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2024141

2034 年邊緣運算人工智慧市場預測：按組件、部署模式、設備類型、連接方式、應用、最終用戶和地區分類的全球分析

AI in Edge Computing Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Deployment, Device Type, Connectivity, 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，全球邊緣運算人工智慧市場規模將達到 168 億美元，並在預測期內以 19.2% 的複合年成長率成長，到 2034 年將達到 686 億美元。

AI邊緣運算是指將機器學習模型、神經網路推理引擎和AI驅動的分析功能直接部署到位於資料來源（例如工業設備、自動駕駛汽車、智慧攝影機、零售POS系統和行動裝置）附近或附近的邊緣運算設備、閘道器和伺服器上。這使得AI推理無需往返雲端即可實現即時運行，即使連接中斷也能確保持續運行，並透過在定義的地理或組織邊界內本地處理敏感資訊來保護資料隱私。

工業IoT對人工智慧推理的需求

工業IoT的部署需要機器控制安全系統具備亞毫秒級的AI推理能力，實現即時缺陷檢測和設備自主運行，而雲端連接的延遲與即時工業自動化的時效性要求根本無法相容，因此必須採用邊緣AI。實施AI驅動的品質檢測、預測性維護和自主物料輸送系統的製造企業，是邊緣AI基礎設施的大量採購客戶，其硬體和軟體收入也因此穩定成長。

邊緣硬體碎片化

邊緣人工智慧部署環境的硬體架構極度碎片化，涵蓋ARM、x86、RISC-V以及專用人工智慧加速器晶片系列，導致人工智慧模型必須針對多個不相容的硬體目標進行最佳化，從而增加了軟體開發的複雜性，並延長了邊緣人工智慧應用部署的成本和時間。由於缺乏通用的邊緣人工智慧執行時間標準，人工智慧模型開發人員必須為各種邊緣硬體平台維護並行的最佳化流程，以應對不同的應用領域。

用於自動駕駛汽車的邊緣人工智慧

自動駕駛汽車車載人工智慧運算平台代表了邊緣人工智慧硬體和軟體市場中最有價值的細分領域。每輛自動駕駛汽車都需要先進的多模態感測器融合、即時目標偵測、路徑規劃和用於車輛控制的人工智慧推理系統，所有這些都必須在強大的邊緣運算硬體上同時運作。這些硬體必須在嚴格的、對安全性至關重要的延遲約束下處理海量感測器數據，而依賴雲端的人工智慧架構無法滿足這些要求。

5G延遲降低競賽

對於特定應用，部署超低延遲的 5G 網路切片，實現與邊緣環境響應時間相當的雲端 AI 處理，為部署專用邊緣 AI 硬體提供了技術替代方案。這有望降低互聯環境中邊緣硬體的整體投資需求，因為在這些環境中，5G 專用網路基礎設施能夠提供足夠的 AI 卸載延遲，從而消除設備級 AI 處理的複雜性以及先進的車載邊緣 AI 系統的成本。

新冠疫情的影響：

新冠疫情導致現場技術人員難以保障，但也凸顯了邊緣人工智慧系統在營運韌性方面的優勢。即使在人員受限的情況下，邊緣人工智慧系統也能在不依賴雲端連線或遠端管理的情況下，維持在地化的智慧運作。此外，供應鏈中斷也促使人們對邊緣人工智慧產生更多興趣，尤其是在供應鏈視覺化和倉庫自動化方面，因為這些系統無需依賴集中式資料中心基礎架構即可運作。後疫情時代工業自動化的加速發展，進一步推動了對邊緣人工智慧的強勁需求。

在預測期內，服務業預計將佔據最大的市場佔有率。

預計在預測期內，服務領域將佔據最大的市場佔有率。這主要是由於市場對企業邊緣人工智慧系統設計、部署、模型最佳化以及持續託管的邊緣基礎設施服務有著極高的需求。複雜的工業和汽車邊緣人工智慧的實施需要專業的硬體整合、無線連接配置以及跨地理位置分散的設備叢集的持續模型更新管理。這些需求超出了企業內部IT團隊在邊緣部署的專業知識範圍。

在預測期內，設備邊緣細分市場預計將呈現最高的複合年成長率。

在預測期內，設備端邊緣運算領域預計將呈現最高的成長率。這主要得益於人工智慧加速晶片的快速小型化，使得在資源受限的終端設備（例如攝影機、感測器、穿戴式裝置和嵌入式控制器）上進行高階神經網路推理成為可能。這些設備無需依賴外部處理硬體即可在本地運行有效的電腦視覺和預測模型，從而顯著擴展了可部署嵌入式人工智慧邊緣運算的設備數量。

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。這是因為英偉達、英特爾和高通等領先的人工智慧晶片和軟體平台開發商都位於美國，它們創造了全球大部分邊緣人工智慧技術收入。此外，北美在工業自動化、自動駕駛汽車和智慧基礎設施領域也表現出色，這使得該地區成為全球邊緣人工智慧投資最集中的地區，並擁有最先進的商業邊緣人工智慧部署專案。

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。這主要歸功於中國、日本、韓國和印度的大規模智慧製造、智慧城市和5G基礎設施部署項目，這些項目催生了對邊緣人工智慧系統的廣泛採購需求；中國和韓國對邊緣人工智慧晶片研發的國內投資不斷增加；以及工業IoT在亞洲製造業的快速普及，這需要本地化的人工智慧推理能力。

免費客製化服務：

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
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 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
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球邊緣運算人工智慧市場：按組件分類

  • 硬體
  • 軟體
  • 服務

第6章：全球邊緣運算人工智慧市場：以部署方式分類

  • 設備邊緣
  • 本地部署邊緣
  • 雲邊緣

第7章：全球邊緣運算人工智慧市場：按設備類型分類

  • 閘道
  • 感應器
  • 邊緣伺服器
  • 智慧顯示器和自助服務終端
  • 自主無人機

第8章：全球邊緣運算人工智慧市場：連結性

  • 細胞
  • Wi-Fi
  • Bluetooth／BLE
  • LPWAN
  • 乙太網路

第9章：全球邊緣運算人工智慧市場：按應用領域分類

  • 自動駕駛汽車
  • 智慧城市
  • 工業IoT
  • 健康監測

第10章：全球邊緣運算人工智慧市場：按最終用戶分類

  • 製造業
  • 衛生保健
  • 車
  • 零售

第11章：全球邊緣運算人工智慧市場：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

  • Intel Corporation
  • NVIDIA Corporation
  • Qualcomm Technologies Inc.
  • IBM Corporation
  • Microsoft Corporation
  • Amazon Web Services Inc.
  • Cisco Systems Inc.
  • Hewlett Packard Enterprise
  • Dell Technologies Inc.
  • Google LLC
  • Siemens AG
  • Samsung Electronics
  • Huawei Technologies
  • Advantech Co. Ltd.
  • Schneider Electric SE
  • FogHorn Systems
  • Edge Impulse Inc.
Product Code: SMRC35352

According to Stratistics MRC, the Global AI in Edge Computing Market is accounted for $16.8 billion in 2026 and is expected to reach $68.6 billion by 2034 growing at a CAGR of 19.2% during the forecast period. AI in edge computing refers to the deployment of machine learning models, neural network inference engines, and AI-powered analytics directly on edge computing devices, gateways, and servers located at or near data sources including industrial equipment, autonomous vehicles, smart cameras, retail point-of-sale systems, and mobile devices, enabling real-time AI inference without cloud round-trip latency, continuous operation during connectivity interruptions, and data privacy preservation through local processing of sensitive information within defined geographic or organizational boundaries.

Market Dynamics:

Driver:

Industrial IoT AI Inference Demand

Industrial IoT deployments requiring sub-millisecond AI inference for machine control safety systems, real-time defect detection, and autonomous equipment operation are driving mandatory edge AI adoption as cloud connectivity latency is fundamentally incompatible with real-time industrial automation timing requirements. Manufacturing companies deploying AI-powered quality inspection, predictive maintenance, and autonomous material handling systems represent high-volume edge AI infrastructure procurement buyers generating consistent hardware and software revenue growth.

Restraint:

Edge Hardware Fragmentation

Extreme hardware architecture fragmentation across edge AI deployment environments spanning ARM, x86, RISC-V, and specialized AI accelerator chip families requires AI model optimization for multiple incompatible hardware targets, creating software development complexity that increases edge AI application deployment costs and timelines. Absence of universal edge AI runtime standards forces AI model developers to maintain parallel optimization pipelines for different edge hardware platforms serving different application verticals.

Opportunity:

Autonomous Vehicle Edge AI

Autonomous vehicle onboard AI compute platforms represent the highest-value edge AI hardware and software market segment as each autonomous vehicle requires sophisticated multi-modal sensor fusion, real-time object detection, path planning, and vehicle control AI inference systems executing simultaneously on powerful edge computing hardware that must process enormous sensor data volumes within strict safety-critical latency constraints incompatible with cloud-dependent AI architectures.

Threat:

5G Latency Reduction Competition

Ultra-low latency 5G network slice deployments enabling cloud AI processing at edge-competitive response times for specific applications create a technological alternative to dedicated edge AI hardware deployment that may reduce total edge hardware investment requirements in connected environments where 5G private network infrastructure provides adequate AI offload latency performance without the device-level AI processing complexity and cost of sophisticated onboard edge AI systems.

Covid-19 Impact:

COVID-19 reduced on-site technical personnel availability that demonstrated the operational resilience advantage of edge AI systems maintaining local intelligent operation without cloud connectivity or remote management dependency during personnel access restrictions. Supply chain disruptions also created interest in edge AI for supply chain visibility and warehouse automation that could operate independently of centralized data center infrastructure. Post-pandemic industrial automation acceleration sustains strong edge AI deployment demand.

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

The services segment is expected to account for the largest market share during the forecast period, due to substantial enterprise demand for edge AI system design, deployment, model optimization, and ongoing managed edge infrastructure services that accompany complex industrial and automotive edge AI implementations requiring specialized hardware integration, wireless connectivity configuration, and continuous model update management across geographically distributed device fleets that exceed internal IT team edge deployment expertise.

The on-device edge segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the on-device edge segment is predicted to witness the highest growth rate, driven by rapid AI accelerator chip miniaturization enabling sophisticated neural network inference on resource-constrained endpoint devices including cameras, sensors, wearables, and embedded controllers that can now execute meaningful computer vision and predictive models locally without external processing hardware dependency, dramatically expanding the addressable device population for endpoint-embedded AI edge computing deployments.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the United States hosting leading edge AI chip and software platform developers including NVIDIA, Intel, and Qualcomm generating the majority of global edge AI technology revenue, combined with strong industrial automation, autonomous vehicle, and smart infrastructure sectors representing the world's highest per-region edge AI investment concentrations and most advanced commercial edge AI deployment programs.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to large-scale smart manufacturing, smart city, and 5G infrastructure deployment programs across China, Japan, South Korea, and India creating extensive edge AI system procurement demand, growing domestic edge AI chip development investment in China and South Korea, and rapidly expanding industrial IoT adoption across Asian manufacturing sectors requiring local AI inference capability.

Key players in the market

Some of the key players in AI in Edge Computing Market include Intel Corporation, NVIDIA Corporation, Qualcomm Technologies Inc., IBM Corporation, Microsoft Corporation, Amazon Web Services Inc., Cisco Systems Inc., Hewlett Packard Enterprise, Dell Technologies Inc., Google LLC, Siemens AG, Samsung Electronics, Huawei Technologies, Advantech Co. Ltd., Schneider Electric SE, FogHorn Systems, and Edge Impulse Inc..

Key Developments:

In February 2026, Intel Corporation introduced Edge AI Suite 2.0 providing enterprise customers unified model optimization and deployment management across diverse Intel-powered edge hardware platforms through a single software framework.

In January 2026, FogHorn Systems secured a major industrial edge AI deployment with a global energy company implementing real-time AI analytics across thousands of distributed oil and gas production asset monitoring endpoints.

In October 2025, Edge Impulse Inc. launched a new enterprise TinyML platform enabling companies to deploy optimized AI models on ultra-low-power microcontroller-class edge devices for industrial sensor monitoring and predictive maintenance applications.

Components Covered:

  • Hardware
  • Software
  • Services

Deployments Covered:

  • On-Device Edge
  • On-Premise Edge
  • Cloud Edge

Device Types Covered:

  • Gateways
  • Sensors
  • Edge Servers
  • Smart Displays & Kiosks
  • Autonomous Drones

Connectivities Covered:

  • Cellular
  • Wi-Fi
  • Bluetooth/BLE
  • LPWAN
  • Ethernet

Applications Covered:

  • Autonomous Vehicles
  • Smart Cities
  • Industrial IoT
  • Healthcare Monitoring

End Users Covered:

  • Manufacturing
  • Healthcare
  • Automotive
  • Retail

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 AI in Edge Computing Market, By Component

  • 5.1 Hardware
  • 5.2 Software
  • 5.3 Services

6 Global AI in Edge Computing Market, By Deployment

  • 6.1 On-Device Edge
  • 6.2 On-Premise Edge
  • 6.3 Cloud Edge

7 Global AI in Edge Computing Market, By Device Type

  • 7.1 Gateways
  • 7.2 Sensors
  • 7.3 Edge Servers
  • 7.4 Smart Displays & Kiosks
  • 7.5 Autonomous Drones

8 Global AI in Edge Computing Market, By Connectivity

  • 8.1 Cellular
  • 8.2 Wi-Fi
  • 8.3 Bluetooth/BLE
  • 8.4 LPWAN
  • 8.5 Ethernet

9 Global AI in Edge Computing Market, By Application

  • 9.1 Autonomous Vehicles
  • 9.2 Smart Cities
  • 9.3 Industrial IoT
  • 9.4 Healthcare Monitoring

10 Global AI in Edge Computing Market, By End User

  • 10.1 Manufacturing
  • 10.2 Healthcare
  • 10.3 Automotive
  • 10.4 Retail

11 Global AI in Edge Computing 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 Intel Corporation
  • 14.2 NVIDIA Corporation
  • 14.3 Qualcomm Technologies Inc.
  • 14.4 IBM Corporation
  • 14.5 Microsoft Corporation
  • 14.6 Amazon Web Services Inc.
  • 14.7 Cisco Systems Inc.
  • 14.8 Hewlett Packard Enterprise
  • 14.9 Dell Technologies Inc.
  • 14.10 Google LLC
  • 14.11 Siemens AG
  • 14.12 Samsung Electronics
  • 14.13 Huawei Technologies
  • 14.14 Advantech Co. Ltd.
  • 14.15 Schneider Electric SE
  • 14.16 FogHorn Systems
  • 14.17 Edge Impulse Inc.

List of Tables

  • Table 1 Global AI in Edge Computing Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Edge Computing Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Edge Computing Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI in Edge Computing Market Outlook, By Software (2023-2034) ($MN)
  • Table 5 Global AI in Edge Computing Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global AI in Edge Computing Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 7 Global AI in Edge Computing Market Outlook, By On-Device Edge (2023-2034) ($MN)
  • Table 8 Global AI in Edge Computing Market Outlook, By On-Premise Edge (2023-2034) ($MN)
  • Table 9 Global AI in Edge Computing Market Outlook, By Cloud Edge (2023-2034) ($MN)
  • Table 10 Global AI in Edge Computing Market Outlook, By Device Type (2023-2034) ($MN)
  • Table 11 Global AI in Edge Computing Market Outlook, By Gateways (2023-2034) ($MN)
  • Table 12 Global AI in Edge Computing Market Outlook, By Sensors (2023-2034) ($MN)
  • Table 13 Global AI in Edge Computing Market Outlook, By Edge Servers (2023-2034) ($MN)
  • Table 14 Global AI in Edge Computing Market Outlook, By Smart Displays & Kiosks (2023-2034) ($MN)
  • Table 15 Global AI in Edge Computing Market Outlook, By Autonomous Drones (2023-2034) ($MN)
  • Table 16 Global AI in Edge Computing Market Outlook, By Connectivity (2023-2034) ($MN)
  • Table 17 Global AI in Edge Computing Market Outlook, By Cellular (2023-2034) ($MN)
  • Table 18 Global AI in Edge Computing Market Outlook, By Wi-Fi (2023-2034) ($MN)
  • Table 19 Global AI in Edge Computing Market Outlook, By Bluetooth/BLE (2023-2034) ($MN)
  • Table 20 Global AI in Edge Computing Market Outlook, By LPWAN (2023-2034) ($MN)
  • Table 21 Global AI in Edge Computing Market Outlook, By Ethernet (2023-2034) ($MN)
  • Table 22 Global AI in Edge Computing Market Outlook, By Application (2023-2034) ($MN)
  • Table 23 Global AI in Edge Computing Market Outlook, By Autonomous Vehicles (2023-2034) ($MN)
  • Table 24 Global AI in Edge Computing Market Outlook, By Smart Cities (2023-2034) ($MN)
  • Table 25 Global AI in Edge Computing Market Outlook, By Industrial IoT (2023-2034) ($MN)
  • Table 26 Global AI in Edge Computing Market Outlook, By Healthcare Monitoring (2023-2034) ($MN)
  • Table 27 Global AI in Edge Computing Market Outlook, By End User (2023-2034) ($MN)
  • Table 28 Global AI in Edge Computing Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 29 Global AI in Edge Computing Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 30 Global AI in Edge Computing Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 31 Global AI in Edge Computing Market Outlook, By Retail (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.