人工智慧應用：全球視角

本報告對人工智慧硬體、軟體和服務解決方案進行調查分析，包括對每種解決方案的公司估值。

本報告還對人工智慧在各個終端用戶行業的應用趨勢進行了說明分析，並包含了每個行業的案例研究。

報告內容

• 對主要產業和全球區域的人工智慧應用趨勢進行全面、即時的分析
• 關於人工智慧採用概況、過往里程碑、相關法規和標準以及美國關稅對人工智慧採用的影響的資料和分析
• 人工智慧在各個終端用戶產業成功案例案例研究
• 對人工智慧硬體、軟體和服務解決方案進行詳細分析，並對每項解決方案進行公司評估。
• 分析北美、歐洲、亞太、南美以及中東和非洲等地區的AI採用趨勢，並確定影響採用的因素。
• 基於業務流程改善和產品開發的案例研究分析，識別人工智慧實施中的關鍵挑戰
• 分析未來幾年人工智慧在關鍵產業的應用潛力，同時考慮技術進步和產業需求的變化。
• 企業關鍵策略舉措、人工智慧相關市場支出與投資趨勢的分析

目錄

第1章 執行摘要

• 研究目標和目的
• 調查範圍
• 市場摘要
• 採用觀點
• 投資情境
• 未來趨勢與發展
• 產業分析
• 區域洞察
• 結論

第2章 市場概覽

• 人工智慧實施概述
• 人工智慧應用的發展歷程
• 重要歷史里程碑
• 人工智慧爆炸：2020年及以後
• 人工智慧的現狀
• 主要技術模型
• 人工智慧實施的法規和標準
• EU
• 德國
• 美國
• 中國
• 日本
• 韓國
• 印度
• 人工智慧普及的主要障礙
• 缺乏知識
• 資料隱私
• 整合挑戰
• 美國關稅法對人工智慧普及的影響

第3章 硬體解決方案中的人工智慧應用

• 重點總結
• 按硬體類型分類的採用情況分析
• 人工智慧處理器和加速器
• 記憶
• 人工智慧資料中心基礎設施
• 領先人工智慧硬體供應商的當前和未來創新

第4章 MCP伺服器技術採用分析

• 重點總結
• 概述
• MCP 伺服器架構
• 部署與採用趨勢（2024年11月後）
• MCP 伺服器提供者分析
• 技術創新
• 重大策略發展
• 投資情境
• 未來投資趨勢
• 應用
• 主要應用領域
• 真實案例研究
• 結論

第5章 軟體解決方案中的人工智慧應用

• 重點總結
• 部署分析
• 人工智慧在商業職能中的應用：趨勢與影響
• 人工智慧平台
• 主要人工智慧軟體供應商的現狀和未來計劃
• 人工智慧的實際應用
• 人工智慧整合的關鍵領域

第6章 人工智慧在服務解決方案的應用

• 重點總結
• 依服務類型進行的採用率分析
• 專業服務
• 託管服務
• 主要服務供應商的當前和未來計劃

第7章 人工智慧在業界的應用

• 重點總結
• 產業採用分析
• 衛生保健
• 銀行、金融服務和保險
• 物流和供應鏈
• 零售與電子商務
• 教育/教育科技
• 媒體與娛樂
• 通訊
• 其他（農業、汽車、製造業、能源與公共產業）
• 未來展望
• 人工智慧應用的關鍵產業發展趨勢

第8章 各地區的人工智慧應用趨勢

• 重點總結
• 部署分析
• 北美洲
• 歐洲
• 亞太地區
• 拉丁美洲
• 中東和非洲
• 負責任地採用人工智慧面臨的區域性挑戰

第9章 人工智慧應用案例研究

• 引進人工智慧以改善業務流程
• 案例1：General Electric採用Predix平台
• 案例2：General Motors簡化車輛檢驗流程
• 案例3：British Columbia Investment Management Corp.運用人工智慧最佳化營運
• 案例4：BP 石油天然氣部門：提高營運效率
• 案例5：Delta Airlines利用人工智慧提高營運效率
• 案例6：Bank of America採用人工智慧工具「Erica」
• 案例7：Zodiac Maritime公司的AI增強型碰撞預測系統
• 案例8：Deutsche Telekom利用人工智慧提高營運效率
• 案例9：Port of Rotterdam的智慧貨櫃管理
• 案例10：Fox Corp.採用Amazon的人工智慧工具
• 案例11：Kroger利用人工智慧最佳化貨架和價格
• 人工智慧在產品/服務創新的應用
• 案例1：人工智慧驅動的電子健康記錄（EHR）最佳化
• 案例2：Vodafone的AI賦能客戶服務
• 案例3：零售業的預測分析
• 案例4：Mastercard利用人工智慧最佳化支付處理
• 案例5：Siemens Digital Industries Software開發人工智慧解決方案
• 案例6：University of Rochester Medical Center與 Butterfly Network 合作開發人工智慧
• 案例7：OSF HealthCare使用人工智慧虛擬助手
• 案例8：Valley Bank 的洗錢防制人工智慧
• 案例9：歐洲管理和商學院的人工智慧工具
• 案例10：AT&T 利用人工智慧變革客戶服務
• 案例11：Bolton College的人工智慧影片製作平台
• 案例12：Sephora在美妝零售領域的AI 創新
• 運用人工智慧改善客戶體驗
• 案例1：Motel Rocks 客戶服務自動化
• 案例2：Best Buy的AI購物助手
• 案例3：OPPO 的AI 客戶支持
• 案例4：DevRev 基於 Turing AI 的支援工單自動化
• 案例5：Unity AI 客戶支援自動化
• 案例6：Esusu 的金融科技人工智慧支持
• 案例7：CompassAI 聯絡路由
• 案例8：Intel的AI 技術支援聊天機器人
• 案例9：Shopify 預測性個人化
• 案例10：Starbucks人工智慧驅動的會員個人化
• 案例11：BloomsyBox 生成式人工智慧客戶參與

第10章 人工智慧應用的未來

• 預報與預報
• 組織影響：採納、認知和投資訊號
• 人工智慧在主要產業應用的未來
• 衛生保健
• 銀行、金融服務和保險
• 物流和供應鏈
• 媒體與娛樂
• 教育機構
• 零售與電子商務

第11章 附錄

This report provides an in-depth analysis of AI hardware, software, and service solutions, including company evaluations for each solution. It covers a descriptive analysis of AI adoption across various end-use industries as well as case studies for each industry.

Report Scope

This report aims to provide a thorough and detailed analysis of the current and future state of AI applications. Its scope includes a multifaceted review, covering both the technological progress driving AI and the various ways these developments are being used across different industries and by emerging businesses.

The following parameters define the scope of the report:

• The report will explore AI hardware, software, and service solutions and provide a detailed overview of key developments and innovations. It will define each solution and highlight its significance in the evolving AI ecosystem.
• The report covers a descriptive analysis of AI adoption across various end-use industries. Case studies will be included at the application level within these sectors to provide deeper insight.
• The study highlights AI adoption trends across North America, Europe, Asia-Pacific, South America, and the Middle East and Africa (MEA).
• The report identifies major challenges affecting AI implementation based on case study analyses for business process improvement and product development.
• It will also outline key government guidelines, regulations, and standards such as the EU AI Act, which are driving the rapid adoption of AI globally.

Report Includes

• A comprehensive and real-time analysis of AI adoption trends across major industries and global regions.
• Facts and figures pertaining to adoption overview, historical milestones, regulations and standards, and the impact of U.S. tariff laws on AI adoption.
• Case studies for successful implementation of AI across various end-use industries.
• An in-depth analysis of AI hardware, software, and service solutions, including company evaluations for each solution.
• AI adoption trends at the regional levels, featuring North America, Europe, Asia-Pacific, South America, and the Middle East and Africa (MEA) and factors influencing the adoption
• Identification of major challenges affecting AI implementation based on case study analyses for business process improvement and product development.
• The potential for AI adoption in key industries over the coming years, considering technological progress and evolving industry demands.
• Analysis of companies' key strategic initiatives, market spendings on AI and an investment outlook

Table of Contents

Chapter 1 Executive Summary

• Study Goals and Objectives
• Scope of Report
• Market Summary
• Adoption Viewpoint
• Investment Scenario
• Future Trends and Developments
• Industry Analysis
• Regional Insights
• Conclusion

Chapter 2 Market Overview

• AI Adoption Overview
• Evolution of AI Adoption
• Key Historical Milestones
• AI Surge: Post 2020
• Current State of AI
• Key Technology Models
• Regulations and Standards for AI Adoption
• European Union
• Germany
• U.S.
• China
• Japan
• South Korea
• India
• Key Barriers for AI Adoption
• Lack of Knowledge
• Data Privacy
• Integration Challenges
• Impact of U.S. Tariff Laws on AI Adoption

Chapter 3 AI Adoption in Hardware Solutions

• Key Takeaways
• Adoption Analysis by Hardware Type
• AI Processors and Accelerators
• Memory
• AI Data Center Infrastructure
• Current and Future Innovations of Key AI Hardware Providers

Chapter 4 Analysis of MCP Server Technology Adoption

• Key Takeaways
• Overview
• MCP Server Architecture
• Deployment and Adoption Trends (Since November 2024)
• Analysis of MCP Server Providers
• Technological Innovation
• Key Strategic Developments
• Investment Scenario
• Future Investment Trends
• Applications
• Major Applicational Areas
• Real-World Case Studies
• Conclusion

Chapter 5 AI Adoption in Software Solutions

• Key Takeaways
• Adoption Analysis
• AI in Business Functions 2025: Trends and Impact
• AI Platforms
• Current and Future Plans of Key AI Software Providers
• Real-World Applications of Artificial Intelligence
• Key Areas of the AI Integration

Chapter 6 AI Adoption in Service Solutions

• Key Takeaways
• Adoption Analysis by Service Type
• Professional Services
• Managed Services
• Current and Future Plans for Key Service Providers

Chapter 7 AI Adoption by Industries

• Key Takeaways
• Adoption Analysis by Industry
• Healthcare
• Banking, Financial Services, and Insurance
• Logistics and Supply Chain
• Retail and eCommerce
• Education and EdTech
• Media and Entertainment
• Telecommunication
• Others (Agriculture, Automotive, Manufacturing, Energy and Utilities, and More)
• Future Outlook
• Key Developments in the Industrial Sector for AI Adoption

Chapter 8 AI Adoption Trends by Regions

• Key Takeaways
• Adoption Analysis
• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East and Africa
• Regional Challenges in Responsible AI Adoption

Chapter 9 Case Studies on AI Adoption

• AI Implementation to Improve Business Processes
• Case Study 1: General Electric's Deployment of Predix Platform
• Case Study 2: General Motors' Vehicle Inspection Process Efficiency
• Case Study 3: British Columbia Investment Management Corp. Implemented AI to Optimize Business Procedures
• Case Study 4: AI for Operational Efficiency in Oil and Gas at BP
• Case Study 5: Delta Airlines Improved Operational Efficiency Using AI
• Case Study 6: Bank of America's Adoption of AI Tool Erica
• Case Study 7: Zodiac Maritime's AI-Enhanced Collision Prediction System
• Case Study 8: Deutsche Telekom Improving Operational Efficacy with AI
• Case Study 9: Port of Rotterdam's Smart Container Management
• Case Study 10: Fox Corp. Implemented Amazon's AI-Driven Tools
• Case Study 11: Kroger's Intelligent Shelving and Pricing Optimization
• AI Implementation for Product/Service Innovation
• Case Study 1: AI-powered Electronic Health Records Optimization
• Case Study 2: Vodafone's AI-driven Customer Service
• Case Study 3: Predictive Analytics in Retail
• Case Study 4: Mastercard Optimized Payment Processing with AI
• Case Study 5: Siemens Digital Industries Software Developed an AI Solution
• Case Study 6: Collaboration Between the University of Rochester Medical Center and Butterfly Network
• Case Study 7: OSF HealthCare's AI-powered Virtual Assistant
• Case Study 8: Valley Bank's Anti-money Laundering
• Case Study 9: AI-powered Tool for European School of Management and Business
• Case Study 10: AT&T Transformed Customer Service with AI
• Case Study 11: Bolton College's AI-powered Video Creation Platform
• Case Study 12: Sephora's Innovation in Beauty Retail
• AI Implementation for Customer Experience Enhancement
• Case Study 1: Motel Rocks Customer Service Automation
• Case Study 2: Best Buy's AI Shopping Assistant
• Case Study 3: OPPO's AI-powered Customer Support
• Case Study 4: DevRev Turing AI-Support Ticket Automation
• Case Study 5: Unity - AI Customer Support Automation
• Case Study 6: Esusu - Fintech AI Support
• Case Study 7: Compass - AI Query Routing
• Case Study 8: Intel - AI Technical Support Chatbots
• Case Study 9: Shopify - Predictive Personalization
• Case Study 10: Starbucks - AI-driven Loyalty Personalization
• Case Study 11: BloomsyBox - Generative AI for Customer Engagement

Chapter 10 Future of AI Adoption

• Forecasts and Predictions
• Impact on Organizations: Adoption, Perception, and Investment Signals
• Future of AI Adoption in Key Industries
• Healthcare
• Banking, Financial Services, and Insurance
• Logistics and Supply Chain
• Media and Entertainment
• Education Institutions
• Retail and E-Commerce

Chapter 11 Appendix

• Methodology
• References
• Abbreviations

List of Tables

• Table 1 : Key Historical AI Milestones, 1942-2024
• Table 2 : Comprehensive Analysis of MCP Server Providers, 2025
• Table 3 : Strategic Developments by MCP Manufacturers, November 2024-October 2025
• Table 4 : Key Strategic Investments in MCP Servers, April 2024-October 2025
• Table 5 : Types of AI Technology, Primary Function, and Applications
• Table 6 : Comparative Performance of RL-based Recommendation Engines, Global, 2025
• Table 7 : AI Services Provided by IBM
• Table 8 : Value of AI Implementation Across the BFSI Sector
• Table 9 : AI Applications in Media and Entertainment
• Table 10 : AI Applications in Agriculture
• Table 11 : Phases and Milestones: The AI Adoption Roadmap
• Table 12 : Agentic AI in BFSI
• Table 13 : Agentic AI in Retail and eCommerce
• Table 14 : Abbreviations Used in This Report

List of Figures

• Figure 1 : Corporate Investments in AI, Global, 2019-2024
• Figure 2 : Usage of Predictive Models Across Primary Inpatient EHR Vendors, 2024
• Figure 3 : Number of Notable Units of AI Models, by Country, 2024
• Figure 4 : Total Number of AI Laws Around the World, by Country, 2025
• Figure 5 : Barriers to AI Adoption in Organizations, 2024
• Figure 6 : Imports of AI-Directed Technology, U.S., November 2024-March 2025
• Figure 7 : MCP Server Architecture
• Figure 8 : Number of MCP Servers Across the World, by Quarter, November 2024-June 2025
• Figure 9 : Integration State of AI Solutions, by Business Function, 2025
• Figure 10 : U.S. Survey of GenAI Adoption at Work and at Home, as of August 2024
• Figure 11 : Growth in U.S. Job Postings Requiring GenAI Skills, 2023 and 2024
• Figure 12 : Percentage of AI Adoption Across Various Business Functions, 2025
• Figure 13 : Strategic Importance of AI for Managed Service Providers' Growth, 2024
• Figure 14 : Organizations Using AI and GenAI in at Least One Business Function, 2020-2024
• Figure 15 : Number of AI Medical Devices Approved by the FDA, 2018-2023
• Figure 16 : Organizations Adopting Responsible AI, by Region, 2024
• Figure 17 : Survey of U.S. Officials on AI Policy Impacts on AI Benefits
• Figure 18 : Share of Firms That Have Adopted AI, by Employee Size, U.S., 2024
• Figure 19 : Responsible AI Papers by Region at Major AI Conferences, 2024
• Figure 20 : AI Perception Breakdown: Corporate Views in Selected Latin American Countries
• Figure 21 : Major Factors Impacting AI Adoption, 2025
• Figure 22 : Global Perceptions of AI's Impact on Current Employment, 2024
• Figure 23 : Rate of AI Adoption in Hospitals, Global, 2018-2025
• Figure 24 : Distribution of Classroom Time Spent on AI Topics, by Grade Level, 2024