市场调查报告书
商品编码
1466805
人工智慧边缘运算市场:按组件、资料来源、应用程式和最终用户划分 - 2024-2030 年全球预测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边缘运算市场规模预计2023年为162.5亿美元,2024年达到195.2亿美元,预计2030年将达到604.3亿美元,复合年增长率为20.63%。
人工智慧边缘运算包括人工智慧(AI)和机器学习(ML)技术在边缘运算系统中的应用。这些系统可以在本地即时处理和分析资料,而不是依赖集中的云端基础的伺服器。该市场包括能够有效处理靠近源头的大量资料、提高效能、减少延迟并改善各种最终用途的隐私的硬体、软体和服务。物联网设备的普及普及以及 AI/ML 演算法的进步(可以更快、更低功耗地执行复杂任务)正在推动 AI 边缘运算的使用。对低延迟应用程式的需求不断增长,由于法律规范加大而对资料安全和隐私的担忧日益增加,以及全球各行业对工业4.0 计划的日益关注,都推动了对人工智慧边缘运算形成平台的需求。与实施边缘基础设施相关的高昂初始投资成本以及将来自不同物联网设备的多个资料来源整合到一致系统中的复杂性阻碍了市场成长。节能处理器、记忆体单元和其他专为处理边缘人工智慧任务而设计的组件新兴市场的持续开拓预计将带来市场成长机会。
主要市场统计 | |
---|---|
基准年[2023] | 162.5亿美元 |
预测年份 [2024] | 195.2亿美元 |
预测年份 [2030] | 604.3亿美元 |
复合年增长率(%) | 20.63% |
扩展软体组件的使用以进行组件即时分析
硬体组件在人工智慧边缘运算系统中发挥着至关重要的作用。提供支援网路边缘高阶运算所需的基础设施,从而实现即时决策并减少延迟。在硬体选择方面,效能、能源效率以及与现有基础设施的兼容性等因素是重要的考虑因素。服务涵盖人工智慧边缘运算实施的各个方面,包括实施咨询、系统整合、维护支援和培训。组织经常寻求能够提供针对其特定需求的边缘运算架构专业知识的服务供应商。软体构成了人工智慧边缘运算系统的支柱,支援资料处理、机器学习演算法和即时分析。选择正确的软体可确保与硬体组件的无缝整合以及计算资源的高效利用。在比较这些子类别时,硬体选择应优先考虑效能和相容性。同时,服务在协助组织进行系统整合和支援边缘运算架构方面发挥关键作用,软体供应商则促进边缘设备的人工智慧模型的部署和最佳化。
资料来源:行动资料对创作者的需求不断增长
生物辨识资料是指用于识别或认证目的的个人的身体或行为特征。其中包括指纹、脸部辨识、虹膜扫描、语音模式、步态分析等。基于安全需求和便利因素,各行业的组织正在采用生物辨识技术。行动资料包括智慧型手机和其他可携式装置产生的各种资讯。这包括用户产生的内容、定位服务资料、使用模式、应用程式分析资料等。企业使用此资料来源进行有针对性的行销宣传活动并优化客户参与。感测器资料由各种电子设备生成,包括物联网设备、工业设备和穿戴式健康监视器。包含有关物理属性的信息,例如温度、压力、湿度和运动。本公司使用此资料来源进行预测性维护、远端监控和流程最佳化。
存取管理对于确保安全存取关键资源和维护组织中的资料完整性至关重要。人工智慧边缘运算供应商提供先进的人工智慧边缘运算解决方案,透过即时监控和异常检测来增强存取控制流程。自动驾驶汽车的兴起推动了对人工智慧边缘运算解决方案的需求,这些解决方案可促进即时决策能力。创新的能源管理系统利用人工智慧边缘运算技术来优化能源消耗并降低与发电相关的成本。人工智慧边缘运算透过在源头实现快速资料处理、减少延迟和网路负载,彻底改变了远端监控和预测性维护。由于人工智慧边缘运算的采用,遥测领域正在经历显着成长,人工智慧边缘运算可以从不同来源高效收集资料。视讯监控产业正在利用人工智慧边缘运算技术并在边缘设备上实施即时分析,以提高安全系统的效率和准确性。
最终用户:它越来越多地应用于IT和通讯领域,以优化网路运营并改善客户经验。
人工智慧边缘运算在汽车领域极为重要,特别是在 ADAS(高级驾驶辅助系统)和自动驾驶汽车中。对即时处理、低延迟通讯和资料安全的需求正在推动人工智慧边缘解决方案在该领域的采用。能源和公共产业部门透过改善电网管理、能源效率和资产的预测性维护而受益于人工智慧边缘运算。各国政府正采用人工智慧边缘运算,透过监控系统增强公共,同时保护资料隐私。人工智慧边缘运算将透过即时患者监测系统、诊断工具和远端医疗应用来改变医疗保健产业。人工智慧驱动的边缘运算使 IT 和通讯公司能够优化网路营运、增强资料分析能力并透过个人化服务改善客户体验。在製造业中,人工智慧驱动的边缘运算技术可用于对设备进行预测性维护,提高生产效率,降低营运成本。
区域洞察
在美洲,美国和加拿大等北美国家在技术进步方面处于领先地位,对研发活动进行了大量投资。推动该地区成长的因素包括企业物联网设备采用率的上升以及云端基础的服务的增加。此外,医疗保健和汽车等各行业对即时资料处理解决方案以提高业务效率的需求不断增长,这进一步加速了人工智慧边缘运算技术的采用。欧洲目前处于采用先进人工智慧技术的前沿,各国政府采取了许多措施来支持欧盟(EU)国家的人工智慧研究。工业 4.0 推动者也在推动製造业中人工智慧驱动的自动化解决方案,导致对边缘运算功能的需求增加。儘管人工智慧技术在中东和非洲的采用仍处于起步阶段,但由于政府加大对数位转型措施的支持以及对智慧城市计划的投资增加,预计成长潜力强劲。由于快速工业化、智慧型手机普及的提高以及通讯基础设施的进步,亚太地区的人工智慧边缘运算市场在预测期内呈现快速成长。中国、日本和韩国正在透过政府政策支持研发活动并专注于跨产业的物联网应用,推动人工智慧的强劲发展。此外,澳洲和新加坡等新兴经济体正致力于创建包含人工智慧主导解决方案的智慧城市框架,以促进该领域的成长机会。
FPNV定位矩阵
FPNV定位矩阵对于评估AI边缘运算市场至关重要。我们检视与业务策略和产品满意度相关的关键指标,以对供应商进行全面评估。这种深入的分析使用户能够根据自己的要求做出明智的决策。根据评估,供应商被分为四个成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市场占有率分析
市场占有率分析是一个综合工具,可以对人工智慧边缘运算市场供应商的现状进行深入而详细的研究。全面比较和分析供应商在整体收益、基本客群和其他关键指标方面的贡献,以便更好地了解公司的绩效及其在争夺市场占有率时面临的挑战。此外,该分析还提供了对该行业竞争特征的宝贵见解,包括在研究基准年观察到的累积、分散主导地位和合併特征等因素。这种详细程度的提高使供应商能够做出更明智的决策并制定有效的策略,从而在市场上获得竞争优势。
1. 市场渗透率:提供有关主要企业所服务的市场的全面资讯。
2. 市场开拓:我们深入研究利润丰厚的新兴市场,并分析其在成熟细分市场的渗透率。
3. 市场多元化:提供有关新产品发布、开拓地区、最新发展和投资的详细资讯。
4.竞争力评估与资讯:对主要企业的市场占有率、策略、产品、认证、监管状况、专利状况、製造能力等进行全面评估。
5. 产品开发与创新:提供对未来技术、研发活动和突破性产品开发的见解。
1.AI边缘运算市场的市场规模与预测为何?
2.AI边缘运算市场预测期间有哪些产品、细分市场、应用和领域值得考虑投资?
3.AI边缘运算市场的技术趋势与法规结构是什么?
4.AI边缘运算市场主要厂商的市场占有率为何?
5.进入AI边缘运算市场合适的型态和策略手段是什么?
[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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