AIソフトウェア

当レポートでは、世界のAIソフトウェアの市場を調査し、 市場影響因子および市場機会の分析、AIソフトウェア収益の推移・予測、フレームワーク・地域など各種区分別の詳細分析などをまとめています。

レポートのメリット：

• 将来の収益機会を可視化しながら、AIハードウェアおよびソフトウェア戦略を策定できます
• 幅広い市場力学を考慮した投資とイノベーションの計画と、「ホット」な収益機会の特定が行えます
• エッジAIとクラウドAI、オープンソースとクローズドソース、地域ごとの発展、消費モデルなど、AIソフトウェアの主な動向を把握できます

主な質問への回答：

• 生成AIの企業導入は、自然言語処理 (NLP) ソフトウェアの収益をどの程度刺激するか？
• エッジAIソフトウェアの収益機会は、異なるAIフレームワーク間でどのように異なるか？
• AIソフトウェアの収益のうち、オープンソースの占める割合はどの程度か？
• 今後最大の市場機会をもたらすAIソフトウェアツールはどれか？

調査ハイライト：

• 各ソフトウェアツール別のソフトウェア収益の詳細な内訳
• 地域別のソフトウェア収益の包括的な内訳
• クラウドAIに対するエッジAIの収益機会の予測
• AIソフトウェアの消費モデルの検討
• オープンソースとクローズドソースの収益予測

目次

• テーブル
• チャート

Actionable Benefits:

• Develop Artificial Intelligence (AI) hardware and software strategies with visibility over future revenue opportunities.
• Plan investment and innovation considering wider market dynamics and identify "hot" revenue opportunities.
• Become cognizant of key trends in AI software, including edge versus cloud AI, open versus closed source, regional developments, and consumption models.

Critical Questions Answered:

• To what extent will generative AI enterprise adoption stimulate Natural Language Processing (NLP) software revenue?
• How will edge AI software revenue opportunities vary across different AI frameworks?
• What share of AI software revenue will result from open-source?
• Which AI software tools offer the biggest market opportunity moving forward?

Research Highlights:

• A detailed breakdown of software revenue by each software tool.
• Comprehensive breakdown of software revenue by region.
• Forecast of edge against cloud AI revenue opportunities.
• Examination of AI software consumption models.
• Forecast of open and closed-source revenue opportunities.

Who Should Read This?

• AI software decision makers looking to refine their technical and commercial strategies.
• AI hardware leaders building product alignment and new capabilities in the software market.
• Market strategists aligning entry, competitor, and growth approaches to key software opportunities.
• Cloud provider innovation leaders looking to build a strong position in the AI software market across different frameworks and regions.
• Enterprise leaders and professional investors assessing the direction of the AI software market.

Table of Contents

Tables

• Table 1: Artificial Intelligence Software Revenue by Framework
• Table 2: Artificial Intelligence Software Revenue by Tool
• Table 3: Artificial Intelligence Software Revenue by Type
• Table 4: Artificial Intelligence Software Revenue by Region
• Table 5: Artificial Intelligence Software Revenue by Deployment Location
• Table 6: Artificial Intelligence MLOps Tool Revenue by Interface Type
• Table 7: Artificial Intelligence Software Revenue by Workload Type
• Table 8: Artificial Intelligence Software Revenue by Tool Type
• Table 9: Artificial Intelligence Software Revenue by Consumption Model
• Table 10: Artificial Intelligence Software Revenue by Source Code
• Table 11: Open-Source Software Revenue by Artificial Intelligence Framework
• Table 12: Closed-Source Software Revenue by Artificial Intelligence Framework
• Table 13: Edge AI Software Revenue by Artificial Intelligence Framework
• Table 14: Cloud AI Software Revenue by Artificial Intelligence Framework
• Table 15: Computer Vision Software Revenue by Region
• Table 16: Computer Vision Software Revenue by Tool
• Table 17: Computer vision Software Revenue by Deployment Location
• Table 18: Computer vision Software Revenue by Consumption Model
• Table 19: Computer vision Software Revenue by Source Code
• Table 20: Natural Language Processing Software Revenue by Region
• Table 21: Natural Language Processing Software Revenue by Tool
• Table 22: Natural Language Processing Software Revenue by Deployment Location
• Table 23: Natural Language Processing Software Revenue by Consumption Model
• Table 24: Natural Language Processing Software Revenue by Source Code
• Table 25: Graph-Based AI Models Software Revenue by Region
• Table 26: Graph-Based AI Models Software Revenue by Tool
• Table 27: Graph-Based AI Models Software Revenue by Deployment Location
• Table 28: Graph-Based AI Models Software Revenue by Consumption Model
• Table 29: Graph-Based AI Models Software Revenue by Source Code
• Table 30: Computer Vision Edge AI Software Revenue by Tool
• Table 31: Computer Vision Cloud AI Software Revenue by Tool
• Table 32: Natural Language Processing Edge AI Software Revenue by Tool
• Table 33: Natural Language Processing Cloud AI Software Revenue by Tool
• Table 34: Graph-Based AI Models Edge AI Software Revenue by Tool
• Table 35: Graph-Based AI Models Cloud AI Software Revenue by Tool
• Table 36: Edge AI Software Revenue by Workload Interface
• Table 37: Cloud AI Software Revenue by Workload Interface
• Table 38: Edge AI Software Revenue by Source Code
• Table 39: Cloud AI Software Revenue by Source Code
• Table 40: Computer Vision Inference Software Revenue
• Table 41: Computer Vision Training Software Revenue
• Table 42: Natural Language Processing Inference Software Revenue
• Table 43: Natural Language Processing Training Software Revenue by Tool
• Table 44: Graph-Based AI Model Inference Software Revenue by Tool
• Table 45: Graph-Based AI Model Training Software Revenue by Tool
• Table 46: Training Software Revenue
• Table 47: Inferencing Software Revenue

Charts

• Chart 1: Artificial Intelligence Software Revenue by Framework
• Chart 2: Artificial Intelligence MLOps Tool Revenue by Type
• Chart 3: Edge AI Software Revenue by Framework
• Chart 4: Artificial Intelligence Software Revenue by Skill Level
• Chart 5: Comparing Regional AI Software Revenue
• Chart 6: Natural Language Processing Software Revenue by Type
• Chart 7: AI Software Revenue by Deployment Location
• Chart 8: Regional AI Software Revenue by Framework
• Chart 9: Cloud and Edge AI Software Revenue by Source Code
• Chart 10: Cloud and Edge AI Software by Workload Interface