[{"data":1,"prerenderedAt":1027},["ShallowReactive",2],{"/ja-jp/the-source/ai/6-strategies-to-help-developers-accelerate-ai-adoption":3,"footer-ja-jp":34,"the-source-banner-ja-jp":344,"the-source-navigation-ja-jp":356,"the-source-newsletter-ja-jp":383,"footer-source-/ja-jp/the-source/ai/6-strategies-to-help-developers-accelerate-ai-adoption/":394,"article-site-categories-ja-jp":399,"6-strategies-to-help-developers-accelerate-ai-adoption-article-hero-category-ja-jp":401,"6-strategies-to-help-developers-accelerate-ai-adoption-the-source-source-cta-ja-jp":424,"6-strategies-to-help-developers-accelerate-ai-adoption-category-ja-jp":439,"6-strategies-to-help-developers-accelerate-ai-adoption-the-source-resources-ja-jp":451,"6-strategies-to-help-developers-accelerate-ai-adoption-article-hero-author-ja-jp":478},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":13,"content":17,"type":25,"category":5,"slug":26,"_id":27,"_type":28,"title":29,"_source":30,"_file":31,"_stem":32,"_extension":33},"/ja-jp/the-source/ai/6-strategies-to-help-developers-accelerate-ai-adoption","ai",false,"",{"layout":9,"template":10,"author":11,"featured":6,"sourceCTA":12},"the-source","TheSourceArticle","emilio-salvador","source-lp-how-to-think-about-developer-productivity-in-the-age-of-ai",{"title":14,"description":15,"ogImage":16},"デベロッパーがAIの導入を加速できる6つの戦略","今やAIは、ソフトウェア開発において広く使用されています。この記事では、リーダーが潜在的な問題を認識しつつ、イノベーションが促進される環境作りを行う方法についてご紹介します。","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464541/da4tvbmwsisqabz8i0mc.png",{"title":14,"date":18,"description":15,"timeToRead":19,"heroImage":16,"keyTakeaways":20,"articleBody":24},"2024-10-29","7分で読めます",[21,22,23],"AIをソフトウェア開発プロセスに統合することで、ワークフローを効率化でき、デベロッパーの生産性が向上されます。結果としてチームは面倒なタスクに時間を取られることなく、イノベーションに取り組むことができます。","このようなメリットがあるものの、知識やリソースの不足、ワークフローへの適応の難しさ、仕事が奪われるのではないかといった懸念により、AIツールをワークフローにうまく統合するのは容易ではない場合があります。","AIの導入を成功させるための戦略として、AI導入の目標と目的の明確化、ガードレールとワークフローの確立、人材と組織文化の変革への注力などが挙げられます。","コーディング工程に人工知能（AI）を統合することで、ソフトウェアデベロッパーが戦略的タスクにかけられる時間が増えるだけでなく、認知負荷の軽減やより優れた価値の提供も可能になります。\n\n組織はすでにAIに多額の投資を行っています。[GitLabの2024年グローバルDevSecOpsレポート](https://about.gitlab.com/ja-jp/developer-survey/)によると、回答者の78%が「現在AIをソフトウェア開発に使用している、または今後2年間で使用する予定である」と回答し、2023年の64%から増加する結果となりました。また、AIを導入済みの組織は、デベロッパーの生産性やコード品質の向上、より安全なコードの作成などのメリットをすでに実感しています。[AIを活用する](https://about.gitlab.com/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity/)ことで、開発チームは、定型コードを手動で作成するといった時間のかかる反復的なタスクではなく、創造力を活かした問題解決やイノベーションにより多くの時間をかけられるようになります。\n\nAIのメリットは明らかであるとはいえ、日々の業務プロセスにAIツールをうまく組み込むのは必ずしも容易ではありません。これは、知識やリソースの不足、既存のワークフローやツールとの適合の難しさ、自動化によって仕事を失うことへの懸念など、さまざまな要因に起因するものと考えられます。GitLabが実施した調査では、回答者の半数近く（49%）が今後5年以内に現在の職務をAIに取られるのではないかと懸念を示しました。\n\nAIの統合を成功させるためには、チームの現状を把握することが不可欠です。[GitLabの調査](https://about.gitlab.com/ja-jp/developer-survey/2024/ai/)によると、組織の過半数（56%）は、評価と調査のステージにいることが判明しました。これはつまり、ほとんどのチームがAIの導入について達成可能な目標を設定し始めているものの、実際にはソフトウェア開発ライフサイクルにおいてAIを使い始めていない状態にいるということです。\n\n早期に導入した場合でも、もしくはAIというアイデア自体を検討中の段階であっても、チームを成功に導く6つの戦略をご紹介します。\n\n## 1. AI導入の目標と目的を明確にする\n最初に行うべきことは、組織のAIガバナンスモデルの作成です。AI導入の目標と目的は何ですか？既存のプロセスやワークフローにどのように適合させますか？\n\nAI戦略と導入を統括するリーダーを決めることも非常に重要です。最高AI責任者（CAIO）を採用する企業も出てきていますが、経営幹部として急いで登用する必要はなく、暫定的なCAIOとして統括責任者（VP）がチーム全体でのAIの使用を調整してもよいでしょう。\n\n第一の目標は、自動化やパーソナライゼーション、データドリブンの意思決定など、AIが大きな価値をもたらす分野に焦点を当てながら、ビジネス成果を直接サポートして大きな影響を及ぼすAIユースケースを特定し、優先順位をつけることです。まずは組織が直面する可能性のあるプライバシー、セキュリティ、法的な要件を取り上げることが必要です。AI導入が継続的なコンプライアンスにどのように関わってくるかを検討しなければ、AI導入の成功はあり得ないことを忘れないようにしましょう。\n\n第一の目標は、自動化やパーソナライゼーション、データドリブンの意思決定など、AIが大きな価値をもたらす分野に焦点を当て、ビジネス成果を直接的に支え、大きな影響を及ぼすAIユースケースを特定し、優先付けすることです。まずは組織が直面する可能性のあるプライバシー、セキュリティ、法的な要件を取り上げ、AI導入が継続的なコンプライアンスにどのように関わってくるかを検討せずにAI導入の成功はあり得ないことを忘れないようにしましょう。\n## 2. AIのガードレールとワークフローを確立する\n開発環境にAIを組み込む前に、責任を持って効果的にAIを使用するためにガイドラインを定める必要があります。セキュリティアナライザーの使用を含む自動テストを設定して、本番環境へ反映される前にすべてのAI生成コードを確実にレビューするゲートメカニズムを構築します。また、シャドーITの最新のパターンであるシャドーAIに注意してください。シャドーAIとは、コードベースで作業する際に作業者が独自のAIアシスタントを導入することで、機密情報や知的財産の漏えいにつながる恐れがあります。\n\nまた、チームがタスクの種類に応じてどのように異なる種類の機械学習（ML）モデルを使い分けるかについても、今から考えておくことをおすすめします。ひとつのMLモデルですべてのタスクを行えるわけではありません。大規模言語モデル（LLM）は多くの場合、特定のタスク向けに調整されています。つまり、複数のユースケースで同じAIモデルを使用しているチームは、最適な結果を得られない可能性があります。AIツールを選ぶ際には、特定のユースケースに合わせてさまざまなモデルを提供するベンダーを探しましょう。そうすることで、後々、モデルを入れ替える手間がかかりません。\n\n## 3. データドリブンなAIの骨組みを構築する\nAIが組織にもたらす成果は、AIシステムからアクセス可能なデータの質によって左右されます。AIシステムにデータを供給することで、組織のニーズに合わせて結果を調整し、ソフトウェア開発ライフサイクル全体の効率性と生産性を向上できます。しかし、長期的な成功を収めるためには、組織全体でデータを使用してプロンプトを与え、生成AIの出力を強化するデータドリブンなAIの骨組みが必要です。\n\nそのために行うべきことは次のとおりです。\n- データの収集、保存、クリーニング、処理を行う堅牢な仕組みを確立。\n- 特にGDPRやCCPAのような規制に確実に準拠するために、データアクセス、データの使用、セキュリティ、プライバシーに関する明確なガバナンスを確立。\n- データのサイロ化を解消。部署を超えたコラボレーションを促進し、組織のさまざまな場所でデータを活用できるようにします。今こそ、デベロッパーとデータサイエンティストが協力してデータウェアハウスやデータレイクを活用し、トレーニングモデルへのアクセスやアプリケーションの利用を促進するのに最適なタイミングです。\n\n## 4. 人材と組織文化の変革に注力する\nAIの可能性を安全かつ確実に、責任を持って引き出すには、継続的にスキルアップを行うことが不可欠です。データサイエンティストやAIエンジニア、その他の専門家からなるチームを編成し、AIソリューションを設計、開発、実装しましょう。従業員のスキルアップを図り、AIシステムを効果的に使用、保守できるようにすることが極めて重要です。また、AIの導入は長期に渡るプロセスであり、ある程度の[文化的変革](https://about.gitlab.com/the-source/ai/5-ways-execs-can-support-their-devops-teams-with-ai/)を行う必要があります。AIとデータドリブンの意思決定を受け入れる文化を育成することがAI導入を成功に導く鍵となります。社員が抱く自動化や仕事を失うことへの懸念に対応しつつ、試行と変革を促しましょう。\n\n## 5. 反復を受け入れる\nAIの実装は継続的なプロセスです。AIソリューションがフィードバックや新規データ、技術の進歩に基づいて常に改良・改善される継続的な学習アプローチを採用しましょう。デベロッパー向けには、個々のワークフローにAIがどのように適合するかを評価できるように試行期間を設ける必要があります。また、組織が長期的な利益を得られるようになるまでに、一時的に生産性が低下する可能性があることにも注意する必要もあります。マネージャーは、実装と反復のサイクル全体を通じて透明性と説明責任を強調することで、このような事態を早期に予測する（前もって考慮する）必要があります。\n\n## 6. コード行数だけでなく、ほかのメトリックも使用して成功を測定する\nタスク完了数や書かれたコードの行数などのメトリックを使用すれば、AIがチームに最も大きな影響を及ぼしている領域を効果的に特定できます。しかし、実際に重要となるのは、チームがどれだけ迅速に顧客に価値を提供できるかという点や、完成した製品のコード品質など、ビジネスにとって重要なメトリックをAIがどのように推進しているかということです。\n\nチームが記述したコードの行数からは、そこまでの情報はわかりません。AIの導入が成功したかどうかを測定するには、[生産性に関する従来のメトリックだけでなく](https://about.gitlab.com/ja-jp/the-source/ai/4-steps-for-measuring-the-impact-of-ai/)、ソフトウェアデリバリーの迅速化、デベロッパーの満足度の向上、顧客満足度の向上など、定量的に測定可能なビジネス価値を示すKPIに着目する必要があります。\n\n## まとめ：AIの導入によってデベロッパーを支援する\n\nまだ完全にAIを導入していない場合、今が取り掛かる絶好のタイミングです。Gartner®社によると、2028年までに企業のソフトウェアエンジニアの75%がAIコードアシスタントを使用すると予想されています。2023年初頭は10%未満だったことを考えると、大きな増加です[1]。\n\nAIの導入ペースは急速に加速しているものの、AIブーム（AIのハイプ・サイクル）が始まってからまだそれほど経っていないため、AIコードアシスタントの導入を検討し始めたばかりのチームであれば、早期導入者が直面した成長痛をある程度回避できるかもしれません。\n\nこの記事でご紹介した戦略に加え、開発から運用まで一貫した[DevSecOpsプラットフォームに統合されたAIソリューション](https://about.gitlab.com/gitlab-duo/)を導入すれば、ワークフローの全段階でデベロッパーの役に立ち、成功に向けた大きな一歩を踏み出せます。\n\nAIが職場に変革をもたらす中、企業はソフトウェア開発ライフサイクル全体でどのようにAIの力を活用して、イノベーションを加速させ、顧客に具体的なビジネスインパクトをもたらすことができるのかを検討する必要があります。\n\n[1] _出典：Joachim Herschmann, Manjunath Bhat, Frank O'Connor, Arun Batchu, Bill Blosen『2024年のソフトウェアエンジニアリングにおける戦略的な技術動向トップ5』Gartner社、2024年5月。GARTNERは、Gartner, Inc.および／またはその関連会社が有する米国内および国際的な登録商標であり、ここでは許可を得て使用されています。無断転載を禁じます。_\n\n*監修：小松原 つかさ [@tkomatsubara](https://gitlab.com/tkomatsubara)\u003Cbr>\n（GitLab合同会社 ソリューションアーキテクト本部 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