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市場調查報告書
商品編碼
1698324
人工智慧（AI）終端市場機會、成長動力、產業趨勢分析及2025-2034年預測Artificial Intelligence (AI) Terminal Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025-2034 |
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2024 年全球人工智慧終端市場價值為 222 億美元，預計 2025 年至 2034 年的複合年成長率將達到 19.6%。成長得益於人工智慧晶片組、神經處理單元 (NPU) 和人工智慧加速器的進步，這些進步在提高效能的同時也提高了能源效率。 AI終端受益於邊緣AI，它在本地處理資料，減少對雲端運算的依賴。這可以提高回應時間，增強安全性，並使企業能夠實現低延遲的 AI 處理以進行即時決策。人工智慧物聯網設備、消費性電子產品和智慧醫療解決方案的日益普及促進了市場擴張。人工智慧處理器的技術進步使得人工智慧終端成為下一代運算的關鍵。
市場依組件分為硬體和軟體。 2024 年，硬體將佔據該領域的主導地位，創造 152 億美元的收入，預計在預測期內的複合年成長率為 19.9%。 AI終端依賴強大的處理器，包括GPU、FPGA和ASIC，這些處理器對於即時AI運算至關重要。汽車、醫療保健和製造等行業正在整合人工智慧推理硬體，以支援自動化並增強基於人工智慧的決策。對人工智慧晶片組、嵌入式設備和支援人工智慧的伺服器的投資增加進一步鞏固了硬體的主導地位。人工智慧物聯網、自主系統和人工智慧驅動的監控解決方案的擴展也在推動成長，從而加強了金融科技人工智慧硬體市場。
| 市場範圍 | |
|---|---|
| 起始年份 | 2024 |
| 預測年份 | 2025-2034 |
| 起始值 | 222億美元 |
| 預測值 | 1035億美元 |
| 複合年成長率 | 19.6% |
AI終端市場部署模式包括本地部署、雲端部署、混合式部署等。 2024 年，基於雲端的解決方案以 47% 的佔有率引領市場，預計到 2034 年將以超過 20% 的複合年成長率成長。雲端運算使企業無需大量硬體投資即可擴展 AI 應用，支援跨多個行業的 AI 模型訓練、即時資料分析和推理。隨著企業產生越來越多的人工智慧驅動資料，對雲端基礎設施的需求持續上升。與雲端系統整合的AI終端，能夠有效率地收集和處理資料，成為企業尋求高階AI能力的必備工具。
根據技術，市場細分為機器學習 (ML) 和深度學習 (DL)、電腦視覺、機器人流程自動化、物聯網感測器和其他人工智慧驅動技術。 ML 和 DL 仍然佔據主導地位，因為它們能夠處理大量資料、識別模式並即時執行操作。利用深度學習神經網路的人工智慧系統增強了自然語言處理 (NLP)、語音辨識和自主系統的能力。銀行、零售和醫療保健領域對人工智慧解決方案的需求日益成長，正在加速該領域的應用。
2024年北美佔最大市場佔有率，約佔全球AI終端市場的40%，創造約90億美元的收入。該地區受益於對人工智慧驅動硬體、邊緣運算解決方案和半導體技術的大力投資。領先的科技公司和人工智慧晶片組製造商的存在支持了醫療保健、國防和零售等各個行業的市場擴張。基於邊緣的人工智慧運算解決方案的增強開發進一步推動了其應用，特別是在需要改進資料隱私和安全性的應用中。
目錄
第1章：方法論與範圍
- 研究設計
- 研究方法
- 資料收集方法
- 基礎估算與計算
- 基準年計算
- 市場估計的主要趨勢
- 預測模型
- 初步研究與驗證
- 主要來源
- 資料探勘來源
- 市場定義
第2章：執行摘要
第3章：行業洞察
- 產業生態系統分析
- 供應商格局
- 製造商
- 感測器提供者
- 軟體開發者
- 技術整合商
- 最終用途
- 利潤率分析
- 技術與創新格局
- 專利分析
- 重要新聞和舉措
- 監管格局
- 衝擊力
- 成長動力
- 邊緣人工智慧技術的進步
- 對人工智慧設備的需求不斷成長
- 5G和物聯網網路的擴展
- 汽車和醫療保健領域人工智慧的融合日益加強
- 政府和企業對人工智慧基礎設施的投資
- 產業陷阱與挑戰
- 初期投資和部署成本高
- 資料隱私和安全問題
- 成長動力
- 成長潛力分析
- 波特的分析
- PESTEL 分析
第4章：競爭格局
- 介紹
- 公司市佔率分析
- 競爭定位矩陣
- 戰略展望矩陣
第5章：市場估計與預測：按組件，2021 - 2034 年
- 主要趨勢
- 硬體
- 軟體
第6章：市場估計與預測：依技術分類，2021 - 2034 年
- 主要趨勢
- 機器學習和深度學習
- 電腦視覺
- 機器人流程自動化
- 物聯網感測器
- 其他
第7章：市場估計與預測：依部署模式，2021 - 2034 年
- 主要趨勢
- 本地
- 基於雲端
- 混合
第8章：市場估計與預測：依最終用途，2021 - 2034 年
- 主要趨勢
- 運輸
- 衛生保健
- 零售
- 金融服務業
- 其他
第9章：市場估計與預測：按地區，2021 - 2034 年
- 主要趨勢
- 北美洲
- 美國
- 加拿大
- 歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 俄羅斯
- 北歐人
- 亞太地區
- 中國
- 印度
- 日本
- 韓國
- 澳新銀行
- 拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
- 中東及非洲
- 阿拉伯聯合大公國
- 沙烏地阿拉伯
- 南非
第10章：公司簡介
- Adobe
- Alibaba Group
- Amazon
- Apple
- Bosch
- Cisco
- Dell Technologies
- Google (Alphabet inc.)
- Huawei Technologies
- IBM
- Intel
- Meta Platforms
- Microsoft
- Nvidia
- Oracle
- Sony
- Tencent Holdings
- Tesla
- VMware
- Zoom Video Communications
The Global Artificial Intelligence Terminal Market was valued at USD 22.2 billion in 2024 and is expected to expand at a CAGR of 19.6% from 2025 to 2034. Growth is fueled by advancements in AI chipsets, neural processing units (NPUs), and AI accelerators, which enhance performance while improving energy efficiency. AI terminals benefit from edge AI, which processes data locally, reducing reliance on cloud computing. This improves response times, strengthens security, and enables businesses to achieve low-latency AI processing for real-time decision-making. Increasing adoption of AI-powered IoT devices, consumer electronics, and smart healthcare solutions contributes to market expansion. Technological improvements in AI processors are making AI terminals essential for next-generation computing.
The market is categorized by component into hardware and software. In 2024, hardware dominated the sector, generating USD 15.2 billion in revenue, and is projected to grow at a CAGR of 19.9% through the forecast period. AI terminals rely on powerful processors, including GPUs, FPGAs, and ASICs, which are crucial for real-time AI computations. Industries such as automotive, healthcare, and manufacturing are integrating AI inference hardware to support automation and enhance AI-based decision-making. Increased investments in AI chipsets, embedded devices, and AI-enabled servers further reinforce hardware's dominance. The expansion of AIoT, autonomous systems, and AI-driven surveillance solutions is also driving growth, strengthening the market for fintech AI hardware.
| Market Scope | |
|---|---|
| Start Year | 2024 |
| Forecast Year | 2025-2034 |
| Start Value | $22.2 Billion |
| Forecast Value | $103.5 Billion |
| CAGR | 19.6% |
Deployment modes in the AI terminal market include on-premises, cloud-based, and hybrid solutions. In 2024, cloud-based solutions led the market with a 47% share and are expected to grow at a CAGR exceeding 20% through 2034. Cloud computing allows enterprises to scale AI applications without substantial hardware investments, supporting AI model training, real-time data analysis, and inference across multiple industries. As businesses generate increasing amounts of AI-driven data, demand for cloud infrastructure continues to rise. AI terminals integrated with cloud-based systems enable efficient data collection and processing, making them an essential tool for enterprises seeking advanced AI capabilities.
By technology, the market is segmented into machine learning (ML) and deep learning (DL), computer vision, robotic process automation, IoT sensors, and other AI-driven technologies. ML and DL remain dominant due to their ability to process vast amounts of data, identify patterns, and execute actions in real-time. AI systems utilizing deep learning neural networks enhance capabilities in natural language processing (NLP), speech recognition, and autonomous systems. The increasing need for AI solutions across banking, retail, and healthcare is accelerating adoption in this segment.
North America held the largest market share in 2024, accounting for approximately 40% of the global AI terminal market, generating around USD 9 billion in revenue. The region benefits from strong investments in AI-driven hardware, edge computing solutions, and semiconductor technologies. The presence of leading technology firms and AI chipset manufacturers supports market expansion across industries, including healthcare, defense, and retail. Enhanced development of edge-based AI computing solutions is further fueling adoption, particularly in applications requiring improved data privacy and security.
Table of Contents
Chapter 1 Methodology & Scope
- 1.1 Research design
- 1.1.1 Research approach
- 1.1.2 Data collection methods
- 1.2 Base estimates and calculations
- 1.2.1 Base year calculation
- 1.2.2 Key trends for market estimates
- 1.3 Forecast model
- 1.4 Primary research & validation
- 1.4.1 Primary sources
- 1.4.2 Data mining sources
- 1.5 Market definitions
Chapter 2 Executive Summary
- 2.1 Industry 3600 synopsis, 2021 - 2034
Chapter 3 Industry Insights
- 3.1 Industry ecosystem analysis
- 3.2 Supplier landscape
- 3.2.1 Manufacturer
- 3.2.2 Sensor providers
- 3.2.3 Software developers
- 3.2.4 Technology Integrators
- 3.2.5 End Use
- 3.3 Profit margin analysis
- 3.4 Technology & innovation landscape
- 3.5 Patent analysis
- 3.6 Key news & initiatives
- 3.7 Regulatory landscape
- 3.8 Impact forces
- 3.8.1 Growth drivers
- 3.8.1.1 Advancements in edge AI technology
- 3.8.1.2 Rising demand for AI-powered devices
- 3.8.1.3 Expansion of 5G and IoT networks
- 3.8.1.4 Growing AI integration in automotive and healthcare
- 3.8.1.5 Government and enterprise investments in AI infrastructure
- 3.8.2 Industry pitfalls & challenges
- 3.8.2.1 High initial investment and deployment costs
- 3.8.2.2 Data privacy and security concerns
- 3.8.1 Growth drivers
- 3.9 Growth potential analysis
- 3.10 Porter's analysis
- 3.11 PESTEL analysis
Chapter 4 Competitive Landscape, 2024
- 4.1 Introduction
- 4.2 Company market share analysis
- 4.3 Competitive positioning matrix
- 4.4 Strategic outlook matrix
Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Bn)
- 5.1 Key trends
- 5.2 Hardware
- 5.3 Software
Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Bn)
- 6.1 Key trends
- 6.2 ML and DL
- 6.3 Computer vision
- 6.4 Robotic process automation
- 6.5 IOT sensors
- 6.6 Others
Chapter 7 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Bn)
- 7.1 Key trends
- 7.2 On-premises
- 7.3 Cloud-based
- 7.4 Hybrid
Chapter 8 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn)
- 8.1 Key trends
- 8.2 Transportation
- 8.3 Healthcare
- 8.4 Retail
- 8.5 BFSI
- 8.6 Others
Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)
- 9.1 Key trends
- 9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
- 9.3 Europe
- 9.3.1 UK
- 9.3.2 Germany
- 9.3.3 France
- 9.3.4 Italy
- 9.3.5 Spain
- 9.3.6 Russia
- 9.3.7 Nordics
- 9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 India
- 9.4.3 Japan
- 9.4.4 South Korea
- 9.4.5 ANZ
- 9.5 Latin America
- 9.5.1 Brazil
- 9.5.2 Mexico
- 9.5.3 Argentina
- 9.6 MEA
- 9.6.1 UAE
- 9.6.2 Saudi Arabia
- 9.6.3 South Africa
Chapter 10 Company Profiles
- 10.1 Adobe
- 10.2 Alibaba Group
- 10.3 Amazon
- 10.4 Apple
- 10.5 Bosch
- 10.6 Cisco
- 10.7 Dell Technologies
- 10.8 Google (Alphabet inc.)
- 10.9 Huawei Technologies
- 10.10 IBM
- 10.11 Intel
- 10.12 Meta Platforms
- 10.13 Microsoft
- 10.14 Nvidia
- 10.15 Oracle
- 10.16 Sony
- 10.17 Tencent Holdings
- 10.18 Tesla
- 10.19 VMware
- 10.20 Zoom Video Communications
