AIoT Market: Growing Trends, Future Outlook, Size, Share, Advance Technology And Forecast - 2030

AIoT Market by Technology (ML, NLP, Computer Vision, Context Aware AI) and Platforms (IoT Device Management, IoT Application Enablement Platforms, IoT Connectivity Management, IoT Cloud, IoT Advanced Analytics) - Global Forecast to 2030.
The size of the worldwide AIoT market [https://www.marketsandmarkets.com/Market-Reports/ai-in-iot-market-43388726.html?utm_campaign=aiiniotmarket&utm_source=abnewswire.com&utm_medium=paidpr] is expected to increase at a Compound Annual Growth Rate (CAGR) of 27.6% from USD 18.37 billion in 2024 to USD 79.13 billion by 2030. The effectiveness of data processing, analysis, and decision-making in the real-time operations of various industries has been improved by a number of advancements made in the fields of AI algorithms and IOT sensors. The demand for quicker and real-time analysis of the data created by the increasing number of IoT devices in our society has put pressure on the development of AI solutions for operations.

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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

The AIoT market uniquely thrives on the seamless integration of Artificial Intelligence (AI) with the Internet of Things (IoT). Unlike traditional IoT systems that rely solely on data collection and transfer, AIoT enables real-time data analysis, decision-making, and autonomous actions. This convergence enhances automation and allows machines and systems to learn from data patterns without human intervention, making AIoT solutions highly adaptive and intelligent.

A defining feature of the AIoT market is its shift toward edge computing, where AI models process data locally on IoT devices instead of relying entirely on the cloud. This drastically reduces latency, increases response time, enhances data privacy, and ensures uninterrupted operations even in low-connectivity environments. Edge AI enables applications in autonomous vehicles, smart cameras, and industrial robots to function with high precision and low delay.

AIoT systems are uniquely capable of not just monitoring but also predicting and prescribing actions based on data insights. In industrial settings, AIoT can anticipate equipment failures, optimize maintenance schedules, and improve operational efficiency. This proactive approach leads to cost savings, enhanced safety, and minimized downtime-setting AIoT apart from legacy automation systems.

AIoT solutions are highly adaptable across various industries such as manufacturing, agriculture, healthcare, smart cities, logistics, and energy. This flexibility stems from AI's ability to tailor decision-making models based on specific use cases and IoT's capability to capture domain-specific data. The AIoT market thus supports both broad deployment and niche applications with industry-grade customization.

Major Highlights of the AIoT Market

The AIoT market is experiencing exponential growth driven by increased investments from tech giants and startups alike. Organizations across the globe are recognizing the value of integrating AI with IoT to drive automation, improve operational efficiency, and unlock new revenue streams. This trend is fueling the development of advanced AIoT platforms, tools, and devices, making the market highly competitive and innovation-driven.

AIoT adoption is expanding rapidly across various sectors, including manufacturing, healthcare, agriculture, retail, energy, logistics, and smart cities. In manufacturing, AIoT enables predictive maintenance and production optimization. In healthcare, it supports remote monitoring and intelligent diagnostics. This cross-industry applicability underscores the market's versatility and broad impact.

Governments and municipalities are increasingly adopting AIoT to build smarter, more efficient urban infrastructures. From intelligent traffic management and energy-efficient buildings to waste management and public safety systems, AIoT technologies are transforming urban living. This demand is a significant growth driver for the AIoT market, particularly in emerging economies.

There is a strong market shift toward deploying AI algorithms at the edge, enabling faster decision-making and reducing reliance on cloud connectivity. Real-time analytics powered by AI at the edge is particularly valuable in mission-critical applications such as autonomous vehicles, industrial automation, and smart surveillance, contributing to the market's technological advancement.

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Top Companies in the AIoT Market

The major vendors covered 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), Tencent Cloud (China), Sharp Global (Japan), SAS (US), PTC (US), Telit Cinterion (UK), Robert Bosch (Germany), Axiomtek (Taiwan), Softweb Solutions (US), Wiliot (Isra6el), Relayr (US), Terminus Group (China), C3 IoT (US), Clearblade (US), Semifive (South Korea), Falkonry (US), Uptake (US), Butlr (US).

IBM (US)

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 (US)

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.

Hitachi (Japan)

Hitachi (Japan) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) through its AIoT solutions, enabling smart manufacturing, predictive maintenance, and energy optimization. Its Lumada platform leverages advanced analytics and machine learning to drive digital transformation across industries.

SAP (Germany)

SAP (Germany) integrates AI and IoT (AIoT) to enhance business processes through its SAP IoT and AI-driven analytics solutions. By leveraging real-time data and intelligent automation, SAP helps industries optimize operations, improve predictive maintenance, and drive digital transformation.

AWS (US)

AWS (US) combines AI and IoT through its AIoT solutions, offering services like AWS IoT Greengrass and AWS AI/ML tools to enable intelligent edge computing and real-time data analytics. These solutions help businesses automate processes, enhance decision-making, and optimize operations across industries like manufacturing, smart cities, and healthcare.

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