人工智慧顛覆：全球概覽

本報告對當前和未來人工智慧對全球主要行業和地區的顛覆性影響進行了前沿分析。

它揭示了人工智慧對多個行業的顛覆性影響，說明了其發展背後的創新，並整合了案例研究、政府數據和特定平台的人工智慧發展，從而提供了對全球人工智慧顛覆性的全面戰略觀點。

調查範圍

本報告從技術、營運、客戶和競爭等角度分析了人工智慧將如何顛覆各行各業和社會。它不僅追蹤人工智慧的應用趨勢，更著重於將顛覆性變革理解為一種系統性力量，並描繪其對全球價值創造和社會經濟的影響。報告利用全球基準、即時應用以及來自學術界、企業和政策機構的深入研究，定義了不斷演變的人工智慧格局。報告檢驗了多個維度，包括涉及人工智慧原生架構、生成式人工智慧、自動化系統、機器人和資料基礎設施的平台轉型。報告分析了透過智慧自動化和基於機器學習的最佳化來重塑內部工作流程、供應鏈、物流和決策的過程。此外，報告也探討了人工智慧在使用者體驗、個人化引擎、預測服務、語音介面和人工智慧代理等方面的應用。

目錄

第1章執行摘要

• 調查目標和目的
• 為什麼要進行調查？
• 調查範圍
• 市場概況
• 混亂觀點
• 未來趨勢與發展
• 產業分析
• 區域洞察
• 結論

第2章 市場概覽

• 人工智慧顛覆性創新概述
• 季度回顧（2025 年第四季）：重點介紹人工智慧驅動的關鍵顛覆性變革
• AI市場脈搏儀表板
• 供應鏈風險
• 運算和GPU短缺
• 半導體地緣政治與出口管制
• 零件短缺和價格上漲
• 能源和資料中心容量限制
• 雲端平台故障
• 資料完整性和跨境資料風險
• 物流、航運和港口的不穩定性
• 人力資源和服務供應
• 領先的人工智慧顛覆性Start-Ups
• 監管執法
• 美國
• 歐洲
• 中國
• 印度
• 雲端和資料中心的限制
• 2025年及以後的人工智慧
• 2030 年情境規劃矩陣

第3章：人工智慧是機會而非威脅

• 概述
• 創造新工作/替換現有工作
• 衛生保健
• 金融與銀行
• 製造和供應鏈
• 零售與電子商務
• 教育/教育科技
• 運輸/物流
• 媒體與娛樂
• 人機協同連續性
• 人工智慧提高生產力並減少人力投入
• 組成工會及其法律風險
• 法律風險

第4章 人工智慧驅動的顛覆類型

• 科技顛覆
• 業務中斷
• 面向客戶的干擾
• 競爭格局的變化
• 嚴重性映射（漸進式中斷與生存性中斷）

第5章：技術顛覆

• 概述
• 科技顛覆的關鍵趨勢
• 人工智慧主導的技術顛覆的組成部分
• 高階機器學習和深度學習
• 人工智慧世代
• 自動化和機器人技術
• 預測分析
• 自然語言處理
• 邊緣人工智慧和雲端人工智慧
• 人工智慧對產品開發和研發的顛覆性影響
• 智慧體人工智慧：它的適用場景和局限性
• 智慧體人工智慧的作用
• 智慧體人工智慧失效的地方

第6章：業務中斷

• 概述
• 人工智慧引發的商業顛覆的關鍵趨勢
• 人工智慧驅動的業務中斷的組成部分
• 超自動化與智慧工作流程編配
• 預測分析與規範分析
• 人工智慧驅動的人力勞動力
• 數位雙胞胎和即時監測
• 動態資源分配與最佳化
• 流程自動化
• 人工智慧在供應鏈和物流的應用
• 供應鏈管理的人工智慧挑戰
• 智慧的代價：模型的訓練與擴展
• 人工智慧在永續營運的應用

第7章：客戶服務困惑

• 概述
• 人工智慧主導的客戶互動領域顛覆性創新的關鍵趨勢
• 人工智慧規模經濟導致的產業集中度變化
• 人工智慧主導的客戶互動中顛覆性創新的組成部分
• 對話式人工智慧和虛擬助手
• 視覺搜尋和推薦系統
• 預測性客戶智慧
• 辨識情緒和感受
• 人工智慧驅動的個人化
• 利用行為人工智慧進行體驗設計
• 身臨其境型人工智慧在擴增實境/虛擬實境商務中的應用
• 消費者人工智慧的監管審查
• 歐洲
• 美國
• 亞太地區

第8章 競爭顛覆

• 概述
• 人工智慧主導的競爭顛覆的關鍵趨勢
• 人工智慧主導的競爭顛覆的組成部分
• 人工智慧原生經營模式
• 獨特數據和網路效應
• 透過自動化實現成本領先
• 平台遊戲和生態系統貨幣化
• 人工智慧工具降低了准入門檻
• Start-Ups與成熟公司
• 人工智慧作為併購和估值中的策略資產
• 市場變化和現有公司面臨的挑戰
• 開放原始碼和人工智慧平台的作用

第9章：人工智慧對關鍵產業的影響

• 概述
• 化學品和材料
• 醫療保健和生命科學
• 科技與軟體
• 製造業和工業
• 能源、公共產業和氣候技術
• 教育/教育科技
• 運輸/物流

第10章：人工智慧在關鍵地區引發的顛覆性變革

• 概述
• 北美洲
• 歐洲
• 亞太地區
• 世界其他地區

第11章：人工智慧驅動的顛覆性變革案例研究

• 案例概覽 - 人工智慧實施
• 顛覆性案例研究
• 衛生保健
• 製造和供應鏈
• 運輸/物流
• 零售與電子商務
• 媒體與娛樂

第12章 專家意見

• 來自主要受訪者和主題專家的引述
• 人工智慧將如何顛覆化學工業
• 人工智慧將如何顛覆科技產業
• 人工智慧將如何顛覆醫療保健產業
• 人工智慧將為製造業帶來顛覆性變革。
• 監管機構和審核的視角

第13章：人工智慧顛覆性變革的未來

• 人工智慧顛覆性變革的未來
• 預報與預報
• 預計產業顛覆熱點
• 人工智慧驅動的顛覆性變革熱點地區
• 人工智慧驅動的市場崩盤
• 創新
• 搜尋增強生成（RAG）和知識基礎
• 參數高效的微調
• 客製化人工智慧加速器和機架級硬體
• 邊緣人工智慧和設備端人工智慧
• 通用人工智慧（AGI）
• 神經型態人工智慧
• 人工智慧在氣候智慧與綠色轉型的應用
• 生物人工智慧和神經符號系統
• 宏觀經濟敏感度情景
• 情境一：生產力激增與通貨緊縮衝擊
• 情境二：勞動外流與需求低迷
• 情境三：資本集中與人工智慧驅動的不平​​等
• 情境四：金融波動與政策延遲

第14章附錄

This report provides an up-to-date analysis of current and future AI disruptions across major industries and global regions. It highlights AI disruptions in multiple industries; explains the innovations behind development; and integrates case studies, governmental data and platform-specific AI developments to deliver a holistic and strategic perspective on global AI disruptions.

Report Scope

This report analyzes how AI disrupts industries and societies across technological, operational, customer-facing and competitive dimensions. It extends beyond tracking AI adoption trends and focuses on understanding disruption as a systemic force, mapping its worldwide impact on value creation and socio-economy. The study draws on global benchmarks, real-time applications and deep research from academic, corporate and policy institutions to define the evolving AI landscape. The report examines several vectors, including platform shifts involving AI-native architectures, generative AI, automation systems, robotics and data infrastructure. It examines the reengineering of internal workflows, supply chains, logistics and decision-making through intelligent automation and ML-based optimization. It also examines AI in user experience, personalization engines, predictive services, voice interfaces and AI agents.

The report focuses on the most AI-affected sectors globally, with trend analysis in domains such as healthcare, finance and banking, manufacturing and supply chain, retail and e-commerce, education and edtech, transportation and logistics, media and entertainment, and other emerging sectors. The study also presents a regional landscape to identify AI leaders and late adopters. It maps the regional maturity, talent ecosystems and policy environment in North America, Asia-Pacific, Europe and the Rest of the World (RoW).

The report evaluates AI disruption through multiple interconnected dimensions that include:

• Shifts in market capitalization linked to AI integration along with Job creation and displacement across cognitive and manual sectors.
• Breakthroughs in foundational models driving sectoral disruption.
• Changes in M&A activity and ecosystem consolidation around data-rich companies.

Report Includes

• An overview of AI-driven disruptions across global industries and regions
• Information on technological and operational disruption, focusing on changes in core operations, workflows, and platforms
• Discussion of how AI is transforming job functions and skill demand across industries
• Analysis of competitive disruption, including platform shifts and lowering of market entry barriers
• Coverage of disruption in customer experience, personalization, and customer support
• Case studies and real-time use cases of companies that have undergone disruption due to AI adoption
• Insights and perspectives from industry experts, thought leaders, and primary respondents

Table of Contents

Chapter 1 Executive Summary

• Study Goals and Objectives
• Reasons for Doing This Study
• Scope of Report
• Market Summary
• Disruption Viewpoint
• Future Trends and Development
• Industry Analysis
• Regional Insights
• Conclusion

Chapter 2 Market Overview

• AI Disruption Overview
• Quarter-in-Review (Q4 2025): Key AI Disruption Highlights
• AI Market Pulse Dashboard
• Supply Chain Risks
• Compute and GPU scarcity
• Semiconductor Geopolitics and Export Controls
• Component Shortages and Price Inflation
• Energy and Data Center Capacity Constraints
• Cloud and Platform Outages
• Data Integrity and Cross-Border Data Risk
• Logistics, Shipping and Port Volatility
• Talent and Services Supply
• Key AI Disruptive Startups
• Regulatory Enforcement
• U.S.
• Europe
• China
• India
• Cloud and Data Center Constraints
• AI Beyond 2025
• 2030 Scenario Planning Matrix

Chapter 3 AI as an Opportunity, not a Threat

• Overview
• New Job Roles Created/Traditional Jobs Being Displaced
• Healthcare
• Traditional Jobs Being Displaced
• New Job Roles Created
• Finance and Banking
• Traditional Jobs Being Displaced
• New Job Roles Created
• Manufacturing and Supply Chain
• Traditional Jobs Being Displaced
• New Job Roles Created
• Retail and e-Commerce
• Traditional Jobs Being Displaced
• New Job Roles Created
• Education and EdTech
• Traditional Jobs Being Displaced
• New Job Roles Created
• Transportation and Logistics
• Traditional Jobs Being Displaced
• New Job Roles Created
• Media and Entertainment
• Traditional Jobs Being Displaced
• New Job Roles Created
• Human-in-the-Loop Persistence
• AI Productivity Dividend versus Headcount Reduction
• Unionization and Legal Risk
• Legal risk 2025

Chapter 4 Types of Disruptions Influenced by AI

• Overview
• Technological Disruption
• Operational Disruption
• Customer-Facing Disruption
• Competitive Landscape Shift
• Severity Mapping (Incremental versus existential disruption)
• Technological Disruption
• Operational Disruption
• Customer-Facing Disruption
• Competitive Landscape Shifts

Chapter 5 Technological Disruptions

• Overview
• Key Trends in Technological Disruption
• Components of AI-Driven Technological Disruption
• Advanced ML and Deep Learning
• Generative AI
• Automation and Robotics
• Predictive Analytics
• Natural Language Processing
• Edge and Cloud AI
• AI's Transformative Impact on Product Development and R&D
• Agentic AI: Where It Works versus Breaks
• Where Agentic AI Works
• Where Agentic AI Breaks

Chapter 6 Operational Disruptions

• Overview
• Key Trends in AI-Driven Operational Disruption
• Components of AI-Driven Operational Disruption
• Hyperautomation and Intelligent Workflow Orchestration
• Predictive and Prescriptive Analytics
• AI-Augmented Human Workforce
• Digital Twins and Real-Time Monitoring
• Dynamic Resource Allocation and Optimization
• Process Automation
• AI in Supply Chain and Logistics
• Challenges of AI in Supply Chain Management
• Cost of Intelligence: Model Training and Scaling
• AI in Sustainable Operations

Chapter 7 Customer-Facing Disruptions

• Overview
• Key Trends in AI-Driven Customer-Facing Disruptions
• Shifts in Industry Concentration Due to AI Scale Effects
• Components of AI-Driven Customer-Facing Disruption
• Conversational AI and Virtual Assistants
• Visual Search and Recommendation Systems
• Predictive Customer Intelligence
• Emotion and Sentiment Recognition
• AI-Driven Personalization
• Experience Design Powered by Behavioral AI
• Immersive AI in AR/VR Commerce
• Regulatory Scrutiny on Consumer AI
• Europe
• The U.S.
• Asia-Pacific

Chapter 8 Competitive Disruptions

• Overview
• Key Trends in AI-Driven Competitive Disruptions
• Components of AI-Driven Competitive Disruption
• AI-Native Business Models
• Proprietary Data and Network Effects
• Automation-Enabled Cost Leadership
• Platform Play and Ecosystem Monetization
• AI Tools Lowering Barriers to Entry
• Startups vs. Incumbents
• AI as a Strategic Asset in M&A and Valuation
• Market Shifts and Incumbent Challenges
• Role of Open-Source and AI Platforms

Chapter 9 AI Impact on Major Industries

• Overview
• Chemicals and Materials
• Healthcare and Life Sciences
• Technology and Software
• Manufacturing and Industrial
• Energy, Utilities and Climate Tech
• Education and Edtech
• Transportation and Logistics

Chapter 10 AI Disruption in Major Regions

• Overview
• North America
• Europe
• Asia-Pacific
• Rest of the World

Chapter 11 Case Studies of AI Disruptions

• Case Snapshots - AI Deployments
• Case Studies of Disruptions
• Healthcare
• Manufacturing and Supply Chain
• Transportation and Logistics
• Retail and e-Commerce
• Media and Entertainment

Chapter 12 Expert Opinions

• Quotes from Primary Respondents and Domain Experts
• How AI is Disrupting the Chemicals Industry
• How AI is Disrupting the Technology Industry
• How AI is Disrupting the Healthcare Industry
• How AI is Disrupting the Manufacturing Industry
• Regulator and Auditor Views

Chapter 13 Future of AI Disruption

• Future of AI Disruption
• Forecasts and Predictions (2025-2030)
• Expected Industry Disruption Hotspots 2026
• AI Disruption Hotspots in 2026
• AI-Induced Market Crashes
• Innovations
• Retrieval-Augmented Generation (RAG) and Knowledge-Grounding
• Parameter-Efficient Fine-Tuning
• Custom AI Accelerators and Rack-Scale Hardware
• Edge and On-device AI
• Artificial General Intelligence (AGI)
• Neuromorphic AI
• AI in Climate Intelligence and Green Transition
• Bio-AI and Neuro-Symbolic Systems
• Macroeconomic Sensitivity Scenarios
• Scenario 1: Productivity Surge and Disinflationary Shock
• Scenario 2: Labor Displacement and Demand Drag
• Scenario 3: Capital Concentration and AI-Led Inequality
• Scenario 4: Financial Volatility and Policy Lag

Chapter 14 Appendix

• Methodology
• References
• Abbreviations

List of Tables

• Table 1 : KPIs Quarter 4, 2025
• Table 2 : Scenario Planning Matrix, 2030
• Table 3 : Exposure to AI Automation, by Aggregated Occupation Group, 2025
• Table 4 : AI Disruption vs. AI Transformation vs. AI Optimization
• Table 5 : Real-Time Technological Use Cases, 2025
• Table 6 : Real-Time Operational Use Cases, 2025
• Table 7 : Real-Time Customer Facing Use Cases, 2025
• Table 8 : Real-Time Competitive Landscape Shift Use Cases, 2025
• Table 9 : Policy-Relevant Severity Matrix, Q4 2025
• Table 10 : SWOT Analysis: Startups vs. Incumbents
• Table 11 : Challenges that Incumbents Must Confront
• Table 12 : AI Deployments, Q4 2025
• Table 13 : Global Market for AI Component Infrastructure, by End Use Industry, Through 2030
• Table 14 : Abbreviations Used in This Report

List of Figures

• Figure 1 : Digital Disruption
• Figure 2 : Illustration of Agentic Orchestration
• Figure 3 : AI Use Cases in Operations Management