人工智慧應用：全球視角

該報告對人工智慧在各行業的應用進行了全面深入的分析，涵蓋了人工智慧的現狀、相關法規和標準，以及人工智慧應用的主要障礙。

該報告也聚焦於硬體、軟體和服務解決方案中人工智慧的應用，分析了各行業的公司估值，展示了關鍵產業人工智慧成功案例的案例研究，並探討了未來幾年人工智慧在關鍵產業應用的前景。

調查範圍

本報告旨在對人工智慧的當前和未來應用進行全面深入的分析。研究範圍檢驗推動人工智慧發展的廣泛技術進步，以及這些技術在各行各業和新興企業中的應用。

報告內容包括：

• 對重點產業和地區人工智慧應用趨勢進行即時分析
• 關於人工智慧採用情況概述、歷史里程碑、法規和標準以及美國關稅對人工智慧採用情況的影響的事實和數據
• 應用層面的案例研究，展示各行業和新興企業如何採用人工智慧
• 對人工智慧硬體、軟體和服務解決方案進行深入分析，包括每項解決方案的公司評級。
• 分析人工智慧在區域層面（北美、歐洲、亞太、中東和非洲、南美）的應用以及影響其應用的因素
• 基於業務流程改善和產品開發的案例研究分析，識別影響人工智慧應用的關鍵挑戰
• 考慮到技術進步和不斷變化的行業需求，未來幾年人工智慧在關鍵產業應用的可能性
• 針對各公司的關鍵策略措施、人工智慧領域的市場支出和投資前景進行分析

目錄

第1章執行摘要

• 調查目標和目的
• 調查範圍
• 市場概況
• 招募視角
• 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
• 英國
• 美國
• 加拿大
• 中國
• 日本
• 韓國
• 印度
• 巴西
• 人工智慧普及的主要障礙
• 資料隱私
• 整合挑戰
• 缺乏人工智慧應用的潛在策略
• 數據可用性和品質
• 不斷變化的監管環境
• 美國關稅法對人工智慧普及的影響

第3章：人工智慧在硬體解決方案的應用

• 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

4. MCP伺服器技術採用分析

• 重點總結
• 概述
• MCP 伺服器架構
• 實施與採用趨勢（2024 年 11 月起）
• MCP 伺服器提供者分析
• 技術創新
• 關鍵策略發展
• 投資情境
• 未來投資趨勢
• 目的
• 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
• 招募分析
• 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
• 按服務類型進行的招募分析
• 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
• 按行業分類的招募分析
• 衛生保健
• 銀行、金融服務和保險（BFSI）
• 物流和供應鏈
• 零售與電子商務
• 教育/教育科技
• 媒體與娛樂
• 溝通
• 車
• 製造業
• 其他（農業、航太與國防、建築、能源與公用事業）

第8章：各地區的人工智慧應用趨勢

• 重點總結
• 區域招募分析
• 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

• 引入人工智慧以改善業務流程
• 案例研究1：通用電氣實施 Predix 平台
• 案例研究2：通用汽車公司簡化車輛檢驗流程
• 案例研究3：卑詩省投資管理公司運用人工智慧最佳化業務流程
• 案例研究4：BP 利用人工智慧提高油氣營運效率
• 案例研究5：達美航空利用人工智慧提高營運效率
• 案例研究6：美國銀行採用人工智慧工具“Erica”
• 案例研究7：Zodiac Maritime 的人工智慧增強型碰撞預測系統
• 案例研究8：德國電信利用人工智慧提高營運效率
• 案例研究9：鹿特丹港的智慧貨櫃管理
• 案例研究10：福斯公司採用亞馬遜的人工智慧驅動工具
• 案例研究11：克羅格的智慧貨架和價格最佳化
• 人工智慧在產品/服務創新的應用
• 案例研究1：人工智慧驅動的電子健康記錄(EHR) 最佳化
• 案例研究2：沃達豐的 AI 驅動客戶服務
• 案例研究3：零售業的預測分析
• 案例研究4：萬事達卡利用人工智慧最佳化支付處理
• 案例研究5：利用西門子數位化工業軟體開發人工智慧解決方案
• 案例研究6：羅徹斯特大學醫學中心與 Butterfly Network 的合作
• 案例研究7：OSF HealthCare 的人工智慧虛擬助手
• 案例研究8：Valley Bank 的反洗錢 (AML) 工作
• 案例研究9：歐洲管理與商業學院的人工智慧工具
• 案例研究10：AT&T 利用人工智慧革新客戶服務
• 案例研究11：博爾頓學院的 AI 驅動影片製作平台
• 案例研究12：絲芙蘭在美妝零售領域的創新
• 運用人工智慧改善客戶體驗
• 案例研究1：Motel Rocks 的客戶服務自動化
• 案例研究2：百思買的 AI 購物助手
• 案例研究3：OPPO 的人工智慧客戶支持
• 案例研究4：Turing AI 與 DevRev 的技術支援工單自動化
• 案例研究5：使用 Unity AI 實現客戶支援自動化
• 案例研究6：Esusu 的人工智慧對金融科技的支持
• 案例研究7：Compass AI 驅動的查詢路由
• 案例研究8：英特爾的 AI 技術支援聊天機器人
• 案例研究9：Shopify 預測性個人化
• 案例研究10：星巴克人工智慧驅動的會員個人化
• 案例研究11：BloomsyBox 利用生成式人工智慧提升客戶參與
• 引入人工智慧進行風險和欺詐管理
• 案例研究1：環球銀行的支票詐欺防範
• 案例研究2：RAZE 銀行預測性詐欺預防
• 案例研究3：Network International 的即時支付詐欺預防
• 案例研究4：TowneBank 的 CECL 合規性
• 案例研究5：萬事達卡的第三方風險管理
• 案例研究6：Grupo Bimbo 的全球資料保護
• 案例研究7：桑坦德銀行利用預測分析來預防貸款違約
• 案例研究8：瑞士信貸利用人工智慧提升房屋抵押貸款承銷能力
• 案例研究9：法國巴黎銀行利用人工智慧革新風險評估
• 案例研究10：BBVA 在貸款風險管理中對人工智慧的應用
• 引入人工智慧最佳化銷售
• 案例研究1：基於人工智慧的預測性案源計分
• 案例研究2：大規模超個人化推廣
• 案例研究3：基於即時訊號的分析
• 案例研究4：人工智慧驅動的對話智慧
• 案例研究5：人工智慧驅動的旅程編配
• 案例研究6：全通路個人化
• 案例研究7：人工智慧驅動的銷售輔導
• 案例研究8：端到端收入智慧
• 人工智慧在品管和合規性方面的應用
• 案例研究1：BMW汽車製造中的人工智慧影像檢查
• 案例研究2：三星電子的 AI 半導體品管
• 案例研究3：默克公司在藥品品管中應用人工智慧
• 案例研究4：亞馬遜的 GDPR 合規自動化
• 案例研究5：西奈山醫療系統的 HIPAA 患者資料保護
• 案例研究6：Airbnb全球GDPR 資料管理
• 案例研究7：西門子 ISO 9001 品質合規性
• 案例研究8：財富 500 強公司的文件安全合規性
• 將人工智慧引入人力資源和人才管理
• 案例研究1：RingCentral 的人工智慧驅動型人才招募與多元化、公平與包容策略
• 案例研究2：萬事達卡全球人才體驗平台
• 案例研究3：海峽互動公司的 AI 資料保護官 (DPO)
• 案例研究4：馬尼帕爾健康企業 (Manipal Health Enterprises) 的 MiPAL 虛擬助手
• 案例研究5：T-Mobile 的包容性招募語言
• 案例研究6：聯合利華的 AI 驅動招募平台
• 案例研究7：IBM 的 AI 驅動入職聊天機器人
• 案例研究8：通用電氣的 AI 驅動績效管理

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/教育科技
• 零售與電子商務
• 製造業
• 車
• 溝通

第11章附錄

This report provides an in-depth analysis of artificial intelligence (AI) adoption across various industries. It includes current state of AI, regulations and standards, and key barriers to this technology adoption. The report focuses on AI adoption in hardware, software and service solutions, including company evaluations for each solution. It also presents application-specific case studies for successful implementation of AI across the major industry verticals. The report concludes with future perspectives of AI adoption in key sectors over the coming years.

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 including healthcare, banking, financial services, and insurance, logistics and supply chain, retail and ecommerce, education and edtech, media and entertainment, telecommunication, automotive, manufacturing and others (agriculture, aerospace and defense, construction, energy and utilities). 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.
• The analysis of the future of AI adoption in key industries is also covered in the report.

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 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
• Application-level case studies highlighting AI adoption by industries and emerging businesses
• An in-depth analysis of AI hardware, software, and service solutions, including company evaluations for each solution
• Analysis of AI adoption at the regional levels, featuring North America, Europe, Asia-Pacific, the Middle East and Africa, and South America 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
• An analysis of the 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
• U.K.
• U.S.
• Canada
• China
• Japan
• South Korea
• India
• Brazil
• Key Barriers for AI Adoption
• Data Privacy
• Integration Challenges
• Lack of Potential Strategy for AI Adoption
• Data Availability and Quality
• Evolving Regulatory Landscape
• 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 (BFSI)
• Logistics and Supply Chain
• Retail and E-Commerce
• Education and EdTech
• Media and Entertainment
• Telecommunication
• Automotive
• Manufacturing
• Others (Agriculture, Aerospace and Defense, Construction, and Energy and Utilities)

Chapter 8 AI Adoption Trends by Regions

• Key Takeaways
• Adoption Analysis by Region
• 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
• AI Implementation for Risk and Fraud Management
• Case Study 1: Global Bank - Check Fraud Prevention
• Case Study 2: RAZE Banking - Predictive Fraud Prevention
• Case Study 3: Network International - Real-Time Payment Fraud
• Case Study 4: TowneBank - CECL Compliance
• Case Study 5: Mastercard - Third-Party Risk
• Case Study 6: Grupo Bimbo - Global Data Protection
• Case Study 7: Santander - Predictive Analytics for Loan Default Prevention
• Case Study 8: Credit Suisse - Enhancing Mortgage Underwriting with AI
• Case Study 9: BNP Paribas - Revolutionizing Risk Assessment with AI
• Case Study 10: BBVA - AI in Loan Risk Management
• AI Implementation for Sales Optimization
• Case Study 1: Predictive Lead Scoring with AI
• Case Study 2: Hyper-Personalized Outreach at Scale
• Case Study 3: Real-Time Signal-based
• Case Study 4: AI-Powered Conversational Intelligence
• Case Study 5: Journey Orchestration with AI
• Case Study 6: Omnichannel Personalization
• Case Study 7: AI-Driven Sales Coaching
• Case Study 8: End-to-End Revenue Intelligence
• AI Implementation for Quality Control and Compliance
• Case Study 1: BMW - AI Visual Inspection in Automotive Manufacturing
• Case Study 2: Samsung Electronics - AI Semiconductor Quality Control
• Case Study 3 Merck - AI Pharmaceutical Quality Control
• Case Study 4: Amazon - GDPR Compliance Automation
• Case Study 5: Mount Sinai Health System - HIPAA Patient Data Protection
• Case Study 6: Airbnb - Global GDPR Data Management
• Case Study 7: Siemens - ISO 9001 Quality Compliance
• Case Study 8: Fortune Company - Document Security Compliance
• AI Implementation for Human Resources and Talent Management
• Case Study 1: RingCentral - AI-Powered Talent Acquisition and DEI Strategy
• Case Study 2: Mastercard - Global Talent Experience Platform
• Case Study 3: Straits Interactive - AI Data Protection Officer
• Case Study 4: Manipal Health Enterprises - MiPAL Virtual Assistant
• Case Study 5: T-Mobile - Inclusive Recruiting Language
• Case Study 6: Unilever - AI-Driven Recruitment Platform
• Case Study 7: IBM - AI-Powered Onboarding Chatbots
• Case Study 8: General Electric - AI Performance Management

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 and EdTech
• Retail and E-Commerce
• Manufacturing
• Automotive
• Telecommunication

Chapter 11 Appendix

• Methodology
• References
• Abbreviations

List of Tables

• Table 1 : Key Historical AI Milestones, 1942-2025
• Table 2 : Comprehensive Analysis of MCP Server Providers, 2025
• Table 3 : Strategic Developments by MCP Manufacturers, November 2024-January 2026
• 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 Automotive Sector
• Table 11 : AI Applications in Agriculture
• Table 12 : AI Applications in Aerospace
• Table 13 : Phases and Milestones: The AI Adoption Roadmap
• Table 14 : Agentic AI in BFSI
• Table 15 : Agentic AI in Retail and E-Commerce
• Table 16 : 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 : Organizations Adopting Responsible AI, by Region, 2024
• Figure 16 : Survey of U.S. Officials on AI Policy Impacts on AI Benefits
• Figure 17 : Share of Firms That Have Adopted AI, by Employee Size, U.S., 2024
• Figure 18 : Responsible AI Papers at Major AI Conferences, by European Countries, 2024
• Figure 19 : AI Perception Breakdown: Corporate Views in Selected Latin American Countries
• Figure 20 : Major Factors Impacting AI Adoption in the Middle East and Africa, 2025
• Figure 21 : Global Perceptions of AI's Impact on Current Employment, 2024
• Figure 22 : Rate of AI Adoption in Hospitals, Global, 2018-2025
• Figure 23 : Distribution of Classroom Time Spent on AI Topics, by Grade Level, 2024