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IoT 人工智能邊緣運算

市場調查報告書

商品編碼

1865518

全球工業環境邊緣人工智慧市場：預測至 2032 年—按組件、部署方式、應用、最終用戶和地區分類的分析

Edge AI in Industrial Environments Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software and Integration & Support Services), Deployment Mode, Application, End User and By Geography

出版日期: 2025年11月01日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的一項研究，預計 2025 年全球工業環境邊緣人工智慧市場價值為 42 億美元，到 2032 年將達到 103.3 億美元，預測期內複合年成長率為 13.7%。

面向工業環境的邊緣人工智慧透過在資料來源附近進行智慧資料處理，變革了營運模式。對從感測器和機器本地收集的數據進行分析，可最大限度地減少延遲、提高可靠性並降低對雲端的依賴。這種即時處理能力支援預測性維護、流程最佳化和早期故障檢測。製造業、公共產業和物流等行業正在利用邊緣人工智慧加速自動化、提高資產利用率並增強安全性。本地決策使各行業即使在網路狀況不佳的情況下也能保持持續生產力。邊緣人工智慧的整合促進了互聯且適應性強的工業生態系統的發展，從而提升效率、韌性和智慧營運控制。

根據《國際運算工程與管理日誌》（IJCEM）的報告，一項關於工業4.0中邊緣人工智慧的研究發現，在汽車和航太領域的試點部署中，基於邊緣的預測維修系統可將計畫外停機時間減少高達30%。該研究強調了聯邦學習和邊緣推理在保護敏感運行資料方面的重要作用。

對預測性維護和營運效率的需求日益成長

對提升營運效率和預測性維護日益成長的需求正在推動邊緣人工智慧在工業應用中的擴展。邊緣人工智慧透過處理來自感測器和機器的即時數據，預測設備故障並防止計劃外停機。這種預測能力可以提高運作、降低維護成本並支援更智慧的生產計畫。此外，邊緣人工智慧還能實現持續的流程最佳化和高效的能源管理。各行業都能從中受益，獲得更快的反應速度和更可靠的效能。隨著製造業尋求提高生產力和減少營運浪費，邊緣驅動的智慧為數位化效率提供了一條永續的途徑，幫助企業提高長期可靠性和資產利用率。

高昂的實施和整合成本

面向工業環境的邊緣人工智慧市場面臨著一項重大挑戰：高昂的部署和整合成本。部署邊緣智慧需要對設備、感測器、平台和專業人員進行大量投資。許多現有的工業系統與人工智慧技術不相容，需要進行昂貴的現代化改造。對於中小企業而言，這種財務負擔構成了大規模採用邊緣人工智慧的障礙。此外，持續的維護、軟體更新和資料處理成本也會對預算造成壓力。這些經濟限制使得企業難以充分利用邊緣人工智慧的功能。因此，成本仍然是一個重要的限制因素，減緩了邊緣人工智慧的普及速度，並阻礙了其在資源受限的工業領域的廣泛應用。

智慧製造與工業4.0的擴展

工業4.0和智慧製造的日益普及，為工業環境中的邊緣人工智慧市場創造了巨大的成長機會。邊緣人工智慧為工廠提供即時分析、自動化決策和智慧控制，從而提高效率和營運靈活性。其整合有助於預測性維護、品質保證數位雙胞胎仿真，進而建構更智慧的生產生態系統。隨著各行業向數據驅動和自主系統轉型，基於邊緣的智慧將增強製造業的競爭力和永續性。這一演進支援無縫連接、提高生產效率和減少停機時間。因此，邊緣人工智慧是工業4.0革命的核心，加速全球製造業的數位轉型。

科技快速過時

技術變革的快速步伐對工業環境的邊緣人工智慧市場構成重大威脅。隨著人工智慧演算法、處理器和邊緣設備的快速發展，現有設備可能很快就會過時。企業難以在不增加高成本下保持相容性並升級系統。這種持續的現代化需求可能導致營運中斷和盈利下降。此外，缺乏統一的技術標準限制了互通性，使得跨不同平台的整合變得困難。過早過時的風險阻礙了一些公司進行大規模投資，減緩了邊緣人工智慧系統的整體普及速度，並使其長期價值和穩定性受到不確定性。

新冠疫情的影響：

新冠疫情為工業環境的邊緣人工智慧市場帶來了挑戰和機會。疫情初期，計劃延期、供應鏈中斷和技術投資減少，但也加速了自動化和數位化創新。隨著各行業適應遠端營運和勞動力限制，邊緣人工智慧成為實現自主決策和即時洞察的關鍵工具。企業紛紛採用邊緣運算來維持生產力、最佳化流程並最大限度地減少中斷。在疫情恢復階段，對智慧自主系統的需求顯著成長。總而言之，儘管疫情初期造成了一定的阻礙，但最終增強了邊緣人工智慧長期應用的前景。

預計在預測期內，硬體細分市場將佔據最大的市場佔有率。

由於硬體在支援即時智慧和自動化方面發揮關鍵作用，預計在預測期內，硬體領域將佔據最大的市場佔有率。感測器、處理器、閘道器和人工智慧加速器等核心組件對於本地數據採集和分析至關重要。這些設備能夠提升工業環境中的運作速度、可靠性和效率。對智慧硬體的日益依賴使得預測性維護、流程最佳化和現場即時決策成為可能。先進晶片組和邊緣運算設備的整合不斷增強系統性能和擴充性。隨著各產業向人工智慧驅動的營運模式轉型，強大的硬體基礎設施仍是實現高效邊緣智慧的基礎。

在預測期內，混合動力汽車細分市場將實現最高的複合年成長率。

由於其適應性強且均衡的架構，混合型解決方案預計將在預測期內呈現最高的成長率。將本地運算與雲端功能結合，既能實現即時本地處理，又能利用雲端基礎設施進行進階分析和儲存。這種雙管齊下的方法降低了延遲，提高了可擴展性，並增強了資料安全性。各行各業都能從即時邊緣洞察和無所不在的集中式智慧中獲益。混合系統的靈活性使其成為需要可靠性和效率的複雜工業運作的理想選擇。隨著企業對其數位生態系統進行現代化改造，混合邊緣人工智慧解決方案正迅速獲得市場認可，以實現效能和控制的最佳化。

佔比最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其先進的基礎設施和對數位轉型的高度重視。該地區的關鍵產業，例如製造業、物流業和公共產業，正在積極採用邊緣人工智慧來提高營運效率、預測性維護和自動化水準。領先的人工智慧和半導體公司的存在正在加速創新和大規模應用。對5G、物聯網和智慧工廠技術的持續投資將進一步推動市場成長。有利於工業現代化的監管政策和政府措施也發揮關鍵作用。憑藉其強大的技術生態系統和早期應用文化，北美將繼續在邊緣人工智慧解決方案的採用和發展方面保持主導地位。

複合年成長率最高的地區：

由於工業擴張加速和對數位轉型的堅定承諾，亞太地區預計將在預測期內實現最高的複合年成長率。中國、日本、印度和韓國等國家正在快速採用人工智慧、物聯網和5G技術來實現工業營運的現代化。邊緣人工智慧在該地區的應用支援自動化、預測性維護和更智慧的製造流程。政府主導的旨在促進工業4.0和發展智慧基礎設施的舉措進一步推動了成長。在對先進技術投資不斷增加和注重效率的推動下，亞太地區正成為邊緣人工智慧應用領域最具活力且發展最快的市場。

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公司簡介
- 對最多三家其他公司進行全面分析
- 對主要企業進行SWOT分析（最多3家公司）
區域分類
- 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
競爭基準化分析
- 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

介紹
北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 其他亞太地區
南美洲
- 阿根廷
- 巴西
- 智利
- 南美洲其他地區
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第10章：主要趨勢

合約、商業夥伴關係和合資企業
企業合併（M&A）
新產品發布
業務拓展
其他關鍵策略

第11章公司簡介

NVIDIA
Intel Corporation
GE Vernova
Siemens
Rockwell Automation
ABB
IBM
Advantech
Bosch
ClearBlade
CanaryBit
Emerson
MicroAI
ADLINK
Arm

簡介目錄圖表

Product Code: SMRC32137

According to Stratistics MRC, the Global Edge AI in Industrial Environments Market is accounted for $4.20 billion in 2025 and is expected to reach $10.33 billion by 2032 growing at a CAGR of 13.7% during the forecast period. Edge AI in industrial environments transforms operations by enabling intelligent data processing close to the data source. It minimizes latency, improves reliability, and reduces cloud dependency through on-site analytics of data collected from sensors and machines. This real-time capability supports predictive maintenance, process optimization, and early fault detection. Sectors like manufacturing, utilities, and logistics leverage Edge AI to achieve faster automation, better asset utilization, and enhanced safety. With local decision-making, industries maintain continuous productivity even under poor network conditions. The integration of Edge AI fosters a connected and adaptive industrial ecosystem, promoting efficiency, resilience, and intelligent operational control.

According to International Journal of Computational Engineering & Management (IJCEM), a study on Edge AI in Industry 4.0 found that Edge-based predictive maintenance systems reduced unplanned downtime by up to 30% in pilot deployments across automotive and aerospace sectors. The study emphasized the role of federated learning and edge inference in protecting sensitive operational data.

Market Dynamics:

Driver:

Rising need for predictive maintenance and operational efficiency

The need for improved operational performance and predictive maintenance is fueling the expansion of Edge AI in industrial applications. By processing real-time data from sensors and machinery, Edge AI can anticipate equipment malfunctions and prevent unexpected breakdowns. This predictive capability enhances uptime, reduces maintenance costs, and supports smarter production planning. Additionally, Edge AI enables continuous process optimization and efficient energy management. Industries benefit from faster responses and more reliable performance. As manufacturers aim to increase productivity and reduce operational waste, edge-driven intelligence offers a sustainable path to digital efficiency, helping enterprises achieve long-term reliability and higher asset utilization.

Restraint:

High implementation and integration costs

The Edge AI in Industrial Environments Market faces a key challenge from high setup and integration expenses. Implementing edge intelligence involves costly investments in devices, sensors, platforms, and trained professionals. Many existing industrial systems lack compatibility with AI technologies, requiring expensive modernization. For smaller firms, these financial demands hinder large-scale adoption. Additionally, ongoing costs for maintenance, software upgrades, and data handling further strain budgets. These economic constraints make it difficult for organizations to fully leverage Edge AI capabilities. As a result, cost remains a critical limiting factor, slowing its expansion and preventing widespread application in resource-constrained industrial sectors.

Opportunity:

Expansion of smart manufacturing and industry 4.0

The growing adoption of Industry 4.0 and smart manufacturing is creating major growth prospects for the Edge AI in Industrial Environments Market. Edge AI empowers factories with real-time analytics, automated decision-making, and intelligent control, improving efficiency and operational flexibility. Its integration facilitates predictive maintenance, quality assurance, and digital twin simulations, enabling smarter production ecosystems. With the industrial shift toward data-driven and autonomous systems, edge-based intelligence strengthens manufacturing competitiveness and sustainability. This evolution supports seamless connectivity, greater productivity, and reduced downtime. Consequently, Edge AI stands at the core of the Industry 4.0 revolution, accelerating the digital transformation of global manufacturing operations.

Threat:

Rapid technological obsolescence

The fast pace of technological change poses a significant threat to the Edge AI in Industrial Environments Market. As AI algorithms, processors, and edge devices evolve rapidly, existing installations may become obsolete within short periods. Organizations struggle to maintain compatibility and upgrade systems without incurring high costs. This constant need for modernization can lead to operational disruptions and reduced profitability. Additionally, the absence of unified technology standards limits interoperability, making integration difficult across diverse platforms. The risk of early obsolescence discourages some companies from large-scale investments, slowing overall adoption and creating uncertainty around the long-term value and stability of Edge AI systems.

Covid-19 Impact:

The COVID-19 pandemic created both challenges and opportunities for the Edge AI in Industrial Environments Market. While early phases saw project delays, supply chain interruptions, and reduced technology spending, the situation also accelerated automation and digital innovation. As industries adapted to remote operations and workforce limitations, Edge AI emerged as a vital tool for enabling autonomous decision-making and real-time insights. Organizations adopted edge computing to maintain productivity, optimize processes, and minimize disruptions. In the recovery stage, demand for intelligent, self-sufficient systems increased significantly. Overall, the pandemic served as an initial barrier but ultimately strengthened long-term growth prospects for Edge AI adoption.

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, driven by its critical role in supporting real-time intelligence and automation. Core components such as sensors, processors, gateways, and AI accelerators are essential for collecting and analyzing data locally. These devices enhance operational speed, reliability, and efficiency in industrial settings. Growing reliance on intelligent hardware enables predictive maintenance, process optimization, and instant decision-making at the source. The integration of advanced chipsets and edge computing devices continues to expand system performance and scalability. As industries increasingly shift toward AI-enabled operations, robust hardware infrastructure remains the foundation for effective and efficient edge intelligence.

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

Over the forecast period, the hybrid segment is predicted to witness the highest growth rate, due to its adaptive and balanced architecture. By merging on-premises computing with cloud capabilities, it enables real-time local processing while utilizing cloud infrastructure for advanced analytics and storage. This dual approach ensures reduced latency, improved scalability, and stronger data security. Industries benefit from both immediate edge insights and broader centralized intelligence. The flexibility of hybrid systems makes them ideal for complex industrial operations demanding reliability and efficiency. As companies modernize digital ecosystems, hybrid Edge AI solutions are rapidly gaining traction for optimized performance and control.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by advanced infrastructure and a strong focus on digital transformation. The region's leading industries-such as manufacturing, logistics, and utilities-actively deploy Edge AI to improve operational efficiency, predictive maintenance, and automation. The presence of major AI and semiconductor companies accelerates innovation and large-scale implementation. Continuous investments in 5G, IoT, and smart factory technologies further strengthen market growth. Favorable regulatory policies and government initiatives promoting industrial modernization also play a key role. With its robust technological ecosystem and early adoption culture, North America continues to lead in deploying and evolving Edge AI solutions.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to its accelerating industrial expansion and strong commitment to digital transformation. Nations like China, Japan, India, and South Korea are rapidly implementing AI, IoT, and 5G technologies to modernize industrial operations. Edge AI adoption in this region supports automation, predictive maintenance, and smarter manufacturing processes. Government-backed initiatives promoting Industry 4.0 and intelligent infrastructure development are further boosting growth. With increasing investments in advanced technologies and a focus on efficiency, Asia-Pacific is emerging as the most dynamic and fastest-evolving market for Edge AI applications.

Key players in the market

Some of the key players in Edge AI in Industrial Environments Market include NVIDIA, Intel Corporation, GE Vernova, Siemens, Rockwell Automation, ABB, IBM, Advantech, Bosch, ClearBlade, CanaryBit, Emerson, MicroAI, ADLINK and Arm.

Key Developments:

In October 2025, GE Vernova has signed a supply agreement with Greenvolt Power to provide 42 wind turbines for the Ialomita wind project in Romania. Under the deal, GE Vernova will supply, install, and commission its 6.1MW turbine with a 158-metre rotor for the 252MW project. The contract was finalised in the third quarter of this year, with turbine deliveries scheduled to begin in 2026.

In August 2025, Intel Corporation announced an agreement with the Trump Administration under which the US government will make an 8.9 billion US dollar investment in Intel common stock. The government agrees to purchase 433.3 million primary shares of Intel common stock at a price of 20.47 dollars per share, equivalent to a 9.9 percent stake in the company.

In August 2025, Nvidia and AMD have agreed to pay the US government 15% of Chinese revenues as part of an unprecedented deal to secure export licences to China. The US had previously banned the sale of powerful chips used in areas like artificial intelligence (AI) to China under export controls usually related to national security concerns. Under the agreement, Nvidia will pay 15% of its revenues from H20 chip sales in China to the US government.

Components Covered:

Hardware
Software
Integration & Support Services

Deployment Modes Covered:

On-Premises
Cloud
Hybrid

Applications Covered:

Predictive Maintenance & Failure Forecasting
Visual Quality Inspection & Defect Detection
Real-Time Process Optimization
Autonomous Robotics & Machine Control
Industrial Asset Tracking & Condition Monitoring
Workplace Safety & Regulatory Compliance
Intelligent Supply Chain & Inventory Analytics

End Users Covered:

Automotive
Electronics & Semiconductors
Food & Beverage
Pharmaceuticals
Aerospace & Defense
Energy & Utilities
Chemicals
Metals & Mining
Logistics & Warehousing

Regions Covered:

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- Italy
- France
- Spain
- Rest of Europe
Asia Pacific
- Japan
- China
- India
- Australia
- New Zealand
- South Korea
- Rest of Asia Pacific
South America
- Argentina
- Brazil
- Chile
- Rest of South America
Middle East & Africa
- Saudi Arabia
- UAE
- Qatar
- South Africa
- Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
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

1 Executive Summary

2 Preface

2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions

3 Market Trend Analysis

3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Application Analysis
3.7 End User Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19

4 Porters Five Force Analysis

4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry

5 Global Edge AI in Industrial Environments Market, By Component

5.1 Introduction
5.2 Hardware
5.3 Software
5.4 Integration & Support Services

6 Global Edge AI in Industrial Environments Market, By Deployment Mode

6.1 Introduction
6.2 On-Premises
6.3 Cloud
6.4 Hybrid

7 Global Edge AI in Industrial Environments Market, By Application

7.1 Introduction
7.2 Predictive Maintenance & Failure Forecasting
7.3 Visual Quality Inspection & Defect Detection
7.4 Real-Time Process Optimization
7.5 Autonomous Robotics & Machine Control
7.6 Industrial Asset Tracking & Condition Monitoring
7.7 Workplace Safety & Regulatory Compliance
7.8 Intelligent Supply Chain & Inventory Analytics

8 Global Edge AI in Industrial Environments Market, By End User

8.1 Introduction
8.2 Automotive
8.3 Electronics & Semiconductors
8.4 Food & Beverage
8.5 Pharmaceuticals
8.6 Aerospace & Defense
8.7 Energy & Utilities
8.8 Chemicals
8.9 Metals & Mining
8.10 Logistics & Warehousing

9 Global Edge AI in Industrial Environments Market, By Geography

9.1 Introduction
9.2 North America
- 9.2.1 US
- 9.2.2 Canada
- 9.2.3 Mexico
9.3 Europe
- 9.3.1 Germany
- 9.3.2 UK
- 9.3.3 Italy
- 9.3.4 France
- 9.3.5 Spain
- 9.3.6 Rest of Europe
9.4 Asia Pacific
- 9.4.1 Japan
- 9.4.2 China
- 9.4.3 India
- 9.4.4 Australia
- 9.4.5 New Zealand
- 9.4.6 South Korea
- 9.4.7 Rest of Asia Pacific
9.5 South America
- 9.5.1 Argentina
- 9.5.2 Brazil
- 9.5.3 Chile
- 9.5.4 Rest of South America
9.6 Middle East & Africa
- 9.6.1 Saudi Arabia
- 9.6.2 UAE
- 9.6.3 Qatar
- 9.6.4 South Africa
- 9.6.5 Rest of Middle East & Africa

10 Key Developments

10.1 Agreements, Partnerships, Collaborations and Joint Ventures
10.2 Acquisitions & Mergers
10.3 New Product Launch
10.4 Expansions
10.5 Other Key Strategies

11 Company Profiling

11.1 NVIDIA
11.2 Intel Corporation
11.3 GE Vernova
11.4 Siemens
11.5 Rockwell Automation
11.6 ABB
11.7 IBM
11.8 Advantech
11.9 Bosch
11.10 ClearBlade
11.11 CanaryBit
11.12 Emerson
11.13 MicroAI
11.14 ADLINK
11.15 Arm

簡介目錄圖表

List of Tables

Table 1 Global Edge AI in Industrial Environments Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global Edge AI in Industrial Environments Market Outlook, By Component (2024-2032) ($MN)
Table 3 Global Edge AI in Industrial Environments Market Outlook, By Hardware (2024-2032) ($MN)
Table 4 Global Edge AI in Industrial Environments Market Outlook, By Software (2024-2032) ($MN)
Table 5 Global Edge AI in Industrial Environments Market Outlook, By Integration & Support Services (2024-2032) ($MN)
Table 6 Global Edge AI in Industrial Environments Market Outlook, By Deployment Mode (2024-2032) ($MN)
Table 7 Global Edge AI in Industrial Environments Market Outlook, By On-Premises (2024-2032) ($MN)
Table 8 Global Edge AI in Industrial Environments Market Outlook, By Cloud (2024-2032) ($MN)
Table 9 Global Edge AI in Industrial Environments Market Outlook, By Hybrid (2024-2032) ($MN)
Table 10 Global Edge AI in Industrial Environments Market Outlook, By Application (2024-2032) ($MN)
Table 11 Global Edge AI in Industrial Environments Market Outlook, By Predictive Maintenance & Failure Forecasting (2024-2032) ($MN)
Table 12 Global Edge AI in Industrial Environments Market Outlook, By Visual Quality Inspection & Defect Detection (2024-2032) ($MN)
Table 13 Global Edge AI in Industrial Environments Market Outlook, By Real-Time Process Optimization (2024-2032) ($MN)
Table 14 Global Edge AI in Industrial Environments Market Outlook, By Autonomous Robotics & Machine Control (2024-2032) ($MN)
Table 15 Global Edge AI in Industrial Environments Market Outlook, By Industrial Asset Tracking & Condition Monitoring (2024-2032) ($MN)
Table 16 Global Edge AI in Industrial Environments Market Outlook, By Workplace Safety & Regulatory Compliance (2024-2032) ($MN)
Table 17 Global Edge AI in Industrial Environments Market Outlook, By Intelligent Supply Chain & Inventory Analytics (2024-2032) ($MN)
Table 18 Global Edge AI in Industrial Environments Market Outlook, By End User (2024-2032) ($MN)
Table 19 Global Edge AI in Industrial Environments Market Outlook, By Automotive (2024-2032) ($MN)
Table 20 Global Edge AI in Industrial Environments Market Outlook, By Electronics & Semiconductors (2024-2032) ($MN)
Table 21 Global Edge AI in Industrial Environments Market Outlook, By Food & Beverage (2024-2032) ($MN)
Table 22 Global Edge AI in Industrial Environments Market Outlook, By Pharmaceuticals (2024-2032) ($MN)
Table 23 Global Edge AI in Industrial Environments Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
Table 24 Global Edge AI in Industrial Environments Market Outlook, By Energy & Utilities (2024-2032) ($MN)
Table 25 Global Edge AI in Industrial Environments Market Outlook, By Chemicals (2024-2032) ($MN)
Table 26 Global Edge AI in Industrial Environments Market Outlook, By Metals & Mining (2024-2032) ($MN)
Table 27 Global Edge AI in Industrial Environments Market Outlook, By Logistics & Warehousing (2024-2032) ($MN)