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市場調查報告書
商品編碼
2102415

工業自動化邊緣人工智慧市場：2034 年預測－按組件、部署模式、企業規模、技術、應用、最終用戶和地區分類的全球分析

Edge AI for Industrial Automation Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Deployment, Enterprise Size, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球工業自動化和邊緣人工智慧市場規模將達到 76 億美元，在預測期內將以 22.4% 的複合年成長率成長，到 2034 年將達到 256 億美元。

工業自動化邊緣人工智慧是指將人工智慧和機器學習系統直接部署在工業設備、控制器和邊緣運算節點上，無需依賴集中式雲端基礎設施即可實現即時資料處理、推理和決策。這些系統將專用人工智慧加速器、嵌入式處理器和最佳化的神經網路模型整合到位於網路邊緣的工業控制器、攝影機、感測器和閘道器中。該技術涵蓋用於模式識別的機器學習、用於視覺檢測的深度學習、用於品管的電腦視覺以及用於流程最佳化的強化學習。邊緣人工智慧能夠為關鍵工業應用帶來亞毫秒的回應時間、增強的資料隱私保護並降低頻寬需求。

即時處理需求

工業自動化流程中對即時決策的關鍵需求，正推動著對邊緣人工智慧解決方案的大量投資，以消除控制迴路中的雲端延遲。諸如機器人焊接、數控加工和高速封裝等製造應用需要毫秒級的響應時間，而廣域網路連接無法可靠地提供這種響應速度。 NVIDIA、Intel 和 Qualcomm 的邊緣人工智慧處理器提供了足夠的運算能力，可以直接在機器層面執行複雜的推理。汽車和半導體製造業的終端用戶更重視確定性性能而非集中式分析。從商業角度來看，這標誌著在時間緊迫的自動化領域，架構正從「雲端優先」轉向「邊緣優先」。

熱力學和電學約束

在工業環境中，將人工智慧推理工作負載部署到邊緣設備面臨諸多限制，尤其是在工廠車間運作所需的緊湊型無風扇外形尺寸下，溫度控管和功耗問題尤為突出。高性能人工智慧加速器會產生大量熱量，在多塵和振動的環境中，必須在沒有主動冷卻的情況下將其散發出去。邊緣設備的功率預算受到現有電力基礎設施和安全要求的限制。這些限制制約了可在邊緣硬體上有效運作的神經網路模型的複雜性，可能會為了速度和可靠性而犧牲準確性。

5G專用網路

在工業設施中部署專用 5G 網路，為邊緣 AI 架構創造了變革性的機遇，它將本地推理與高頻寬、低延遲的連接相結合，從而實現模型更新和協作。專用 5G 能夠實現邊緣 AI 節點、移動機器人和中央管理系統之間的確定性通訊，而無需佔用公共頻譜。製造地和物流中心正在利用專用網路為數千個連接的邊緣設備提供支持，並確保服務品質 (QoS)。最終用戶受益於混合架構，其中邊緣 AI 處理即時決策，而 5G回程傳輸則支援聚合分析。市場趨勢正在推動整合邊緣 AI 和專用網路解決方案的發展。

模型過時

人工智慧模型架構和學習方法的快速演進，以及軟硬體的緊密整合，為邊緣人工智慧部署帶來了過時的風險，因為現場升級十分困難。使用目前框架訓練的神經網路模型可能與下一代邊緣處理器不相容。運算能力固定的邊緣設備無法處理日益複雜、精度要求更高的模型。最終用戶面臨艱難的抉擇：部署當前一代解決方案，還是等待更先進的硬體？這些趨勢縮短了產品生命週期，並增加了工業邊緣人工智慧投資的總體擁有成本 (TCO)。

新型冠狀病毒（COVID-19）的影響：

新冠疫情初期擾亂了半導體價值鏈，導致邊緣人工智慧處理器供不應求，並延緩了工業部署專案。疫情期間，遠距辦公和強制性社交隔離的需求加速了人們對能夠減少人工參與製造設施的自主邊緣系統的興趣。由於遠距辦公帶來的流量壓力，雲端連線不堪重負，這場危機凸顯了本地智慧的價值。疫情後，在供應鏈韌性戰略和勞動力安全考量的驅動下，對邊緣人工智慧作為自主工業營運基礎的投資仍在繼續。

在預測期內，硬體領域預計將佔據最大的市場佔有率。

在預測期內，硬體領域預計將佔據最大的市場佔有率。這是因為專用人工智慧加速器、工業級處理器和邊緣運算設備是實現自動化環境中設備端推理的必要物理基礎。硬體包括GPU和NPU晶片、嵌入式控制器、工業用電腦以及整合處理能力的智慧感測器。 NVIDIA的Jetson平台、英特爾的Movidius和OpenVINO解決方案以及高通的人工智慧處理器正在推動工業邊緣運算市場的發展。終端用戶優先考慮具有寬溫範圍和抗震性能的堅固外形規格。這些商業性優勢反映了工業邊緣運算基礎設施的資本密集特性。

在預測期內，深度學習領域預計將呈現最高的複合年成長率。

在預測期內，深度學習領域預計將呈現最高的成長率，這主要得益於神經網路架構的突破性進展，這些進展使得在邊緣設備上直接進行日益複雜的視覺檢測、異常檢測和預測分析成為可能。在缺陷分類和預測性維護等複雜的工業任務中，深度學習模型比傳統的機器學習方法實現了更高的準確率。模型壓縮和量化技術使得以往依賴雲端的架構能夠部署到資源受限的邊緣硬體。品質關鍵型產業的終端用戶正在採用深度學習進行自動化檢測。演算法進步與硬體能力的融合正在加速其商業部署。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其集中了眾多主要的半導體和人工智慧硬體供應商、擁有先進的製造業以及企業對工業4.0技術的大量投資。美國處於主導地位，英偉達、英特爾和高通等公司正引領邊緣人工智慧處理器的創新，並推動汽車、航太和電子製造業的早期應用。加拿大受益於強大的人工智慧研究機構和政府對創新的大力支持。在墨西哥，隨著先進製造業的擴張，對邊緣智慧的需求也不斷成長。對邊緣人工智慧新創企業的創業投資投資也鞏固了該地區的創新領先地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的產業數位化、政府主導的智慧製造舉措，以及遍布中國、日本、韓國和台灣的大規模電子和半導體製造地。中國的國內半導體發展計畫優先發展邊緣人工智慧晶片的設計和製造。在日本，儘管面臨產業工人老化的挑戰，但對需要邊緣智慧的自動化技術的投資仍在穩步推進。韓國先進的顯示器和儲存晶片產業正在採用邊緣人工智慧進行製程控制。全部區域政府主導的「工業4.0」計畫正在為邊緣運算基礎設施提供資金和監管支援。

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所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域分類
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  • 競爭性標竿分析
    • 根據產品系列、企業發展和策略聯盟對重點公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰和機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球工業自動化邊緣人工智慧市場：按組件分類

  • 硬體
  • 軟體
  • 服務

第6章：全球工業自動化邊緣人工智慧市場：依部署方式分類

  • 現場
  • 雲端連接邊緣
  • 混合邊緣
  • 獨立邊緣

第7章 全球工業自動化邊緣人工智慧市場：依公司規模分類

  • 大公司
  • 小型企業

第8章：全球工業自動化邊緣人工智慧市場：按技術分類

  • 機器學習
  • 深度學習
  • 電腦視覺
  • 自然語言處理
  • 強化學習
  • 人工智慧世代

第9章 全球工業自動化邊緣人工智慧市場：按應用領域分類

  • 預測性保護
  • 目視檢查
  • 流程自動化
  • 資產監控
  • 工業機器人
  • 能源管理
  • 對工人進行安全監督

第10章：全球工業自動化邊緣人工智慧市場：按最終用戶分類

  • 工業公司
  • 系統整合商
  • OEMs
  • 自動化解決方案提供商
  • 工業基礎設施營運商

第11章 全球工業自動化邊緣人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • NVIDIA Corporation
  • Intel Corporation
  • Advanced Micro Devices, Inc.
  • Qualcomm Incorporated
  • Siemens AG
  • Schneider Electric SE
  • ABB Ltd.
  • Rockwell Automation, Inc.
  • Honeywell International Inc.
  • Cisco Systems, Inc.
  • Advantech Co., Ltd.
  • Bosch Rexroth AG
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • HPE（Hewlett Packard Enterprise）
  • Lenovo Group Limited
Product Code: SMRC38305

According to Stratistics MRC, the Global Edge AI for Industrial Automation Market is accounted for $7.6 billion in 2026 and is expected to reach $25.6 billion by 2034 growing at a CAGR of 22.4% during the forecast period. Edge AI for industrial automation refers to artificial intelligence and machine learning systems deployed directly on industrial devices, controllers, and edge computing nodes to enable real-time data processing, inference, and decision-making without dependency on centralized cloud infrastructure. These systems integrate specialized AI accelerators, embedded processors, and optimized neural network models into industrial controllers, cameras, sensors, and gateways located at the network edge. The technology encompasses machine learning for pattern recognition, deep learning for visual inspection, computer vision for quality control, and reinforcement learning for process optimization. Edge AI enables sub-millisecond response times, enhanced data privacy, and reduced bandwidth requirements for critical industrial applications.

Market Dynamics:

Driver:

Real-time processing needs

The critical requirement for instantaneous decision-making in industrial automation processes is driving substantial investment in edge AI solutions that eliminate cloud latency from control loops. Manufacturing applications such as robotic welding, CNC machining, and high-speed packaging require response times measured in milliseconds that wide-area network connectivity cannot reliably provide. Edge AI processors from NVIDIA, Intel, and Qualcomm deliver sufficient compute power for complex inference directly at the machine level. End users in automotive and semiconductor manufacturing prioritize deterministic performance over centralized analytics. The commercial implication is a shift from cloud-first to edge-first architectures for time-critical automation.

Restraint:

Thermal and power limits

The deployment of AI inference workloads on edge devices in industrial environments faces significant constraints related to thermal management and power consumption in compact, fanless form factors required for factory floor operation. High-performance AI accelerators generate substantial heat that must be dissipated without active cooling in dusty, vibration-prone environments. Power budgets for edge devices are limited by existing electrical infrastructure and safety requirements. These constraints restrict the complexity of neural network models that can run effectively on edge hardware, potentially compromising accuracy for speed and reliability.

Opportunity:

5G private networks

The deployment of private 5G networks in industrial facilities is creating transformative opportunities for edge AI architectures that combine local inference with high-bandwidth, low-latency connectivity for model updates and coordination. Private 5G enables deterministic communication between edge AI nodes, mobile robots, and central management systems without competing for public spectrum. Manufacturing campuses and logistics hubs leverage private networks to support thousands of connected edge devices with guaranteed quality of service. End users benefit from hybrid architectures where edge AI handles real-time decisions while 5G backhaul supports aggregated analytics. The commercial momentum favors integrated edge AI and private network solutions.

Threat:

Model obsolescence

The rapid evolution of AI model architectures and training techniques creates obsolescence risks for edge AI deployments where hardware and software are tightly coupled and difficult to upgrade in the field. Neural network models trained on current frameworks may not be compatible with next-generation edge processors. Edge devices with fixed compute capabilities cannot accommodate increasingly complex models that improve accuracy. End users face difficult trade-offs between deploying current-generation solutions and waiting for improved hardware. These dynamics compress product lifecycles and increase total cost of ownership for industrial edge AI investments.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted semiconductor supply chains, creating shortages of edge AI processors and delaying industrial deployment projects. Mid-pandemic, remote operations requirements and social distancing mandates accelerated interest in autonomous edge systems that reduce human presence in manufacturing facilities. The crisis highlighted the value of localized intelligence when cloud connectivity faced strain from remote work traffic. Post-pandemic, supply chain resilience strategies and labor availability concerns sustain investment in edge AI as a foundation for autonomous industrial operations.

The hardware segment is expected to be the largest during the forecast period

The hardware segment is expected to account for the largest market share during the forecast period, due to the essential requirement for specialized AI accelerators, industrial-grade processors, and edge computing devices as the physical foundation enabling on-device inference in automation environments. Hardware encompasses GPU and NPU chips, embedded controllers, industrial PCs, and smart sensors with integrated processing capabilities. NVIDIA's Jetson platform, Intel's Movidius and OpenVINO solutions, and Qualcomm's AI processors dominate the industrial edge landscape. End users prioritize ruggedized form factors with extended temperature ranges and vibration resistance. The commercial dominance reflects the capital-intensive nature of industrial edge infrastructure.

The deep learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the deep learning segment is predicted to witness the highest growth rate, driven by breakthrough advances in neural network architectures that enable increasingly sophisticated visual inspection, anomaly detection, and predictive analytics directly on edge devices. Deep learning models achieve accuracy levels that surpass traditional machine learning approaches for complex industrial tasks such as defect classification and predictive maintenance. Model compression and quantization techniques enable deployment of previously cloud-bound architectures on resource-constrained edge hardware. End users in quality-critical industries adopt deep learning for automated inspection. The convergence of algorithmic advances and hardware capabilities accelerates commercial deployment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the concentration of leading semiconductor and AI hardware vendors, advanced manufacturing sectors, and substantial enterprise investment in Industry 4.0 technologies. The United States leads with NVIDIA, Intel, and Qualcomm driving edge AI processor innovation and early adoption across automotive, aerospace, and electronics manufacturing. Canada benefits from strong AI research institutions and government innovation funding. Mexico's growing advanced manufacturing base creates demand for edge intelligence. Venture capital funding for edge AI startups sustains regional innovation leadership.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapid industrial digitization, government smart manufacturing initiatives, and massive electronics and semiconductor manufacturing bases across China, Japan, South Korea, and Taiwan. China's domestic semiconductor development programs prioritize edge AI chip design and manufacturing. Japan's aging industrial workforce drives automation investments requiring edge intelligence. South Korea's advanced display and memory chip industries deploy edge AI for process control. Government Industry 4.0 programs across the region provide funding and regulatory support for edge computing infrastructure.

Key players in the market

Some of the key players in Edge AI for Industrial Automation include NVIDIA Corporation, Intel Corporation, Advanced Micro Devices, Inc., Qualcomm Incorporated, Siemens AG, Schneider Electric SE, ABB Ltd., Rockwell Automation, Inc., Honeywell International Inc., Cisco Systems, Inc., Advantech Co., Ltd., Bosch Rexroth AG, IBM Corporation, Microsoft Corporation, Oracle Corporation, HPE (Hewlett Packard Enterprise) and Lenovo Group Limited.

Key Developments:

In June 2026, NVIDIA Corporation launched a next-generation industrial edge AI platform combining enhanced GPU acceleration with optimized inference engines, enabling real-time defect detection and predictive maintenance on compact fanless devices for factory floor deployment.

In May 2026, Intel Corporation introduced an updated OpenVINO toolkit release with specialized optimizations for industrial automation workloads, reducing deep learning model inference latency by forty percent on existing edge hardware platforms.

In April 2026, Siemens AG expanded its industrial edge computing portfolio with AI-ready controllers featuring onboard neural processing units for real-time quality inspection and process optimization in discrete manufacturing environments.

Components Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Natural Language Processing
  • Reinforcement Learning
  • Generative AI

Deployments Covered:

  • On-Premise
  • Cloud-Connected Edge
  • Hybrid Edge
  • Standalone Edge

Enterprise Sizes Covered:

  • Large Enterprises
  • Small and Medium Enterprises

Applications Covered:

  • Predictive Maintenance
  • Visual Inspection
  • Process Automation
  • Asset Monitoring
  • Industrial Robotics
  • Energy Management
  • Worker Safety Monitoring

End Users Covered:

  • Industrial Enterprises
  • System Integrators
  • OEMs
  • Automation Solution Providers
  • Industrial Infrastructure Operators

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Edge AI for Industrial Automation Market, By Component

  • 5.1 Hardware
  • 5.2 Software
  • 5.3 Services

6 Global Edge AI for Industrial Automation Market, By Deployment

  • 6.1 On-Premise
  • 6.2 Cloud-Connected Edge
  • 6.3 Hybrid Edge
  • 6.4 Standalone Edge

7 Global Edge AI for Industrial Automation Market, By Enterprise Size

  • 7.1 Large Enterprises
  • 7.2 Small and Medium Enterprises

8 Global Edge AI for Industrial Automation Market, By Technology

  • 8.1 Machine Learning
  • 8.2 Deep Learning
  • 8.3 Computer Vision
  • 8.4 Natural Language Processing
  • 8.5 Reinforcement Learning
  • 8.6 Generative AI

9 Global Edge AI for Industrial Automation Market, By Application

  • 9.1 Predictive Maintenance
  • 9.2 Visual Inspection
  • 9.3 Process Automation
  • 9.4 Asset Monitoring
  • 9.5 Industrial Robotics
  • 9.6 Energy Management
  • 9.7 Worker Safety Monitoring

10 Global Edge AI for Industrial Automation Market, By End User

  • 10.1 Industrial Enterprises
  • 10.2 System Integrators
  • 10.3 OEMs
  • 10.4 Automation Solution Providers
  • 10.5 Industrial Infrastructure Operators

11 Global Edge AI for Industrial Automation Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 NVIDIA Corporation
  • 14.2 Intel Corporation
  • 14.3 Advanced Micro Devices, Inc.
  • 14.4 Qualcomm Incorporated
  • 14.5 Siemens AG
  • 14.6 Schneider Electric SE
  • 14.7 ABB Ltd.
  • 14.8 Rockwell Automation, Inc.
  • 14.9 Honeywell International Inc.
  • 14.10 Cisco Systems, Inc.
  • 14.11 Advantech Co., Ltd.
  • 14.12 Bosch Rexroth AG
  • 14.13 IBM Corporation
  • 14.14 Microsoft Corporation
  • 14.15 Oracle Corporation
  • 14.16 HPE (Hewlett Packard Enterprise)
  • 14.17 Lenovo Group Limited

List of Tables

  • Table 1 Global Edge AI for Industrial Automation Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Edge AI for Industrial Automation Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Edge AI for Industrial Automation Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global Edge AI for Industrial Automation Market Outlook, By Software (2023-2034) ($MN)
  • Table 5 Global Edge AI for Industrial Automation Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global Edge AI for Industrial Automation Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 7 Global Edge AI for Industrial Automation Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 8 Global Edge AI for Industrial Automation Market Outlook, By Cloud-Connected Edge (2023-2034) ($MN)
  • Table 9 Global Edge AI for Industrial Automation Market Outlook, By Hybrid Edge (2023-2034) ($MN)
  • Table 10 Global Edge AI for Industrial Automation Market Outlook, By Standalone Edge (2023-2034) ($MN)
  • Table 11 Global Edge AI for Industrial Automation Market Outlook, By Enterprise Size (2023-2034) ($MN)
  • Table 12 Global Edge AI for Industrial Automation Market Outlook, By Large Enterprises (2023-2034) ($MN)
  • Table 13 Global Edge AI for Industrial Automation Market Outlook, By Small and Medium Enterprises (2023-2034) ($MN)
  • Table 14 Global Edge AI for Industrial Automation Market Outlook, By Technology (2023-2034) ($MN)
  • Table 15 Global Edge AI for Industrial Automation Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 16 Global Edge AI for Industrial Automation Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 17 Global Edge AI for Industrial Automation Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 18 Global Edge AI for Industrial Automation Market Outlook, By Natural Language Processing (2023-2034) ($MN)
  • Table 19 Global Edge AI for Industrial Automation Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 20 Global Edge AI for Industrial Automation Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 21 Global Edge AI for Industrial Automation Market Outlook, By Application (2023-2034) ($MN)
  • Table 22 Global Edge AI for Industrial Automation Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 23 Global Edge AI for Industrial Automation Market Outlook, By Visual Inspection (2023-2034) ($MN)
  • Table 24 Global Edge AI for Industrial Automation Market Outlook, By Process Automation (2023-2034) ($MN)
  • Table 25 Global Edge AI for Industrial Automation Market Outlook, By Asset Monitoring (2023-2034) ($MN)
  • Table 26 Global Edge AI for Industrial Automation Market Outlook, By Industrial Robotics (2023-2034) ($MN)
  • Table 27 Global Edge AI for Industrial Automation Market Outlook, By Energy Management (2023-2034) ($MN)
  • Table 28 Global Edge AI for Industrial Automation Market Outlook, By Worker Safety Monitoring (2023-2034) ($MN)
  • Table 29 Global Edge AI for Industrial Automation Market Outlook, By End User (2023-2034) ($MN)
  • Table 30 Global Edge AI for Industrial Automation Market Outlook, By Industrial Enterprises (2023-2034) ($MN)
  • Table 31 Global Edge AI for Industrial Automation Market Outlook, By System Integrators (2023-2034) ($MN)
  • Table 32 Global Edge AI for Industrial Automation Market Outlook, By OEMs (2023-2034) ($MN)
  • Table 33 Global Edge AI for Industrial Automation Market Outlook, By Automation Solution Providers (2023-2034) ($MN)
  • Table 34 Global Edge AI for Industrial Automation Market Outlook, By Industrial Infrastructure Operators (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.