市場調查報告書
商品編碼
1466805
人工智慧邊緣運算市場:按組件、資料來源、應用程式和最終用戶分類 - 2024-2030 年全球預測AI Edge Computing Market by Component (Hardware, Services, Software), Data Source (Biometric Data, Mobile Data, Sensor Data), Application, End-User - Global Forecast 2024-2030 |
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AI邊緣運算市場規模預計2023年為162.5億美元,2024年達到195.2億美元,預計2030年將達到604.3億美元,複合年成長率為20.63%。
人工智慧邊緣運算包括人工智慧(AI)和機器學習(ML)技術在邊緣運算系統中的應用。這些系統可以在本地即時處理和分析資料,而不是依賴集中的雲端基礎的伺服器。該市場包括能夠有效處理靠近源頭的大量資料、提高效能、減少延遲並改善各種最終用途的隱私的硬體、軟體和服務。物聯網設備的普及普及以及 AI/ML 演算法的進步(可以更快、更低功耗地執行複雜任務)正在推動 AI 邊緣運算的使用。對低延遲應用程式的需求不斷成長,由於法律規範加大而對資料安全和隱私的擔憂日益增加,以及全球各行業對工業4.0 計劃的日益關注,都推動了對人工智慧邊緣運算形成平台的需求。與實施邊緣基礎設施相關的高昂初始投資成本以及將來自不同物聯網設備的多個資料來源整合到一致系統中的複雜性阻礙了市場成長。節能處理器、記憶體單元和其他專為處理邊緣人工智慧任務而設計的組件新興市場的持續開拓預計將帶來市場成長機會。
主要市場統計 | |
---|---|
基準年[2023] | 162.5億美元 |
預測年份 [2024] | 195.2億美元 |
預測年份 [2030] | 604.3億美元 |
複合年成長率(%) | 20.63% |
擴展軟體組件的使用以進行組件即時分析
硬體組件在人工智慧邊緣運算系統中發揮著至關重要的作用。提供支援網路邊緣高階運算所需的基礎設施,從而實現即時決策並減少延遲。在硬體選擇方面,效能、能源效率以及與現有基礎設施的兼容性等因素是重要的考慮因素。服務涵蓋人工智慧邊緣運算實施的各個方面,包括實施諮詢、系統整合、維護支援和培訓。組織經常尋求能夠提供針對其特定需求的邊緣運算架構專業知識的服務供應商。軟體構成了人工智慧邊緣運算系統的支柱,支援資料處理、機器學習演算法和即時分析。選擇正確的軟體可確保與硬體組件的無縫整合以及計算資源的高效利用。在比較這些子類別時,硬體選擇應優先考慮效能和相容性。同時,服務在協助組織進行系統整合和支援邊緣運算架構方面發揮關鍵作用,軟體供應商則促進邊緣設備的人工智慧模型的部署和最佳化。
資料來源:行動資料對創作者的需求不斷成長
生物辨識資料是指用於識別或認證目的的個人的身體或行為特徵。其中包括指紋、臉部辨識、虹膜掃描、語音模式、步態分析等。基於安全需求和便利因素,各行業的組織正在採用生物辨識技術。行動資料包括智慧型手機和其他可攜式裝置產生的各種資訊。這包括用戶產生的內容、定位服務資料、使用模式、應用程式分析資料等。企業使用此資料來源進行有針對性的行銷宣傳活動並最佳化客戶參與。感測器資料由各種電子設備生成,包括物聯網設備、工業設備和穿戴式健康監視器。包含有關物理屬性的資訊,例如溫度、壓力、濕度和運動。本公司使用此資料來源進行預測性維護、遠端監控和流程最佳化。
存取管理對於確保安全存取關鍵資源和維護組織中的資料完整性至關重要。人工智慧邊緣運算供應商提供先進的人工智慧邊緣運算解決方案,透過即時監控和異常檢測來增強存取控制流程。自動駕駛汽車的興起推動了對人工智慧邊緣運算解決方案的需求,這些解決方案可促進即時決策能力。創新的能源管理系統利用人工智慧邊緣運算技術來最佳化能源消耗並降低與發電相關的成本。人工智慧邊緣運算透過在源頭實現快速資料處理、減少延遲和網路負載,徹底改變了遠端監控和預測性維護。由於人工智慧邊緣運算的採用,遙測領域正在經歷顯著成長,人工智慧邊緣運算可以從不同來源高效收集資料。視訊監控產業正在利用人工智慧邊緣運算技術並在邊緣設備上實施即時分析,以提高安全系統的效率和準確性。
最終用戶:它擴大應用於IT和通訊領域,以最佳化網路營運並改善客戶經驗。
人工智慧邊緣運算在汽車領域極為重要,特別是在 ADAS(高級駕駛輔助系統)和自動駕駛汽車中。對即時處理、低延遲通訊和資料安全的需求正在推動人工智慧邊緣解決方案在該領域的採用。能源和公共產業部門透過改善電網管理、能源效率和資產的預測性維護而受益於人工智慧邊緣運算。各國政府正採用人工智慧邊緣運算,透過監控系統增強公共,同時保護資料隱私。人工智慧邊緣運算將透過即時患者監測系統、診斷工具和遠端醫療應用來改變醫療保健產業。人工智慧驅動的邊緣運算使 IT 和通訊公司能夠最佳化網路營運、增強資料分析能力並透過個人化服務改善客戶體驗。在製造業中,人工智慧驅動的邊緣運算技術可用於對設備進行預測性維護,提高生產效率,降低營運成本。
區域洞察
在美洲,美國和加拿大等北美國家在技術進步方面處於領先地位,對研發活動進行了大量投資。推動該地區成長的因素包括企業物聯網設備採用率的上升以及雲端基礎的服務的增加。此外,醫療保健和汽車等各行業對即時資料處理解決方案以提高業務效率的需求不斷成長,這進一步加速了人工智慧邊緣運算技術的採用。歐洲目前處於採用先進人工智慧技術的前沿,各國政府採取了許多措施來支持歐盟(EU)國家的人工智慧研究。工業 4.0 推動者也在推動製造業中人工智慧驅動的自動化解決方案,導致對邊緣運算功能的需求增加。儘管人工智慧技術在中東和非洲的採用仍處於起步階段,但由於政府加大對數位轉型措施的支持以及對智慧城市計劃的投資增加,預計成長潛力強勁。由於快速工業化、智慧型手機普及的提高以及通訊基礎設施的進步,亞太地區的人工智慧邊緣運算市場在預測期內呈現快速成長。中國、日本和韓國正在透過政府政策支持研發活動並專注於跨產業的物聯網應用,推動人工智慧的強勁發展。此外,澳洲和新加坡等新興經濟體正致力於創建包含人工智慧主導解決方案的智慧城市框架,以促進該領域的成長機會。
FPNV定位矩陣
FPNV定位矩陣對於評估AI邊緣運算市場至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市場佔有率分析
市場佔有率分析是一個綜合工具,可以對人工智慧邊緣運算市場供應商的現狀進行深入而詳細的研究。全面比較和分析供應商在整體收益、基本客群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該行業競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、分散主導地位和合併特徵等因素。這種詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,從而在市場上獲得競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:提供有關新產品發布、開拓地區、最新發展和投資的詳細資訊。
4.競爭力評估與資訊:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況、製造能力等進行全面評估。
5. 產品開發與創新:提供對未來技術、研發活動和突破性產品開發的見解。
1.AI邊緣運算市場的市場規模與預測為何?
2.AI邊緣運算市場預測期間有哪些產品、細分市場、應用和領域值得考慮投資?
3.AI邊緣運算市場的技術趨勢與法規結構是什麼?
4.AI邊緣運算市場主要廠商的市場佔有率為何?
5.進入AI邊緣運算市場合適的型態和策略手段是什麼?
[187 Pages Report] The AI Edge Computing Market size was estimated at USD 16.25 billion in 2023 and expected to reach USD 19.52 billion in 2024, at a CAGR 20.63% to reach USD 60.43 billion by 2030.
The AI edge computing includes the application of artificial intelligence (AI) and machine learning (ML) technologies within edge computing systems. These systems enable real-time processing and analysis of data at the local level rather than relying on centralized cloud-based servers. The market encompasses hardware, software, and services that facilitate efficient processing of large-scale data close to its source, thereby offering enhanced performance, reduced latency, and improved privacy for various end-use applications. Increased IoT device adoption rates and advancement in AI/ML algorithms capable of performing complex tasks at a faster pace with lower power consumption requirements is driving the usage of AI edge computing. Growing demand for low-latency applications, rising concerns about data security & privacy due to stricter regulatory oversight, and increasing focus on Industry 4.0 initiatives amidst various industries globally are creating a platform for AI edge computing. High initial investment costs associated with implementing edge infrastructure and the complexity of integrating multiple data sources from different IoT devices into a coherent system are hampering market growth. The growing development of energy-efficient processors, memory units, and other components specifically designed to handle AI tasks at the edge is expected to create opportunities for market growth.
KEY MARKET STATISTICS | |
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Base Year [2023] | USD 16.25 billion |
Estimated Year [2024] | USD 19.52 billion |
Forecast Year [2030] | USD 60.43 billion |
CAGR (%) | 20.63% |
Component: Expanding usage of software components for real-time analytics
Hardware components play a crucial role in AI edge computing systems. They provide the necessary infrastructure to support advanced computations at the edge of networks, enabling real-time decision-making and reducing latency. When it comes to hardware selection, factors such as performance, power efficiency, and compatibility with existing infrastructure are key considerations. Services encompass various aspects of AI edge computing adoption, including deployment consulting, system integration, maintenance support, and training. Organizations often seek service providers that offer expertise in edge computing architectures tailored to their specific needs. Software forms the backbone of AI edge computing systems, enabling data processing, machine learning algorithms, and real-time analytics. Selecting the right software ensures seamless integration with hardware components and efficiently utilizes computational resources. When comparing these subcategories, hardware selection should prioritize performance and compatibility. At the same time, services play a vital role in assisting organizations with system integration and support for edge computing architectures, and software providers facilitate AI model deployment and optimization for edge devices.
Data Source: Increasing demand for mobile data to creat
Biometric data refers to individuals' physical or behavioral characteristics that are used for identification and authentication purposes. It includes fingerprints, facial recognition, iris scanning, voice patterns, and gait analysis. Organizations across various industries are adopting biometric technology based on security needs and convenience factors. Mobile data encompasses various information generated by smartphones and other portable devices. It includes user-generated content, location-based services data, usage patterns, and app analytics data. Companies leverage this data source for targeted marketing campaigns or optimizing customer engagement. Sensor data is generated by a variety of electronic devices, such as IoT gadgets, industrial equipment, and wearable health monitors. It encompasses information about physical properties like temperature, pressure, humidity, or movement. Companies use this data source for predictive maintenance, remote monitoring, and process optimization.
Access management is crucial for ensuring secure access to critical resources and maintaining data integrity in organizations. AI edge computing providers offer advanced AI-powered edge computing solutions that enhance access control processes through real-time monitoring and anomaly detection. The rise of autonomous vehicles has driven significant demand for AI edge computing solutions to facilitate real-time decision-making capabilities. Innovative energy management systems are leveraging AI edge computing technologies to optimize energy consumption and reduce costs associated with power generation. AI edge computing has revolutionized remote monitoring and predictive maintenance by enabling rapid data processing at the source, reducing latency and network load. The telemetry sector is experiencing significant growth with the adoption of AI edge computing to enable efficient data collection from disparate sources. The video surveillance industry is leveraging AI edge computing technologies to enhance security systems' efficiency and accuracy by implementing real-time analytics on edge devices.
End-User: Growing utilization by IT and telecommunication sector to optimize network operations and enhance customer experience
AI edge computing is crucial in the automotive sector, particularly in advanced driver-assistance systems (ADAS) and autonomous vehicles. The need for real-time processing, low-latency communication, and data security drives the adoption of AI edge solutions within the segment. The energy and utilities sector benefits from AI edge computing by improving grid management, energy efficiency, and predictive maintenance of assets. Governments are adopting AI edge computing to enhance public safety through surveillance systems while maintaining data privacy adherence. AI edge computing transforms healthcare through real-time patient monitoring systems, diagnostics tools, and telemedicine applications. AI-driven edge computing enables IT and telecom companies to optimize network operations, enhance data analytics capabilities, and improve customer experience through personalized services. The manufacturing sector adopts AI-powered edge computing technologies to enable predictive equipment maintenance, improve production efficiency, and lower operational costs.
Regional Insights
In the Americas, North American countries such as the United States and Canada are leading in technological advancements with strong investment in research & development activities. Factors driving growth in this region include higher adoption of IoT devices and increased cloud-based services among businesses. Moreover, there is a rising demand for real-time data processing solutions to improve operational efficiency across various industries such as healthcare and automotive, which further accelerates the adoption of AI edge computing technologies. Europe is currently at the forefront of adopting advanced AI technologies with numerous initiatives undertaken by governments to support research on artificial intelligence across European Union countries. Industry 4.0 enablers are also promoting AI-powered automation solutions in manufacturing sectors, leading to a growing demand for edge computing capabilities. In the Middle East and Africa, although the adoption of AI technologies is still in its nascent stage, high growth potential is anticipated due to increasing government support for digital transformation initiatives and rising investment in smart city projects. The Asia-Pacific region is witnessing a prompt growth rate in the AI edge computing market during the forecast period, owing to rapid industrialization, raised smartphone penetration, and advancements in communication infrastructure. China, Japan, and South Korea are driving strong AI development with government policies supporting research & development activities and focusing on IoT applications across industries. Furthermore, emerging countries such as Australia and Singapore focus on creating smart city frameworks incorporating AI-driven solutions, thus promoting growth opportunities within this sector.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the AI Edge Computing Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the AI Edge Computing Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the AI Edge Computing Market, highlighting leading vendors and their innovative profiles. These include ABB Ltd., ADLINK Technology Inc., Advantech Co., Ltd., Akamai Technologies, Inc., Amazon Web Services, Inc., Atos SE, Broadcom Inc., Cisco Systems, Inc., Cloudera, Inc., Dell Inc., EdgeConneX, Inc., EdgeCortix, Inc., Fastly, Inc., General Electric Company, Hewlett Packard Enterprise Development LP, Honeywell International Inc., Huawei Technologies Co., Ltd., Intel Corporation, International Business Machines Corporation, Lenovo Group Limited, Microsoft Corporation, NVIDIA Corporation, Robert Bosch GmbH, Rockwell Automation, Inc., SAP SE, Schneider Electric SE, Siemens AG, Sterlite Technologies Limited, Tata Elxsi Limited, Teksun Inc., Telefonaktiebolaget LM Ericsson, and Unigen Corporation.
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the AI Edge Computing Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the AI Edge Computing Market?
3. What are the technology trends and regulatory frameworks in the AI Edge Computing Market?
4. What is the market share of the leading vendors in the AI Edge Computing Market?
5. Which modes and strategic moves are suitable for entering the AI Edge Computing Market?
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