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市場調查報告書
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1850400

企業人工智慧：市場佔有率分析、產業趨勢、統計數據和成長預測（2025-2030 年）

Enterprise AI - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3個工作天內

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簡介目錄

根據估計和預測，企業人工智慧市場在 2025 年的價值為 972 億美元，預計到 2030 年將達到 2,293 億美元，複合年成長率為 18.9%。

企業人工智慧市場-IMG1

市場擴張的驅動力在於生成式人工智慧（一種能夠自動執行多步驟任務的代理系統）的快速普及，以及對用於加速推理的專用晶片日益成長的需求。光是微軟的人工智慧產品組合預計在2025會計年度就將以年化130億美元的速度成長，年增175%。硬體供應商也反映了這一成長動能。儘管有出口限制，英偉達累計2026會計年度第一季的營收將達到441億美元，證實了市場對高階GPU的強勁需求。雖然雲端運算仍然是主要的採用途徑，但隨著企業在資料主權和即時應用場景之間尋求平衡，混合雲和邊緣運算的採用正在加速。投資模式顯示競爭格局日趨成熟。儘管2024年創業投資資金籌措超過1,000億美元，但交易量集中在少數幾家後進企業手中，這預示著未來將出現整合。

全球企業人工智慧市場趨勢與洞察

對自動化和人工智慧解決方案的需求日益成長

企業自動化正從基於規則的RPA（機器人流程自動化）轉向涵蓋供應鏈、財務和客戶營運的認知代理。在物流領域應用以代理為基礎的人工智慧的企業，其收入成長比同業高出61%；像聯合利華這樣的製造商，透過人工智慧主導的最佳化，整體資產效率提高了85%。曾經需要數天的決策週期現在縮短到幾分鐘，從而能夠更快地響應市場並控制成本。生成式人工智慧與工作流程引擎結合，正在創建自適應流程自動化，無需人工編寫腳本即可自我改進。

分析指數級成長的企業資料集的必要性

資料成長速度已超過傳統分析工具的處理能力，推動了對大規模語言模型介面的需求，這些介面允許業務使用者使用自然語言查詢Petabyte級資料。金融公司正在部署GPT規模的模型，將交易資料、聊天記錄和市場資訊整合起來，用於客戶風險評分；醫療服務提供者正在合成影像和電子病歷記錄，以輔助診斷。現代人工智慧技術堆疊中內建的自動化資料發現功能，將資料準備工作從數月縮短至數天，從而更快釋放資料價值。

文化和技能差距會減緩企業採用

人工智慧人才短缺問題僅次於網路安全和雲端運算技能，71% 的公司表示專業知識缺口是其面臨的最大瓶頸。僅有 21% 的公司針對人工智慧重新設計了工作流程，尤其是在傳統領域。 LLMOps 工程師等新角色加劇了這項挑戰，迫使公司加強培訓舉措或尋求託管服務合作夥伴。

細分市場分析

到2024年，軟體和平台將佔企業人工智慧市場48%的佔有率，凸顯了企業對預整合功能的需求。同時，硬體加速器將以23.11%的複合年成長率實現最快成長，顯示基礎設施投資正轉向以性能為導向。由於出於隱私考慮，企業傾向於在本地運作大規模基礎模型，預計企業人工智慧硬體市場規模將快速擴張。英偉達2025會計年度第二季的資料中心營收達到263億美元，年增154%。

客製化ASIC的激增標誌著從通用CPU到矩陣最佳化處理器的結構性轉變。雲端供應商正在將這些加速器整合到其託管堆疊中，從而實現快速橫向擴展，而無需企業承擔折舊免稅額成本。在邊緣端，高效節能的SoC支援工業視覺和物聯網閘道器中的本地推理，將企業AI市場擴展到核心資料中心之外。

儘管大型企業仍佔據絕對支出主導地位，但中小企業如今正透過模板化模型和SaaS收費獲取先進的人工智慧技術。垂直產業專屬的基礎設施模式降低了專業知識的門檻，使得咖啡連鎖店和精品保險公司能夠以極少的編碼工作量部署人工智慧聊天機器人和需求預測功能。因此，企業級人工智慧市場正迎來越來越多的來自員工人數不到1000人的公司的貢獻，同時，大量創業投資也湧入專注於中小企業的人工智慧平台。

雲端市場正在打包拖放式資料管道，而託管服務公司則將資料準備、微調和監控打包在一起。隨著人工智慧代理實現後勤部門營運自動化，中小企業也能享受到以往只有全球性企業才能獲得的生產力提升，進而將企業級人工智慧的應用範圍擴展到長尾產業。

企業與企業（軟體/平台、服務、硬體加速器）、部署模式（本地部署、雲端部署、混合/邊緣部署）、組織規模（大型企業（1000 多名員工）、中型企業（100-999 名員工）、中小企業（100 名員工以下））、功能領域、技術、最終用戶產業（銀行業、金融服務和地區保險、製造業等地區和地區保險、製造業等地區和地區保險）。

市場預測以美元計價。

區域分析

到2024年，北美將佔據企業人工智慧市場41.50%的收入佔有率，這得益於超過750億美元的超大規模資本投資和深厚的創投生態系統。儘管美國政策導致對雲端運算和人工智慧夥伴關係的審查日益嚴格，以防範反競爭鎖定，但其創新引擎仍然強勁。加拿大正在推行一項平衡的管治藍圖，既維護倫理道德，又保持研究的彈性；而墨西哥則利用近岸外包將人工智慧投資引導至製造業走廊。

歐洲正圍繞歐盟人工智慧法律建立信任平台，並專注於可解釋性來製定解決方案。德國強大的工業基礎推動了對人工智慧驅動的自動化需求，而英國的創新友好立場使其成為人工智慧測試的沙盒。法國和義大利的舉措將倫理框架與獎勵相結合，以扶持新興企業，但合規成本可能會延長產品上市週期。

亞太地區正經歷著人工智慧積極應用的最快成長。中國生成式人工智慧的採用率已達83%，但美國公司在生產級部署方面處於領先地位，凸顯了成熟度差距。日本和韓國正在擴大國內半導體製造廠的規模以確保硬體供應，而印度190億美元的人工智慧資金籌措浪潮正在推動新興企業的發展。同時，隨著主權財富基金支持沙烏地阿拉伯和阿拉伯聯合大公國的人工智慧中心，中東和非洲市場預計將以21.70%的複合年成長率成長。拉丁美洲正在製定以人權為中心的法規，為負責任的人工智慧平台供應商創造了閒置頻段。

其他福利：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章 引言

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章 市場情勢

  • 市場概覽
  • 市場促進因素
    • 對自動化和人工智慧解決方案的需求激增
    • 分析指數級成長的企業資料集的必要性
    • 雲端基礎的AI即服務平台的興起
    • 專用運算硬體（GPU、TPU、NPU）的進步
    • 中小企業的產業特定平台模式，使人工智慧惠及更多企業
    • 淨零排放承諾推動人工智慧驅動的碳最佳化工具發展
  • 市場限制
    • 文化和技能差距會減緩企業採用
    • 資料主權和隱私監管障礙
  • 價值/供應鏈分析
  • 監管環境
  • 技術展望
    • 模型操作與速動操作的演變
    • 邊緣推理加速
  • 波特五力分析
    • 買方的議價能力
    • 供應商的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭對手之間的競爭
  • 評估宏觀經濟趨勢對市場的影響

第5章 市場規模與成長預測

  • 按組件
    • 軟體/平台
    • 服務
    • 硬體加速器
  • 按公司規模
    • 大型公司（員工超過1000人）
    • 中型市場（100-999人）
    • 小型企業（員工人數少於100人）
  • 按功能領域
    • 客戶關係（客戶體驗、行銷、銷售）
    • 營運和供應鏈
    • 金融與風險
    • 人事和人力資源
  • 透過技術
    • 機器學習/基礎模型
    • 自然語言處理
    • 電腦視覺
    • 決策智慧/最佳化
  • 按最終用戶行業分類
    • BFSI
    • 製造業
    • 汽車與出行
    • 資訊科技和電信
    • 媒體與廣告
    • 醫療保健和生命科學
    • 零售與電子商務
    • 能源與公共產業
    • 其他
  • 按部署模式
    • 本地部署
    • 雲
    • 混合/邊緣
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美洲
    • 歐洲
      • 英國
      • 德國
      • 法國
      • 義大利
      • 其他歐洲地區
    • 亞太地區
      • 中國
      • 日本
      • 印度
      • 韓國
      • 亞太其他地區
    • 中東和非洲

第6章 競爭情勢

  • 市場集中度和市場佔有率
  • 策略發展
  • 公司簡介
    • Microsoft Corporation
    • IBM Corporation
    • Amazon Web Services Inc.
    • Google LLC
    • Oracle Corporation
    • Hewlett Packard Enterprise
    • NVIDIA Corporation
    • SAP SE
    • Intel Corporation
    • Wipro Limited
    • NEC Corporation
    • Accenture plc
    • ServiceNow Inc.
    • DataRobot Inc.
    • UiPath Inc.
    • C3.ai Inc.
    • Palantir Technologies
    • H2O.ai Inc.
    • Sentient Technologies
    • AiCure LLC

第7章 市場機會與未來展望

簡介目錄
Product Code: 64271

The enterprise AI market size is estimated at USD 97.2 billion in 2025 and is forecast to reach USD 229.3 billion by 2030, registering an 18.9% CAGR.

Enterprise AI - Market - IMG1

Expansion is propelled by rapid adoption of generative AI, agentic systems that automate multi-step tasks, and rising demand for specialised silicon that cuts inference times. Enterprises increasingly view AI as a route to cost optimisation, with Microsoft's AI portfolio alone running at a USD 13 billion annualised rate in fiscal 2025, a 175% year-on-year jump. Hardware suppliers mirror this momentum; NVIDIA posted USD 44.1 billion in Q1 FY2026 revenue despite export controls, underlining resilient demand for high-end GPUs. Cloud remains the primary deployment path, yet hybrid and edge rollouts are climbing fast as firms juggle data-sovereignty mandates with real-time use cases. Investment patterns hint at a maturing competitive environment: venture capital funding topped USD 100 billion in 2024, but deals are concentrating around fewer late-stage players, signalling future consolidation.

Global Enterprise AI Market Trends and Insights

Surging Demand for Automation & AI-Based Solutions

Corporate automation has moved beyond rule-based RPA toward cognitive agents spanning supply chain, finance, and customer operations. Organisations that embed agentic AI in logistics report 61% higher revenue growth than peers, while manufacturers such as Unilever lifted overall equipment effectiveness by 85% through AI-driven optimisation. Decision cycles that once took days now shrink to minutes, delivering not just cost control but faster market response. Coupling generative AI with workflow engines is spawning adaptive process automation that refines itself without human scripting.

Need to Analyse Exponentially Growing Enterprise Data Sets

Data growth outpaces traditional analytics tooling, creating pent-up demand for large-language-model interfaces that let business users query multi-petabyte stores in natural language. Financial firms deploy GPT-scale models to combine transactions, chat transcripts, and market feeds for in-flight risk scoring, while healthcare providers synthesise imaging and EHR records to support diagnostics. Automated data-discovery features in modern AI stacks now cut data-prep effort from months to days, unlocking value faster.

Cultural & Skills Gap Slowing Enterprise Adoption

Shortfalls in AI talent rank ahead of cybersecurity and cloud skills, with 71% of firms citing expertise gaps as the chief bottleneck. Compensation premiums of up to 20% for generative-AI roles widen disparities, especially in legacy sectors where only 21% of companies have re-engineered workflows for AI. Emerging roles such as LLMOps engineers compound the challenge, forcing enterprises to ramp training initiatives or pursue managed-service partners.

Other drivers and restraints analyzed in the detailed report include:

  1. Rise of Cloud-Based AI-as-a-Service Platforms
  2. Restraint % Impact on CAGR Forecast Geographic Relevance Impact Timeline Cultural & skills gap slowing enterprise adoption -2.8% Global, acute in traditional industries Medium term (2-4 years) Data-sovereignty and privacy-regulation hurdles -2.1% EU & North America, expanding to APAC Long term (>= 4 years)
  3. Data-Sovereignty and Privacy-Regulation Hurdles

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software and platforms accounted for 48% of the enterprise AI market in 2024, underscoring enterprise demand for pre-integrated capabilities. Hardware accelerators, however, are growing the fastest at 23.11% CAGR, indicating a pivot toward performance-centric infrastructure investments. The enterprise AI market size for hardware is projected to climb sharply as organisations run larger foundation models on-premises for privacy. NVIDIA's data-center revenue hit USD 26.3 billion in Q2 FY2025, a 154% rise that highlights sustained capital allocation into GPUs.

Uptake of custom ASICs illustrates a structural shift from general CPUs to matrix-optimised processors. Cloud vendors integrate these accelerators into hosted stacks, giving enterprises rapid scale-out without bearing depreciation. At the edge, power-efficient SoCs enable local inference for industrial vision and IoT gateways, broadening the enterprise AI market beyond core data centers.

Large enterprises continue to dominate absolute spending, yet SMEs now access advanced AI through templated models and SaaS billing. Industry-specific foundation models slash the expertise threshold, enabling a cafe chain or boutique insurer to launch AI chatbots and demand forecasting with minimal coding. Consequently, the enterprise AI market records rising contribution from companies under 1,000 staff, aligning with venture-capital flows into SME-focused AI platforms.

Cloud marketplaces package drag-and-drop pipelines while managed-service firms bundle data prep, fine-tuning, and monitoring. As AI agents automate back-office tasks, smaller firms capture productivity benefits previously reserved for global corporations, extending the enterprise AI industry's reach into long-tail sectors.

Enterprise AI Market is Segmented by Component (Software / Platform, Services and Hardware Accelerators), Deployment Model (On-Premise, Cloud and Hybrid / Edge), Organization Size (Large Enterprise (>=1 000 Employees), Mid-Market (100-999) and Small Enterprise (<100)), Functional Area, Technology, End-User Industry (BFSI, Manufacturing and More), Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America controlled 41.50% of 2024 enterprise AI market revenue, buoyed by hyperscaler capex exceeding USD 75 billion and a deep venture ecosystem. US policy now scrutinises cloud-AI partnerships for anticompetitive lock-ins, yet the innovation engine remains robust. Canada pursues a balanced governance blueprint that preserves research flexibility while safeguarding ethics, and Mexico leverages near-shoring to channel AI investment into manufacturing corridors.

Europe adopts a platform of trust anchored by the EU AI Act, shaping solutions that foreground explainability. Germany's strong industrial base fuels demand for AI-powered automation, while the UK positions itself as an AI testing sandbox under a pro-innovation stance. French and Italian initiatives combine ethical frameworks with incentives for startup creation, though compliance overhead can lengthen go-to-market cycles.

Asia-Pacific records the fastest uplift in active deployments. China shows 83% generative-AI adoption, yet US firms lead in production-grade rollouts, underscoring maturity gaps. Japan and South Korea scale domestic semiconductor fabs to secure hardware supply, and India's USD 19 billion AI funding wave accelerates startup momentum. Meanwhile, the Middle East and Africa enterprise AI market is forecast at 21.70% CAGR as sovereign wealth funds bankroll national AI hubs in Saudi Arabia and the UAE. Latin America crafts human-rights-oriented regulations, creating white spaces for responsible-AI platform vendors.

  1. Microsoft Corporation
  2. IBM Corporation
  3. Amazon Web Services Inc.
  4. Google LLC
  5. Oracle Corporation
  6. Hewlett Packard Enterprise
  7. NVIDIA Corporation
  8. SAP SE
  9. Intel Corporation
  10. Wipro Limited
  11. NEC Corporation
  12. Accenture plc
  13. ServiceNow Inc.
  14. DataRobot Inc.
  15. UiPath Inc.
  16. C3.ai Inc.
  17. Palantir Technologies
  18. H2O.ai Inc.
  19. Sentient Technologies
  20. AiCure LLC

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Surging demand for automation and AI-based solutions
    • 4.2.2 Need to analyse exponentially growing enterprise data sets
    • 4.2.3 Rise of cloud-based AI-as-a-Service platforms
    • 4.2.4 Advances in specialised computing hardware (GPU, TPU, NPU)
    • 4.2.5 Industry-specific foundation models democratising AI for SMEs
    • 4.2.6 Net-Zero pledges driving AI-enabled carbon-optimisation tools
  • 4.3 Market Restraints
    • 4.3.1 Cultural and skills gap slowing enterprise adoption
    • 4.3.2 Data-sovereignty and privacy-regulation hurdles
  • 4.4 Value / Supply-Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
    • 4.6.1 Model-Ops and Prompt-Ops evolution
    • 4.6.2 Edge inference acceleration
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Bargaining Power of Buyers
    • 4.7.2 Bargaining Power of Suppliers
    • 4.7.3 Threat of New Entrants
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Competitive Rivalry
  • 4.8 Assessment of the Impact of Macroeconomic Trends on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Component
    • 5.1.1 Software / Platform
    • 5.1.2 Services
    • 5.1.3 Hardware Accelerators
  • 5.2 By Organisation Size
    • 5.2.1 Large Enterprise (>=1 000 Employees)
    • 5.2.2 Mid-market (100-999)
    • 5.2.3 Small Enterprise (<100)
  • 5.3 By Functional Area
    • 5.3.1 Customer-facing (CX, marketing, sales)
    • 5.3.2 Operations and Supply-chain
    • 5.3.3 Finance and Risk
    • 5.3.4 HR and Talent
  • 5.4 By Technology
    • 5.4.1 Machine Learning / Foundation Models
    • 5.4.2 Natural-Language Processing
    • 5.4.3 Computer Vision
    • 5.4.4 Decision Intelligence / Optimisation
  • 5.5 By End-user Industry
    • 5.5.1 BFSI
    • 5.5.2 Manufacturing
    • 5.5.3 Automotive and Mobility
    • 5.5.4 IT and Telecom
    • 5.5.5 Media and Advertising
    • 5.5.6 Healthcare and Life-sciences
    • 5.5.7 Retail and e-Commerce
    • 5.5.8 Energy and Utilities
    • 5.5.9 Others
  • 5.6 By Deployment Model
    • 5.6.1 On-premise
    • 5.6.2 Cloud
    • 5.6.3 Hybrid / Edge
  • 5.7 By Geography
    • 5.7.1 North America
      • 5.7.1.1 United States
      • 5.7.1.2 Canada
      • 5.7.1.3 Mexico
    • 5.7.2 South America
      • 5.7.2.1 Brazil
      • 5.7.2.2 Argentina
      • 5.7.2.3 Rest of South America
    • 5.7.3 Europe
      • 5.7.3.1 United Kingdom
      • 5.7.3.2 Germany
      • 5.7.3.3 France
      • 5.7.3.4 Italy
      • 5.7.3.5 Rest of Europe
    • 5.7.4 Asia-Pacific
      • 5.7.4.1 China
      • 5.7.4.2 Japan
      • 5.7.4.3 India
      • 5.7.4.4 South Korea
      • 5.7.4.5 Rest of Asia-Pacific
    • 5.7.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration and Share
  • 6.2 Strategic Developments
  • 6.3 Company Profiles (includes Global-level Overview, Market-level Presence, Core Segments, Financials, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
    • 6.3.1 Microsoft Corporation
    • 6.3.2 IBM Corporation
    • 6.3.3 Amazon Web Services Inc.
    • 6.3.4 Google LLC
    • 6.3.5 Oracle Corporation
    • 6.3.6 Hewlett Packard Enterprise
    • 6.3.7 NVIDIA Corporation
    • 6.3.8 SAP SE
    • 6.3.9 Intel Corporation
    • 6.3.10 Wipro Limited
    • 6.3.11 NEC Corporation
    • 6.3.12 Accenture plc
    • 6.3.13 ServiceNow Inc.
    • 6.3.14 DataRobot Inc.
    • 6.3.15 UiPath Inc.
    • 6.3.16 C3.ai Inc.
    • 6.3.17 Palantir Technologies
    • 6.3.18 H2O.ai Inc.
    • 6.3.19 Sentient Technologies
    • 6.3.20 AiCure LLC

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-need Assessment