当初は\u003Cb\u003E毎週約1万件の質問\u003C/b\u003Eに回答していましたが、2025年末までに、\u003Cb\u003E2,500人上のウィークリーアクティブユーザー\u003C/b\u003E以に対して、GTM AI 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AIアシスタントのコスト構造と投資利益率の両方を把握するための詳細な分析を実施しました。\u003C/p\u003E\n\u003Cp\u003E分析の結果、\u003Cb\u003Eアクティブユーザーあたりのコストは、ユーザーあたりの標準的なエンタープライズ生産性ツールに匹敵\u003C/b\u003Eすることがわかりました。生産性の大幅な向上と照らし合わせると、このアシスタントはコスト最適化のための特別な取り組みを行う前にもかかわらず、\u003Cb\u003E5倍を超える投資利益率を達成\u003C/b\u003Eしました。\u003C/p\u003E\n\u003Cp\u003E重要なこととして、この見積もりは\u003Cb\u003E直接生産性の削減分\u003C/b\u003Eのみを反映したものです。これはアナリストの負荷軽減、取引サイクルの短縮、セールスチームやマーケティングチーム全体の意思決定の質の向上などの二次的なメリットは除外されます。言い換えれば、ソリューションが成熟度や運用効率に達する前に、経済性に優位性があったということです。\u003C/p\u003E\n\u003Ch2\u003E最後に\u003C/h2\u003E\n\u003Cp\u003EAIは、エンタープライズチームと市場投入チームを根本的に変革しています。\u003C/p\u003E\n\u003Cp\u003E孤立したデモや小規模な実験の時代は終わりつつあります。エンタープライズAIは現在、\u003Cb\u003E実際の効果を測定可能な形で大規模に\u003C/b\u003Eもたらしています。しかし、そのためには、強力なモデルや巧妙なプロンプトだけでなく、適切なマインドセット、統制のとれた実行、変更管理への深い焦点も必要です。\u003C/p\u003E\n\u003Cp\u003Eここまでで概説してきたように、エンタープライズAIエージェントのスケーリングを成功させるには、以下が必要です。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E品質と信頼を譲れないものとして扱う\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E意図的なローンチと意図的な範囲の拡大\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003Eテクノロジーだけでなく、アクティベーションと導入にも投資する\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E長期的なプロダクトオーナーシップに向けて進化するデータチーム\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E今後数か月のうちにこの目標が達成されれば、企業は生産性を改善できるだけでなく、チームの働き方を根本的に変え、有意義な競争優位性を獲得できます。\u003C/p\u003E\n\u003Cp\u003Eシフトはすでに始まっています。今、行動を起こす企業は、次世代の市場進出の方法を定義します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_2105309366","image_1962251299","blog_text_1827519857","image_361604255","blog_text_1428120683","image_76636164","blog_text_1129566895","image_777003060","blog_text_560111879","image_1372633756","blog_text_1906359881"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid 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href=\"https://www.snowflake.com/ja/legal/privacy/privacy-policy/#12\"\u003E個人情報を共有しない\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/ja/legal/\"\u003E法務関連\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. 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