The global AIoT (Artificial Intelligence of Things) market is projected to expand from USD 18.37 billion in 2024 to USD 79.13 billion by 2030, reflecting a robust CAGR of 27.6% during the forecast period. Advances in AI algorithms and IoT sensors have significantly enhanced the efficiency of real-time data processing, analysis, and decision-making across various industries. The increasing adoption of IoT devices, coupled with the growing demand for faster, real-time data analysis, has driven the development of AI solutions for operational optimization.
The introduction of 5G technology further accelerates market growth by enabling higher data transfer rates and low latency, critical for AIoT applications such as autonomous vehicles and smart cities. Additionally, the rise of Industry 4.0 and automation trends in manufacturing is fueling demand for AIoT, as it aids in predicting equipment failures, optimizing maintenance schedules, and managing inventory more effectively.
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Based on offerings, the platforms segment holds the largest market size during the forecast period.
In the AloT market, platforms help develop and deploy Al solutions, enhancing IoT systems’ performance and functionality. These platforms perform several different roles and are also divided into various subcategories. IoT device management is the management platform that manages the entire life cycle of usage from installation and operations to maintenance and upgrades of the loT devices. This process aims to ensure that the designed figures are used effectively and safely for the period they have been intended to serve. IoT application enablement platforms are the platforms that collect the requirements needed for the development and monetization of AloT applications. This enables the interface of various devices and information, which makes it easier for application developers to create and manage applications that deal with the data generated from IoT devices. IoT connectivity management platforms and services are designed to manage data flow within the network and between loT devices and the cloud. A loT cloud is defined as a service that provides a flexible architecture for the storage, management, and analysis of large quantities of loT data for better and quicker business decisions. IoT advanced analytics use big data analytics and Al techniques to scrutinize performance data retrieved from loT devices to provide actionable information that can help improve operations.
Based on deployment type, edge-based AIoT is projected to register the highest CAGR during the forecast period.
Edge-based AloT implementations exploit the advantage of processing data closer to the loT devices or at the extremities, thereby reducing the dependency on high bandwidths and the latencies experienced during data analysis. AloT systems with edge characteristics are managed into three major layers: the collection terminal, connectivity, and edge layer, each performing its designated functions. Certain hardware components in the collection terminal layer include sensors, vehicles, embedded systems, tags, and active mobile components wired to gateways through the existing electricity lines. In this case, the connectivity layer also possesses field gateways that connect with the collection terminal layer using these power transmission lines. Finally, the edge layer includes functionalities such as data warehouses, data processing resources, and even insight generators within the system.
Based on region, North America holds the second-largest market size during the forecast period.
North America has been relatively predominant in technological advancement, widespread usage with other industries, and significant investment in the AIoT segment. This region is expected to occupy the 2nd largest market share of the global AIoT market based on its technology prowess. The manufacturing, healthcare, and transport sectors are expected to be the main sectors that will help drive the adoption of IoT technology. For instance, manufacturing industries use AIoT solutions such as predictive maintenance and supply chain management to propel the market. Likewise, AIoT’s primary concern in healthcare is virtual patient care delivery and personalized medicine.
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Unique Features in the AIoT Market
AIoT integrates advanced AI algorithms with IoT devices to enable real-time data processing and decision-making. This combination allows businesses to analyze large volumes of data instantly and take immediate action, enhancing operational efficiency and responsiveness, especially in critical applications like manufacturing, healthcare, and logistics.
One of the standout features of AIoT is its ability to drive autonomous operations. AI-enabled IoT devices can perform tasks without human intervention, such as predictive maintenance, autonomous vehicles, and smart energy management. This automation reduces the need for manual oversight and increases operational efficiency across industries.
AIoT often incorporates edge computing, which enables data processing closer to the source of data generation. By minimizing the need to send data to central servers, edge computing enhances the speed of decision-making, reduces latency, and alleviates bandwidth constraints, making AIoT applications more efficient and reliable, especially in remote locations or time-sensitive scenarios.
AIoT devices are increasingly leveraging 5G networks for faster data transfer, ultra-low latency, and improved connectivity. 5G’s capabilities allow AIoT systems to operate in real time, supporting high-demand applications like autonomous vehicles, smart cities, and industrial automation. This integration accelerates innovation and creates new opportunities for AIoT use cases.
AIoT solutions excel in predictive analytics, helping businesses predict and prevent equipment failures before they occur. By analyzing data from IoT sensors, AI algorithms can identify patterns and anomalies, enabling businesses to perform maintenance only when needed, reducing downtime, and lowering operational costs in sectors like manufacturing, energy, and healthcare.
Major Highlights of the AIoT Market
The convergence of artificial intelligence (AI) and the Internet of Things (IoT) has led to the creation of intelligent systems that can process and analyze data in real time. These innovations are enhancing operational efficiencies, optimizing workflows, and enabling new business models in industries such as manufacturing, healthcare, and agriculture.
As more IoT devices are deployed globally, the amount of data generated has skyrocketed, creating a need for faster, smarter data processing. AIoT is designed to manage this influx of data, enabling faster analysis and more efficient decision-making. The growing number of connected devices is expected to further propel AIoT adoption across sectors.
The introduction of 5G technology is accelerating the growth of AIoT by providing the high-speed, low-latency connectivity required for real-time data processing. 5G’s enhanced capabilities are enabling AIoT applications in areas such as autonomous vehicles, smart cities, and industrial automation, where fast data transfer is critical.
AIoT is playing a crucial role in Industry 4.0 by enabling automation, predictive maintenance, and process optimization in manufacturing. AIoT solutions help predict equipment failures, manage supply chains, and reduce downtime, contributing to cost savings and improved productivity in the manufacturing sector.
The AIoT market is seeing significant applications in autonomous systems, such as self-driving cars and drones. AI algorithms process real-time data from IoT sensors to enable vehicles to make decisions without human input, driving innovation in transportation and logistics.
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Top Companies in the AIoT Market
The major vendors in the AIoT market include IBM (US), Cisco (US), AWS (US), Google (US), Microsoft (US), Oracle (US), HPE (US), Intel (US), Hitachi (Japan), SAP (Germany). The market players have adopted various strategies such as developing advanced products, partnerships, contracts, expansions, and acquisitions to strengthen their AIoT market position. Organic and inorganic strategies have helped the market players expand globally by providing AIoT solutions.
IBM
IBM deals in hardware, software, and service across computing intrusion, Artificial intelligence, Cyber security, and the Internet of Things. The company operates through multiple strategic segments: Cloud and cognitive software, Global business services (GBS), Global technological services, Systems, and Global Financing. The AIoT solutions provided by the company include IBM Maximo Asset Management and IBM Edge Application Manager. IBM Maximo Asset Management aims to help organizations manage their assets using maintenance and monitoring functionality, such as IoT devices. It helps monitor the performance of the assets and facilitates preventive maintenance, hence causing less or no downtime and increasing operation efficiency. The IBM Edge Application Manager is a solution that addresses the needs of organizations by allowing them to deploy AI and IoT applications at the edge. Analytics also makes it easier to have up-to-date information and improve the speed of making decisions.
Cisco
Cisco specializes in providing comprehensive technological solutions across five primary segments: Networking, Security, Collaboration, Application, and Cloud computing. In these segments, Cisco provides products and services to improve access, protection, and effectiveness for clients worldwide. Organizations serve these businesses’ data center and cloud needs with data center switches, servers, storage, and cloud management software for building the next-generation data center and adopting cloud computing. In the AIoT market, Cisco provides Cisco IoT Networking and Cisco Edge Intelligence. Cisco IoT Networking also offers various IoT networking solutions covering industrial routers, switches, and gateways. These solutions deliver robust and secure IoT communication, data collection, and analytics at the IoT node and bridge the IoT devices with Cisco’s IoT platform. The Cisco Edge Intelligence provides the ability to process, analyze, and act on the IoT data at the organization’s edge. Some components are edge computing, machine learning, and analytical capabilities for near real-time decision-making.
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