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市場調查報告書
商品編碼
2069268

人工智慧驅動的車輛管理市場預測至2034年：按組件、部署模式、車輛類型、應用、技術、最終用戶和地區分類的全球分析

AI-Powered Fleet Management Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Deployment Mode, Fleet Type, Application, Technology, 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，全球人工智慧驅動的車輛管理市場預計將在 2026 年達到 58 億美元，到 2034 年達到 294 億美元，在預測期內以 22.6% 的複合年成長率成長。

人工智慧驅動的車輛管理是一種先進的方法，它利用人工智慧技術分析車輛、駕駛員、路線和營運的即時和歷史數據，從而管理車隊。這使企業能夠最佳化路線規劃、提高燃油效率、增強駕駛員安全、預測維護需求、降低營運成本並提高車輛整體生產力。透過自動化決策和可執行的洞察，人工智慧驅動的車輛管理能夠實現更有效率、更可靠、更數據驅動的運輸和物流營運。

物流日益複雜化以及對即時營運情報的需求不斷成長。

在電子商務履約不斷提升、燃油價格波動以及監管合規要求日益嚴格的推動下，車輛營運商正被迫從基本的GPS追蹤轉向人工智慧驅動的管理平台，以獲得可操作的預測性洞察。人工智慧驅動的車輛管理系統能夠持續處理車輛感測器數據、交通資訊、天氣模式和駕駛員行為遙測數據，從而最佳化路線規劃、預測維護需求並主動管理燃油消耗。人工智慧驅動的車輛管理最佳化帶來的顯著投資回報——通常可降低10-20%的燃油成本和15-25%的維護成本——已使最初持懷疑態度的車輛管理人員轉變為積極的採用者。

傳統車輛管理基礎設施中資料整合的挑戰

許多成熟的車隊營運商仍然保留著對傳統車載資訊平台、車輛追蹤硬體和管理軟體的現有投資，這些設備缺乏與人工智慧平台無縫整合所需的開放API和資料架構。遷移到人工智慧驅動的管理系統通常需要更換大規模車隊的車載硬體，這將導致巨額資本支出，並在過渡期間造成潛在的營運中斷。商用車輛車隊的異質性，包括不同製造商、不同年份的車型以及不同的OEM車載資訊系統架構，帶來了複雜的數據標準化挑戰，必須解決這些挑戰，人工智慧模型才能為所有在運車輛提供可靠的洞察。

自動駕駛車輛的車隊管理和預測性物流最佳化

新興的自動駕駛商用車領域需要一個先進的人工智慧車隊管理平台，該平台能夠協調人工駕駛車輛和自動駕駛車輛的混合運營，管理遠端監控職責，並根據動態需求模式最佳化自動駕駛車輛的部署。在傳統車隊管理領域已佔據主導地位的人工智慧平台，在將其功能擴展到自動駕駛車隊編配具有獨特的優勢，使其能夠佔據高階市場，並獲得極高的軟硬體收益比。此外，將人工智慧車隊數據與供應鏈規劃系統深度整合，為從倉儲運營到最後一公里配送的端到端物流最佳化創造了機會。

人工智慧互聯車隊架構中的網路安全漏洞

人工智慧驅動的車隊管理平台匯集了高度敏感的營運數據，例如車輛位置、客戶交付資訊、貨物詳情和駕駛員身份，所有這些數據都集中在一個雲端架構上進行管理，這使其成為網路犯罪分子和國家支持的攻擊者的首要目標。針對車隊管理平台的網路攻擊一旦成功，可能導致貨物被盜、關鍵價值鏈中斷、駕駛員隱私洩漏或高度敏感的商業活動曝光。隨著車輛管理系統和車輛控制單元之間連接性的增強，惡意攻擊者干擾車輛運行的可能性也越來越大。應對這些日益成長的網路風險需要持續投資於平台安全架構、威脅監控和員工安全意識提升。

新型冠狀病毒（COVID-19）的影響：

新冠疫情大大加速了人工智慧驅動的車隊管理技術的應用，因為電子商務交易量激增，而勞動力短缺問題也變得愈發嚴峻。這使得企業迫切需要能夠最大限度提高有限資源利用效率的營運最佳化工具。非接觸式配送的要求以及對駕駛員健康狀況的監控進一步增加了複雜性，但人工智慧驅動的調度和路線規劃平台恰好能夠完美應對這些挑戰。供應鏈中斷也讓物流經營團隊更意識到即時營運視覺化所帶來的競爭優勢，加速了他們在復甦階段的技術投資決策。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率。這反映了基於​​訂閱的車輛管理平台所產生的高額經常性收入，以及人工智慧驅動的分析相比硬體組件所能創造的更高價值。大規模商用車業者願意大力投資已被證實有效的效率提升方案，因此對車輛追蹤、預測分析和路線最佳化系統等軟體平台的需求旺盛，價格也居高不下。隨著人工智慧模型效能的不斷提升，軟體領域受益於可擴展的單位經濟效益，從而為現有平台供應商創造了協同競爭優勢。

預計人工智慧和機器學習領域在預測期內將呈現最高的複合年成長率。

在預測期內，人工智慧和機器學習領域預計將呈現最高的成長率，因為車隊管理供應商正擴大將先進的預測模型、用於駕駛員監控的電腦視覺系統以及自然語言介面整合到其核心平台產品中。生成式人工智慧功能正在徹底改變車隊管理人員與營運數據的互動方式，使他們能夠進行以往需要專業分析師才能完成的互動式查詢。隨著聯網汽車的不斷成長，用於訓練人工智慧模型的資料集也在不斷擴大，這提高了維護、路線規劃和需求預測等應用的預測精度，人工智慧的實際價值貢獻也持續成長。

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。這主要歸功於其商用車隊中全球領先的遠端資訊處理技術普及率、高度發展且能快速採用創新技術提升營運效率的物流行業，以及Samsara、Geotab和Verizon Connect等領先的人工智慧車隊管理平台供應商的集中。監管要求，例如美國強制使用電子記錄設備（ELD），正在加速遠端資訊處理技術的基礎應用，並建立一個隨時可以升級到人工智慧功能的部署基礎。北美擁有數萬輛汽車的大規模車隊提供了必要的數據量，從而最大限度地發揮人工智慧模型的性能。

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。這主要得益於中國、印度和東南亞電子商務物流的爆炸式成長，由此產生了對車隊最佳化技術的巨大需求；同時，基於智慧型手機的遠端資訊處理技術的日益普及，也使得中小企業能夠以更具成本效益的方式進行車隊管理。中國科技公司正在開發針對該地區獨特物流模式和車輛架構量身定做的人工智慧車輛管理平台。在印度，正規物流業正在快速擴張，政府大力推動電子運單（e-way bill）數位化，為人工智慧車輛管理平台的應用創造了有利環境。

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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
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動車輛管理市場：按組件分類

  • 軟體
    • 車輛追蹤和遠端資訊處理軟體
    • 預測分析平台
    • 路線最佳化系統
  • 硬體
    • GPS裝置
    • 物聯網感測器
    • 行車記錄器和ADAS系統
  • 服務

第6章：全球人工智慧驅動車輛管理市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合

第7章：全球人工智慧驅動車輛管理市場：按車輛類型分類

  • 商用車輛
  • 搭乘用車
  • 建築和採礦設備車隊
  • 大眾運輸車輛車隊

第8章：全球人工智慧驅動車輛管理市場：按應用領域分類

  • 車輛追蹤和監控
  • 預測性保護
  • 燃料管理
  • 駕駛員行為監控
  • 路線最佳化
  • 合規管理
  • 安全與風險管理

第9章：全球人工智慧驅動車輛管理市場：按技術分類

  • 人工智慧（AI）和機器學習（ML）
  • 物聯網 (IoT)
  • 巨量資料分析
  • 雲端運算
  • 邊緣運算

第10章：全球人工智慧驅動車輛管理市場：按最終用戶分類

  • 物流/運輸
  • 電子商務與零售
  • 製造業
  • 建造
  • 石油和天然氣
  • 政府/公共部門
  • 共享旅遊服務

第11章 全球人工智慧驅動車輛管理市場：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

  • Samsara
  • Geotab
  • Lytx
  • Powerfleet
  • Verizon Connect
  • Motive
  • Teletrac Navman
  • Webfleet
  • Trimble Inc.
  • Omnitracs
  • Fleet Complete
  • MiX Telematics
  • ORBCOMM
  • Zonar Systems
  • Netradyne
Product Code: SMRC37285

According to Stratistics MRC, the Global AI-Powered Fleet Management Market is accounted for $5.8 billion in 2026 and is expected to reach $29.4 billion by 2034, growing at a CAGR of 22.6% during the forecast period. AI-Powered Fleet Management is an advanced approach to overseeing vehicle fleets by utilizing artificial intelligence technologies to analyze real-time and historical data from vehicles, drivers, routes, and operations. It helps organizations optimize route planning, improve fuel efficiency, enhance driver safety, predict maintenance needs, reduce operational costs, and increase overall fleet productivity. By automating decision-making and delivering actionable insights, AI-powered fleet management enables more efficient, reliable, and data-driven transportation and logistics operations.

Market Dynamics:

Driver:

Escalating logistics complexity and demand for real-time operational intelligence

Intensifying e-commerce fulfillment expectations, fuel price volatility, and increasing regulatory compliance requirements are compelling fleet operators to move beyond basic GPS tracking toward artificial intelligence-driven management platforms that deliver actionable predictive insights. AI-powered fleet management systems process continuous streams of vehicle sensor data, traffic information, weather patterns, and driver behavioral telemetry to optimize routing decisions, predict maintenance requirements, and proactively manage fuel consumption. The demonstrable return on investment from AI-driven fleet optimization typically yielding 10-20% fuel savings and 15-25% maintenance cost reductions is converting skeptical fleet managers into enthusiastic adopters.

Restraint:

Data integration challenges with legacy fleet management infrastructure

Many established fleet operators maintain existing investments in legacy telematics platforms, vehicle tracking hardware, and management software that lack the open APIs and data architectures required for seamless AI platform integration. Transitioning to AI-powered management systems often requires replacing vehicle-installed hardware across large fleets, creating substantial capital expenditure requirements and operational disruptions during migration periods. The heterogeneous nature of commercial vehicle fleets encompassing multiple vehicle makes, model years, and OEM telematics architectures creates complex data normalization challenges that must be resolved before AI models can deliver reliable insights across the entire operated fleet.

Opportunity:

Autonomous vehicle fleet management and predictive logistics optimization

The emerging autonomous commercial vehicle sector will require sophisticated AI fleet management platforms capable of coordinating mixed human-piloted and autonomous vehicle operations, managing remote monitoring responsibilities, and optimizing autonomous vehicle deployment against dynamic demand patterns. AI platforms that successfully establish themselves in conventional fleet management are uniquely positioned to extend their capabilities into autonomous fleet orchestration, capturing a premium market segment with extremely high software-to-hardware revenue ratios. Additionally, deep integration of AI fleet data with supply chain planning systems creates opportunities to deliver end-to-end logistics optimization extending from warehouse operations through last-mile delivery completion.

Threat:

Cybersecurity vulnerabilities in AI-connected fleet architectures

AI-powered fleet management platforms aggregate sensitive operational data including vehicle locations, customer delivery information, cargo details, and driver identities across centralized cloud architectures that represent high-value targets for cybercriminals and nation-state actors. A successful cyberattack targeting a fleet management platform could enable cargo theft, disrupt critical supply chains, compromise driver privacy, or expose confidential commercial operations. The increasing connectivity between fleet management systems and vehicle control units creates potential pathways for malicious actors to interfere with vehicle operations. Managing these escalating cyber risks requires continuous investment in platform security architecture, threat monitoring, and employee security awareness.

Covid-19 Impact:

The COVID-19 pandemic dramatically accelerated AI fleet management adoption as e-commerce volumes surged while labor availability declined sharply, creating urgent demand for operational optimization tools that could extract maximum efficiency from constrained resources. Contactless delivery requirements and health monitoring needs for drivers added additional complexity that AI-powered dispatch and routing platforms were uniquely positioned to address. Supply chain disruptions created heightened awareness among logistics executives of the competitive advantage afforded by real-time operational visibility, driving accelerated technology investment decisions during the recovery period.

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

The Software segment is expected to account for the largest market share during the forecast period, reflecting the high recurring revenue streams generated by subscription-based fleet management platforms and the disproportionate value creation delivered through AI-driven analytics relative to hardware components. Software platforms including fleet tracking, predictive analytics, and route optimization systems command premium pricing from large commercial fleet operators willing to invest significantly for documented efficiency gains. The software segment benefits from scalable unit economics as AI model performance improves with data accumulation, creating compounding competitive advantages for established platform providers.

The AI & Machine Learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the AI & Machine Learning segment is predicted to witness the highest growth rate as fleet management vendors increasingly embed advanced predictive models, computer vision systems for driver monitoring, and natural language interfaces into their core platform offerings. Generative AI capabilities are transforming how fleet managers interact with operational data, enabling conversational queries that previously required specialized analyst expertise. Expanding AI model training datasets from growing connected vehicle populations are improving prediction accuracy across maintenance, routing, and demand forecasting applications, continually expanding AI's demonstrable value contribution.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by the world's highest commercial fleet penetration of telematics technologies, a sophisticated logistics industry that rapidly adopts operational efficiency innovations, and the concentration of leading AI fleet management platform vendors including Samsara, Geotab, and Verizon Connect. Regulatory requirements such as the Electronic Logging Device mandate in the United States have accelerated baseline telematics adoption, creating a receptive installed base for AI capability upgrades. Large North American fleets managing tens of thousands of vehicles provide the data volumes that maximize AI model performance.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by the explosive growth of e-commerce logistics in China, India, and Southeast Asia creating massive demand for fleet optimization technology, combined with increasing smartphone telematics adoption enabling cost-effective fleet management for smaller operators. Chinese technology companies are developing AI fleet management platforms tailored to the region's unique logistics patterns and vehicle architectures. India's rapidly expanding organized logistics sector and government e-way bill digitization initiative are creating enabling conditions for AI fleet management platform adoption.

Key players in the market

Some of the key players in AI-Powered Fleet Management Market include Samsara, Geotab, Lytx, Powerfleet, Verizon Connect, Motive, Teletrac Navman, Webfleet, Trimble Inc., Omnitracs, Fleet Complete, MiX Telematics, ORBCOMM, Zonar Systems, and Netradyne.

Key Developments:

In March 2026, Samsara announced the launch of its AI-powered Fleet Intelligence platform featuring a large language model-based operational assistant enabling fleet managers to interact with vehicle data through natural language queries, automated incident analysis, and proactive safety coaching recommendations, representing a significant advancement in AI-driven fleet management usability.

In February 2026, Geotab announced the acquisition of a leading AI-powered predictive maintenance startup to strengthen its vehicle health monitoring capabilities, enabling deeper integration of machine learning-based component failure prediction into the MyGeotab platform and expanding the company's competitive differentiation in the rapidly evolving AI fleet management sector.

Components Covered:

  • Software
  • Hardware
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Fleet Types Covered:

  • Commercial Vehicles
  • Passenger Vehicles
  • Construction & Mining Equipment Fleets
  • Public Transportation Fleets

Applications Covered:

  • Vehicle Tracking & Monitoring
  • Predictive Maintenance
  • Fuel Management
  • Driver Behavior Monitoring
  • Route Optimization
  • Compliance Management
  • Safety & Risk Management

Technologies Covered:

  • AI & Machine Learning (ML)
  • Internet of Things (IoT)
  • Big Data Analytics
  • Cloud Computing
  • Edge Computing

End Users Covered:

  • Logistics & Transportation
  • E-commerce & Retail
  • Manufacturing
  • Construction
  • Oil & Gas
  • Government & Public Sector
  • Ride-Hailing & Mobility Services

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-Powered Fleet Management Market, By Component

  • 5.1 Software
    • 5.1.1 Fleet tracking & telematics software
    • 5.1.2 Predictive analytics platforms
    • 5.1.3 Route optimization systems
  • 5.2 Hardware
    • 5.2.1 GPS devices
    • 5.2.2 IoT sensors
    • 5.2.3 Dashcams & ADAS systems
  • 5.3 Services

6 Global AI-Powered Fleet Management Market, By Deployment Mode

  • 6.1 Cloud-Based
  • 6.2 On-Premises
  • 6.3 Hybrid

7 Global AI-Powered Fleet Management Market, By Fleet Type

  • 7.1 Commercial Vehicles
  • 7.2 Passenger Vehicles
  • 7.3 Construction & Mining Equipment Fleets
  • 7.4 Public Transportation Fleets

8 Global AI-Powered Fleet Management Market, By Application

  • 8.1 Vehicle Tracking & Monitoring
  • 8.2 Predictive Maintenance
  • 8.3 Fuel Management
  • 8.4 Driver Behavior Monitoring
  • 8.5 Route Optimization
  • 8.6 Compliance Management
  • 8.7 Safety & Risk Management

9 Global AI-Powered Fleet Management Market, By Technology

  • 9.1 Artificial Intelligence (AI) & Machine Learning (ML)
  • 9.2 Internet of Things (IoT)
  • 9.3 Big Data Analytics
  • 9.4 Cloud Computing
  • 9.5 Edge Computing

10 Global AI-Powered Fleet Management Market, By End User

  • 10.1 Logistics & Transportation
  • 10.2 E-commerce & Retail
  • 10.3 Manufacturing
  • 10.4 Construction
  • 10.5 Oil & Gas
  • 10.6 Government & Public Sector
  • 10.7 Ride-Hailing & Mobility Services

11 Global AI-Powered Fleet Management 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 Samsara
  • 14.2 Geotab
  • 14.3 Lytx
  • 14.4 Powerfleet
  • 14.5 Verizon Connect
  • 14.6 Motive
  • 14.7 Teletrac Navman
  • 14.8 Webfleet
  • 14.9 Trimble Inc.
  • 14.10 Omnitracs
  • 14.11 Fleet Complete
  • 14.12 MiX Telematics
  • 14.13 ORBCOMM
  • 14.14 Zonar Systems
  • 14.15 Netradyne

List of Tables

  • Table 1 Global AI-Powered Fleet Management Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Powered Fleet Management Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Powered Fleet Management Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI-Powered Fleet Management Market Outlook, By Fleet tracking & telematics software (2023-2034) ($MN)
  • Table 5 Global AI-Powered Fleet Management Market Outlook, By Predictive analytics platforms (2023-2034) ($MN)
  • Table 6 Global AI-Powered Fleet Management Market Outlook, By Route optimization systems (2023-2034) ($MN)
  • Table 7 Global AI-Powered Fleet Management Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 8 Global AI-Powered Fleet Management Market Outlook, By GPS devices (2023-2034) ($MN)
  • Table 9 Global AI-Powered Fleet Management Market Outlook, By IoT sensors (2023-2034) ($MN)
  • Table 10 Global AI-Powered Fleet Management Market Outlook, By Dashcams & ADAS systems (2023-2034) ($MN)
  • Table 11 Global AI-Powered Fleet Management Market Outlook, By Services (2023-2034) ($MN)
  • Table 12 Global AI-Powered Fleet Management Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 13 Global AI-Powered Fleet Management Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 14 Global AI-Powered Fleet Management Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 15 Global AI-Powered Fleet Management Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 16 Global AI-Powered Fleet Management Market Outlook, By Fleet Type (2023-2034) ($MN)
  • Table 17 Global AI-Powered Fleet Management Market Outlook, By Commercial Vehicles (2023-2034) ($MN)
  • Table 18 Global AI-Powered Fleet Management Market Outlook, By Passenger Vehicles (2023-2034) ($MN)
  • Table 19 Global AI-Powered Fleet Management Market Outlook, By Construction & Mining Equipment Fleets (2023-2034) ($MN)
  • Table 20 Global AI-Powered Fleet Management Market Outlook, By Public Transportation Fleets (2023-2034) ($MN)
  • Table 21 Global AI-Powered Fleet Management Market Outlook, By Application (2023-2034) ($MN)
  • Table 22 Global AI-Powered Fleet Management Market Outlook, By Vehicle Tracking & Monitoring (2023-2034) ($MN)
  • Table 23 Global AI-Powered Fleet Management Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 24 Global AI-Powered Fleet Management Market Outlook, By Fuel Management (2023-2034) ($MN)
  • Table 25 Global AI-Powered Fleet Management Market Outlook, By Driver Behavior Monitoring (2023-2034) ($MN)
  • Table 26 Global AI-Powered Fleet Management Market Outlook, By Route Optimization (2023-2034) ($MN)
  • Table 27 Global AI-Powered Fleet Management Market Outlook, By Compliance Management (2023-2034) ($MN)
  • Table 28 Global AI-Powered Fleet Management Market Outlook, By Safety & Risk Management (2023-2034) ($MN)
  • Table 29 Global AI-Powered Fleet Management Market Outlook, By Technology (2023-2034) ($MN)
  • Table 30 Global AI-Powered Fleet Management Market Outlook, By Artificial Intelligence (AI) & Machine Learning (ML) (2023-2034) ($MN)
  • Table 31 Global AI-Powered Fleet Management Market Outlook, By Internet of Things (IoT) (2023-2034) ($MN)
  • Table 32 Global AI-Powered Fleet Management Market Outlook, By Big Data Analytics (2023-2034) ($MN)
  • Table 33 Global AI-Powered Fleet Management Market Outlook, By Cloud Computing (2023-2034) ($MN)
  • Table 34 Global AI-Powered Fleet Management Market Outlook, By Edge Computing (2023-2034) ($MN)
  • Table 35 Global AI-Powered Fleet Management Market Outlook, By End User (2023-2034) ($MN)
  • Table 36 Global AI-Powered Fleet Management Market Outlook, By Logistics & Transportation (2023-2034) ($MN)
  • Table 37 Global AI-Powered Fleet Management Market Outlook, By E-commerce & Retail (2023-2034) ($MN)
  • Table 38 Global AI-Powered Fleet Management Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 39 Global AI-Powered Fleet Management Market Outlook, By Construction (2023-2034) ($MN)
  • Table 40 Global AI-Powered Fleet Management Market Outlook, By Oil & Gas (2023-2034) ($MN)
  • Table 41 Global AI-Powered Fleet Management Market Outlook, By Government & Public Sector (2023-2034) ($MN)
  • Table 42 Global AI-Powered Fleet Management Market Outlook, By Ride-Hailing & Mobility Services (2023-2034) ($MN)

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