The global AIoT (Artificial Intelligence of Things) market is expected to expand from USD 18.37 billion in 2024 to USD 79.13 billion by 2030, with a Compound Annual Growth Rate (CAGR) of 27.6% during this period. Advances in AI algorithms and IoT sensors have significantly improved data processing, analysis, and real-time decision-making across various industries. The increasing number of IoT devices, along with the demand for faster, real-time data analysis, is driving the development of AI-based solutions for operational efficiency. The market’s growth is further accelerated by the introduction of 5G technology, which provides higher data transfer speeds and low latency, essential for AIoT applications like autonomous vehicles and smart cities. Additionally, Industry 4.0 and automation trends in manufacturing are key drivers, as AIoT enables predictive maintenance and optimized inventory management.
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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
Significant progress has been made in both AI algorithms and IoT sensor technologies, leading to enhanced data processing, real-time analysis, and decision-making capabilities. These advancements allow AIoT solutions to operate more efficiently in various real-world applications, helping industries streamline processes and increase automation.
The growing number of IoT devices in use today has created an urgent need for faster and more accurate data analysis. AIoT solutions are uniquely positioned to meet this demand, enabling industries to make real-time, data-driven decisions. This is particularly crucial in sectors where operational efficiency and agility are key to success.
One of the key drivers of the AIoT market is the introduction of 5G technology, which offers faster data transfer rates and reduced latency. These capabilities are essential for AIoT applications such as autonomous vehicles, smart cities, and other high-bandwidth, low-latency environments, where real-time communication and quick decision-making are critical.
Industry 4.0 and automation trends are also playing a pivotal role in driving the AIoT market forward. AIoT solutions are transforming manufacturing by enabling predictive maintenance, optimizing inventory management, and improving production processes. By incorporating AIoT, industries can predict equipment failures, reduce downtime, and improve overall efficiency.
Major Highlights of the AIoT Market
The core highlight of the AIoT market is the powerful combination of artificial intelligence (AI) and the Internet of Things (IoT). This integration enables more efficient data collection, processing, and real-time decision-making, providing organizations with actionable insights and operational improvements. AI enhances IoT’s capabilities, making connected devices smarter and more responsive.
As IoT devices proliferate, the demand for real-time data analysis has surged. AIoT solutions address this need by enabling faster and more accurate processing of the vast amounts of data generated by IoT devices. This capability is crucial in industries where real-time responsiveness and data-driven decision-making are essential, such as healthcare, transportation, and manufacturing.
The rollout of 5G technology is a game changer for the AIoT market. With its high data transfer speeds and low latency, 5G unlocks the potential of AIoT applications, particularly in areas like autonomous vehicles, smart cities, and advanced robotics. The enhanced connectivity allows AIoT systems to operate more efficiently and in real-time, significantly expanding the possibilities for AI-driven automation.
The rise of Industry 4.0 and the move toward automation in manufacturing are key drivers of AIoT adoption. AIoT technologies are transforming production processes by enabling predictive maintenance, minimizing equipment downtime, and optimizing supply chain and inventory management. These smart solutions are helping manufacturers increase productivity, reduce costs, and improve operational efficiency.
Ongoing advancements in IoT sensors and AI algorithms are enhancing the functionality of AIoT systems. These innovations allow for more precise monitoring, improved data accuracy, and better predictive capabilities, leading to smarter decision-making in real-time. These technological improvements are helping drive AIoT adoption in sectors like healthcare, energy, and agriculture.
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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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