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市場調查報告書
商品編碼
2124393

物聯網中的人工智慧：市場佔有率分析、行業趨勢和統計數據、成長預測（2026-2031 年）

AI In IoT - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 150 Pages | 商品交期: 2-3個工作天內

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簡介目錄

據 Mordor Intelligence 稱，2025 年物聯網人工智慧市場價值 607.1 億美元，預計到 2031 年將達到 1994.6 億美元，而 2026 年為 740.4 億美元，預測期（2026-2031 年）複合年成長率為 21.95%。

物聯網市場中的人工智慧-IMG1

本報告按組件（軟體和服務）、部署模式（本地部署和雲端部署）、技術（機器學習和深度學習、自然語言處理等）、物聯網連接方式（蜂窩網路、低功耗廣域網路等）、最終用戶行業（製造業、能源和公共產業等）以及地區進行分類。

全球物聯網人工智慧市場趨勢與洞察

連網設備產生的巨量資料量日益成長

預計到2025年，物聯網終端將產生約80Zetta位元組的數據，其中工業感測器將佔73.1Zetta位元組。僅靠雲端處理如此龐大的資料量會對頻寬預算造成巨大壓力，因此製造商正在採用邊緣人工智慧技術，在現場壓縮和分析資料流，從而降低網路成本，並實現亞秒級的品管響應目標。領先採用者報告稱，透過將關鍵分析功能遷移到整合到生產線中的智慧控制器，生產效率實現了兩位數的成長。這一趨勢也正在擴展到物流樞紐和公共產業領域，在這些領域，現場推理處理避免了回程傳輸原始感測器數據所帶來的高昂成本。

對即時人工智慧分析的需求日益成長，以實現物聯網數據貨幣化

零售銀行、電力交易商和城市交通運輸業者指出，每一秒的延遲都意味著資訊遺失。如今，部署的重點已轉向將即時感測器測量數據轉化為即時的價格最佳化、通路調整或安全警報，從而直接提高收入或減少罰款。閘道器內建的生成演算法能夠在幾分鐘內（而非幾天）指導倉庫員工、引導機器人並微調庫存。這種對時效性強的商業化關注，正加速已開發國家邊緣人工智慧平台從試點階段向生產階段的過渡。

異質物聯網節點中的資料安全和隱私漏洞

設備功能的多樣性造成了漏洞，這些漏洞成為攻擊者可利用的「薄弱環節」。 ScienceDirect 的評測指出，傳統感測器通常缺乏安全啟動和基於硬體的信任根，這使得人工智慧模型面臨被篡改的風險。歐盟的《人工智慧法》強制要求進行風險審計和加密升級，導致合規性延遲和預算超支。儘管供應商正在透過零信任框架和設備端異常檢測來應對這一問題，但對數百萬台設備在其整個生命週期內的所有資產類別進行修補仍然是一項艱鉅的任務。

細分市場分析

預計到2025年，軟體收入將佔總收入的67.88%，其中演算法、中介軟體和分析引擎將成為物聯網人工智慧市場價值創造的主要驅動力。隨著企業將模型調優、設備上線和生命週期監控等工作外包，服務領域正以23.6%的複合年成長率快速成長。這種快速成長推動了以超大規模平台為中心的生態系統的整合，這些平台整合了串流分析、邊緣編配和零信任安全等功能。隨著企業優先考慮有保障的託管正常運作運作，物聯網人工智慧服務的市場規模預計將迅速擴大。同時，專用推理引擎的授權模式正從永久許可轉向基於設備數量的訂閱套餐，這簡化了預算管理，但也使客戶受限於供應商的藍圖。

第二代應用程式管理套件可根據概念漂移自動進行模型重訓練，而裝置管理入口網站則提供差異化更新，以最大限度地減少停機時間。安全層透過自動化威脅搜尋功能進一步增強，該功能可關聯整個設備群中的異常情況。這些改進促進了硬體無關架構的構建，使客戶能夠混合使用不同品牌的閘道器，而無需重寫其分析管道。商業開放原始碼核心與專有最佳化程式庫的結合，在透明度和效能之間實現了平衡，同時滿足了嚴格的審計要求。

到 2025 年，隨著製造商和醫院需要保護敏感資料並確保確定性延遲，本地部署將佔據 70.65% 的市場佔有率。然而，雲端工作負載正以 23.9% 的複合年成長率 (CAGR) 更快成長，這反映出一種向混合模式的轉變：匿名化的摘要信息被發送到雲叢集，透過本地推理進行大規模訓練任務。物聯網市場的 AI 受益於企業將個人識別資訊保留在受監管的設施中，同時利用超大規模區域的彈性圖形處理能力進行季節性重新訓練。這種雙層拓樸結構既可以減少本地伺服器的資本投入，又能滿足合規性目標。

邊緣閘道器擴大容器化的微服務，這些微服務可以安全地連接到公共雲端進行編配。這種配置使企業能夠在夜間更新其願景模型，而無需關閉生產線。金融服務公司正在採用類似的模式，將單一交易的詳細資訊儲存在本地，同時利用雲端的大規模語言模型來分析匯總趨勢。雲端供應商正在透過「私有鏈路」服務加速這項轉型，這些服務提供基於硬體的安全運算環境，無需經過公用網際網路。

區域分析

北美地區在強勁的創業投資資金、廣泛的5G部署和有利的智慧財產權體系的支持下，預計到2025年將佔全球收入的41.60%。光是AWS一家公司就已在2025年前累計超過1000億美元用於新的人工智慧基礎設施建設，以確保客戶能夠無縫存取高效能運算服務。聯邦政府旨在快速推進智慧港口和國防項目的計劃也進一步刺激了市場需求。然而，與新興市場相比，薪資上漲和人才短缺抑制了該地區的成長速度。

亞太地區預計到2031年將維持23.0%的年複合成長率（CAGR），位居全球之冠。中國廠商透過垂直整合從裝置半導體到雲端控制面板的各個環節，降低成本並加快產品改善週期。日本和韓國則將世界一流的機器人技術與覆蓋全國的高密度5G網路結合，實現即時工業視覺技術的商業化。從印度到印尼，政府資助的智慧城市計畫正在扶持新創企業，協助其建構交通監控、廢棄物分類和洪水預警系統。儘管平均售價仍然較低，但亞太地區物聯網地區的人工智慧市場規模在銷售量方面已超過其他地區。

在歐洲，隨著《人工智慧法案》明確了相關義務並加強了合規程序，同時也為資本預算的落實提供了保障，人工智慧領域正穩步推進。德國在汽車生產線的預測性維護方面處於主導，而荷蘭則正在試點將邊緣人工智慧應用於運河水位控制。有關資料主權的法規迫使企業部署本地和邊緣環境，從而刺激了對用於敏感運算的處理器的需求。在中東和非洲，油田監測和智慧灌溉領域已初見成效，衛星回程傳輸克服了地面網路覆蓋範圍有限的挑戰。由於當地技術人才仍在培養中，部署速度較為緩慢。

其他好處

  • 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 市場狀況

  • 市場概覽
  • 市場促進因素
    • 連網設備產生的巨量資料量日益成長
    • 對即時人工智慧分析的需求日益成長，以實現物聯網數據貨幣化
    • 邊緣AI晶片組可降低延遲和功耗
    • 5G與NTN的整合使得在偏遠地區部署AIoT成為可能。
    • 與永續發展相關的法規正在透過人工智慧的應用來促進能源利用的最佳化。
    • 推廣預測性維護以提高供應鏈韌性
  • 市場限制因素
    • 異質物聯網節點中的資料安全與隱私漏洞
    • 缺乏具備人工智慧物聯網（AIoT）專業知識的人員，且實施成本高。
    • 由於標準碎片化，模型可移植性受到限制。
    • 後量子時代對設備密碼學的迫在眉睫的威脅
  • 價值鏈分析
  • 監理情勢
  • 技術展望
  • 波特五力分析
  • 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
      • 應用程式管理
      • 連線管理
      • 設備管理
      • 資料管理
      • 網路頻寬管理
      • 即時串流分析
      • 遠端監控
      • 安全
      • 邊緣解決方案
    • 服務
      • 託管服務
      • 專業服務
  • 部署模式
    • 現場
    • 雲
  • 透過技術
    • 機器學習和深度學習
    • 自然語言處理
    • 電腦視覺
    • 情境感知計算
  • 物聯網連接方式
    • 蜂窩網路（2G-5G）
    • LPWAN（LoRa、NB-IoT、Sigfox）
    • 衛星/NTN
    • 短距離（Wi-Fi、BLE、Zigbee）
  • 按最終用戶行業分類
    • 製造業
    • 能源與公共產業
    • 衛生保健
    • BFSI
    • 資訊科技/通訊
    • 交通運輸與出行
    • 政府
    • 零售與電子商務
    • 農業
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 義大利
      • 西班牙
      • 俄羅斯
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 印度
      • 韓國
      • ASEAN
      • 澳洲和紐西蘭
      • 其他亞太國家
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美國家
    • 中東和非洲
      • 中東
        • 沙烏地阿拉伯
        • 阿拉伯聯合大公國
        • 土耳其
        • 其他中東國家
      • 非洲
        • 南非
        • 奈及利亞
        • 其他非洲地區

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • Amazon Web Services
    • Microsoft
    • IBM
    • Google
    • Oracle
    • Cisco Systems
    • NVIDIA
    • Siemens
    • Bosch.IO
    • ARM
    • Qualcomm
    • Intel
    • Huawei
    • Schneider Electric
    • Honeywell
    • PTC
    • SAS Institute
    • General Electric
    • Hitachi
    • Salesforce

第7章 市場機會與未來展望

簡介目錄
Product Code: 70756

According to Mordor Intelligence, the AI in IoT market size was valued at USD 60.71 billion in 2025 and estimated to grow from USD 74.04 billion in 2026 to reach USD 199.46 billion by 2031, at a CAGR of 21.95% during the forecast period (2026-2031).

AI In IoT - Market - IMG1

This report is Segmented by Component (Software and Services), Deployment Mode (On-Premises and Cloud), Technology (Machine Learning and Deep Learning, Natural Language Processing, and More), Iot Connectivity Type (Cellular, LPWAN, and More), End-User Vertical (Manufacturing, Energy and Utilities, and More), and Geography.

Global AI In IoT Market Trends and Insights

Rising Big-Data Volumes Generated by Connected Devices

IoT end-points will create about 80 zettabytes of data in 2025, with industrial sensors contributing 73.1 zettabytes. Processing this torrent in the cloud alone strains bandwidth budgets, so manufacturers are adopting edge AI that compresses and analyses streams on-site, trimming network costs and meeting sub-second response targets for quality control. Early movers report double-digit productivity gains after shifting critical analytics to smart controllers embedded in production lines. The pattern is spreading across logistics hubs and utilities where local inference prevents expensive back-haul of raw sensor feeds.

Demand for Real-Time AI Analytics to Monetise IoT Data

Retail banks, power traders and city transit operators note that insight loses value with every second of delay. Deployments now focus on turning live sensor readings into instant price optimisation, routing changes or safety alerts that directly lift revenue or cut penalties. Generative algorithms embedded on gateways are guiding warehouse staff, adjusting robotic paths and fine-tuning inventory in minutes instead of days. The emphasis on time-sensitive monetisation is accelerating pilot-to-production cycles for edge AI platforms across developed economies.

Data-Security and Privacy Gaps Across Heterogeneous IoT Nodes

Diverse device capabilities leave weakest-link gaps that adversaries exploit. A ScienceDirect review shows legacy sensors often lack secure boot or hardware roots of trust, exposing AI models to tampering. The EU Artificial Intelligence Act compels risk audits and encryption upgrades, adding compliance delays and budget overruns. Vendors respond with zero-trust frameworks and on-device anomaly detection, yet lifecycle patching remains arduous for fleets exceeding millions of assets.

Other drivers and restraints analyzed in the detailed report include:

  1. Edge-AI Chipsets Lowering Latency and Energy Use
  2. 5G-NTN Convergence Unlocking Remote AIoT Deployments
  3. Scarcity of AIoT-Skilled Talent and High Integration Costs

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

Segment Analysis

Software commanded 67.88% revenue in 2025, confirming that algorithms, middleware, and analytics engines drive most value creation within the AI in IoT market. Services expand at a 23.6% CAGR because enterprises outsource model tuning, device onboarding, and lifecycle monitoring. This surge fosters ecosystem consolidation around hyperscale platforms that bundle streaming analytics, edge orchestration, and zero-trust security. The AI in IoT market size for services-linked offerings is projected to expand quickly as companies prioritise managed uptime commitments. Meanwhile, licensing models for specialised inference engines are shifting toward subscription bundles aligned with device counts rather than perpetual fees, smoothing budgets yet locking customers into vendor roadmaps.

Second-generation application-management suites now automate model retraining based on concept drift while device-management portals push differential updates that limit downtime. Security layers grow more sophisticated, adding automated threat hunting that cross-correlates anomalies across fleets. These trends encourage hardware-agnostic architectures so customers can mix gateway brands without rewriting analytics pipelines. Commercial open-source cores joined with proprietary optimisation libraries balance transparency with performance, meeting stringent audit requirements.

On-premises deployments held a 70.65% share in 2025 because manufacturers and hospitals safeguard sensitive data and guarantee deterministic latency. Still, cloud workloads scale faster at 23.9% CAGR, reflecting a swing toward hybrid patterns where local inference feeds anonymised summaries to cloud clusters for heavy training tasks. The AI in IoT market benefits when enterprises keep personally identifiable data inside regulated facilities, yet exploit elastic graphics processing in hyperscale regions for seasonal retraining. Such dual-tier topologies reduce capital expense on local servers while preserving compliance objectives.

Edge gateways increasingly host containerised microservices that tunnel securely to public clouds for orchestration. This set-up lets operators update vision models nightly without halting production lines. Financial services firms adopt similar blueprints, storing individual transaction details on-site but leveraging cloud-resident large-language models to analyse aggregate trends. Cloud providers encourage the shift with private-link offerings that avoid the public internet and supply hardware-rooted confidential-computing enclaves.

Complete Report Scope:

  • By Component
    • Software
      • Application Management
      • Connectivity Management
      • Device Management
      • Data Management
      • Network Bandwidth Management
      • Real-time Streaming Analytics
      • Remote Monitoring
      • Security
      • Edge Solution
    • Services
      • Managed Services
      • Professional Services
  • By Deployment Mode
    • On-premises
    • Cloud
  • By Technology
    • Machine Learning and Deep Learning
    • Natural Language Processing
    • Computer Vision
    • Context-Aware Computing
  • By IoT Connectivity Type
    • Cellular (2G-5G)
    • LPWAN (LoRa, NB-IoT, Sigfox)
    • Satellite / NTN
    • Short-range (Wi-Fi, BLE, Zigbee)
  • By End-user Vertical
    • Manufacturing
    • Energy and Utilities
    • Healthcare
    • BFSI
    • IT and Telecom
    • Transportation and Mobility
    • Government
    • Retail and e-Commerce
    • Agriculture
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • Europe
      • Germany
      • United Kingdom
      • France
      • Italy
      • Spain
      • Russia
      • Rest of Europe
    • Asia-Pacific
      • China
      • Japan
      • India
      • South Korea
      • ASEAN
      • Australia and New Zealand
      • Rest of Asia-Pacific
    • South America
      • Brazil
      • Argentina
      • Rest of South America
    • Middle East and Africa
      • Middle East
        • Saudi Arabia
        • UAE
        • Turkey
        • Rest of Middle East
      • Africa
        • South Africa
        • Nigeria
        • Rest of Africa

Geography Analysis

North America controlled 41.60% of revenue in 2025, powered by robust venture backing, extensive 5G roll-outs, and favourable intellectual property regimes. AWS alone budgeted more than USD 100 billion for new AI infrastructure in 2025, ensuring customers have low-friction access to high-performance compute. Federal programmes that fast-track smart port and defence projects further stimulate demand. Nevertheless, wage inflation and talent shortages temper the regional growth rate compared with emerging markets.

Asia-Pacific records the highest 23.0% CAGR through 2031. Chinese vendors integrate vertically from device silicon to cloud dashboards, compressing costs and accelerating iteration cycles. Japan and South Korea pair world-class robotics with dense nationwide 5G to commercialise real-time industrial vision. Government-funded smart-city schemes from India to Indonesia channel subsidies toward start-ups building traffic-monitoring, waste-sorting, and flood-alert systems. The AI in IoT market size for Asia-Pacific eclipses other regions in unit volumes, even though average selling prices remain lower.

Europe advances steadily as the Artificial Intelligence Act clarifies obligations and unlocks capital budgets despite adding compliance steps. Germany spearheads predictive maintenance in automotive lines, while the Netherlands pilots edge AI to control canal water levels. Data-sovereignty rules push enterprises to adopt on-premises and edge configurations, stimulating demand for confidential-computing processors. Middle East and Africa see early traction in oil-field surveillance and smart irrigation, where satellite backhaul circumvents sparse terrestrial coverage. Implementation pace is modest while local skills pipelines build up.

  1. Amazon Web Services
  2. Microsoft
  3. IBM
  4. Google
  5. Oracle
  6. Cisco Systems
  7. NVIDIA
  8. Siemens
  9. Bosch.IO
  10. ARM
  11. Qualcomm
  12. Intel
  13. Huawei
  14. Schneider Electric
  15. Honeywell
  16. PTC
  17. SAS Institute
  18. General Electric
  19. Hitachi
  20. Salesforce

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 Rising big-data volumes generated by connected devices
    • 4.2.2 Demand for real-time AI analytics to monetize IoT data
    • 4.2.3 Edge-AI chipsets lowering latency and energy use
    • 4.2.4 5G-NTN convergence unlocking remote AIoT deployments
    • 4.2.5 Sustainability-linked regulations driving AI-optimised energy use
    • 4.2.6 Predictive-maintenance push for supply-chain resilience
  • 4.3 Market Restraints
    • 4.3.1 Data-security and privacy gaps across heterogeneous IoT nodes
    • 4.3.2 Scarcity of AIoT-skilled talent and high integration costs
    • 4.3.3 Fragmented standards limiting model portability
    • 4.3.4 Looming post-quantum threats to device cryptography
  • 4.4 Value Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Bargaining Power of Suppliers
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Threat of New Entrants
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Intensity of 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
      • 5.1.1.1 Application Management
      • 5.1.1.2 Connectivity Management
      • 5.1.1.3 Device Management
      • 5.1.1.4 Data Management
      • 5.1.1.5 Network Bandwidth Management
      • 5.1.1.6 Real-time Streaming Analytics
      • 5.1.1.7 Remote Monitoring
      • 5.1.1.8 Security
      • 5.1.1.9 Edge Solution
    • 5.1.2 Services
      • 5.1.2.1 Managed Services
      • 5.1.2.2 Professional Services
  • 5.2 By Deployment Mode
    • 5.2.1 On-premises
    • 5.2.2 Cloud
  • 5.3 By Technology
    • 5.3.1 Machine Learning and Deep Learning
    • 5.3.2 Natural Language Processing
    • 5.3.3 Computer Vision
    • 5.3.4 Context-Aware Computing
  • 5.4 By IoT Connectivity Type
    • 5.4.1 Cellular (2G-5G)
    • 5.4.2 LPWAN (LoRa, NB-IoT, Sigfox)
    • 5.4.3 Satellite / NTN
    • 5.4.4 Short-range (Wi-Fi, BLE, Zigbee)
  • 5.5 By End-user Vertical
    • 5.5.1 Manufacturing
    • 5.5.2 Energy and Utilities
    • 5.5.3 Healthcare
    • 5.5.4 BFSI
    • 5.5.5 IT and Telecom
    • 5.5.6 Transportation and Mobility
    • 5.5.7 Government
    • 5.5.8 Retail and e-Commerce
    • 5.5.9 Agriculture
  • 5.6 By Geography
    • 5.6.1 North America
      • 5.6.1.1 United States
      • 5.6.1.2 Canada
      • 5.6.1.3 Mexico
    • 5.6.2 Europe
      • 5.6.2.1 Germany
      • 5.6.2.2 United Kingdom
      • 5.6.2.3 France
      • 5.6.2.4 Italy
      • 5.6.2.5 Spain
      • 5.6.2.6 Russia
      • 5.6.2.7 Rest of Europe
    • 5.6.3 Asia-Pacific
      • 5.6.3.1 China
      • 5.6.3.2 Japan
      • 5.6.3.3 India
      • 5.6.3.4 South Korea
      • 5.6.3.5 ASEAN
      • 5.6.3.6 Australia and New Zealand
      • 5.6.3.7 Rest of Asia-Pacific
    • 5.6.4 South America
      • 5.6.4.1 Brazil
      • 5.6.4.2 Argentina
      • 5.6.4.3 Rest of South America
    • 5.6.5 Middle East and Africa
      • 5.6.5.1 Middle East
        • 5.6.5.1.1 Saudi Arabia
        • 5.6.5.1.2 UAE
        • 5.6.5.1.3 Turkey
        • 5.6.5.1.4 Rest of Middle East
      • 5.6.5.2 Africa
        • 5.6.5.2.1 South Africa
        • 5.6.5.2.2 Nigeria
        • 5.6.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
    • 6.4.1 Amazon Web Services
    • 6.4.2 Microsoft
    • 6.4.3 IBM
    • 6.4.4 Google
    • 6.4.5 Oracle
    • 6.4.6 Cisco Systems
    • 6.4.7 NVIDIA
    • 6.4.8 Siemens
    • 6.4.9 Bosch.IO
    • 6.4.10 ARM
    • 6.4.11 Qualcomm
    • 6.4.12 Intel
    • 6.4.13 Huawei
    • 6.4.14 Schneider Electric
    • 6.4.15 Honeywell
    • 6.4.16 PTC
    • 6.4.17 SAS Institute
    • 6.4.18 General Electric
    • 6.4.19 Hitachi
    • 6.4.20 Salesforce

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-Need Assessment