Artificial Intelligence of Things

Global Artificial Intelligence of Things Market to Reach US$51.3 Billion by 2030

The global market for Artificial Intelligence of Things estimated at US$14.1 Billion in the year 2024, is expected to reach US$51.3 Billion by 2030, growing at a CAGR of 24.0% over the analysis period 2024-2030. Software / Platforms, one of the segments analyzed in the report, is expected to record a 20.8% CAGR and reach US$17.7 Billion by the end of the analysis period. Growth in the Services segment is estimated at 26.7% CAGR over the analysis period.

The U.S. Market is Estimated at US$3.7 Billion While China is Forecast to Grow at 22.9% CAGR

The Artificial Intelligence of Things market in the U.S. is estimated at US$3.7 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$7.9 Billion by the year 2030 trailing a CAGR of 22.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 21.5% and 21.1% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 16.9% CAGR.

Global Artificial Intelligence of Things (AIoT) Market – Key Trends & Drivers Summarized

Why Is the Convergence of AI and IoT Unlocking Next-Generation Intelligence Across Devices and Systems?
The Artificial Intelligence of Things (AIoT)—the fusion of Artificial Intelligence (AI) and the Internet of Things (IoT)—is redefining the way devices, machines, and systems interact by enabling real-time, autonomous decision-making at the edge and in the cloud. IoT provides the sensing and connectivity infrastructure, while AI brings the cognitive ability to analyze data, recognize patterns, predict outcomes, and optimize operations without human intervention. This convergence allows for a shift from reactive automation to proactive intelligence, where devices can learn, adapt, and self-optimize based on contextual insights.

AIoT is fueling a new generation of applications across industries such as manufacturing (predictive maintenance, process automation), healthcare (remote diagnostics, smart wearables), smart cities (traffic control, energy optimization), and agriculture (climate monitoring, autonomous irrigation). By embedding AI models directly into edge devices—such as cameras, sensors, and robots—systems can execute decisions faster, reduce data transmission requirements, and maintain performance even with limited or intermittent connectivity. As digital transformation intensifies globally, AIoT is becoming a foundational layer in building smart, scalable, and resilient cyber-physical systems.

How Are Edge AI and Cloud Intelligence Powering the Evolution of AIoT Architectures?
The advancement of edge computing, low-power AI chips, and distributed machine learning is significantly shaping AIoT deployment models. Edge AI enables data to be processed locally, reducing latency, bandwidth consumption, and cloud dependency—essential for time-sensitive applications like autonomous vehicles, industrial robots, and smart surveillance. Purpose-built AI processors, such as Nvidia Jetson, Intel Movidius, and Google Coral, are allowing real-time inferencing at the edge with minimal energy consumption, enabling scalable AIoT implementations in constrained environments.

At the same time, cloud-based AI provides the computational heft for model training, historical analytics, and system-wide orchestration. Hybrid AIoT architectures leverage both edge and cloud capabilities to balance performance and scalability: the edge handles immediate decision-making, while the cloud oversees system learning, long-term optimization, and model retraining. Software platforms from AWS, Azure, and Google Cloud are enabling seamless device-to-cloud integration with support for edge analytics, digital twins, containerized applications, and federated learning. This architectural flexibility is allowing enterprises to deploy AIoT in diverse environments while ensuring cost-efficiency, security, and continuous improvement of AI models.

Where Is AIoT Adoption Accelerating and Which Use Cases Are Leading Market Growth?
AIoT adoption is accelerating globally, with high uptake across North America, Western Europe, and rapidly growing investment in Asia-Pacific. In the U.S., AIoT is central to industrial automation, smart home ecosystems, and logistics optimization. Europe is leveraging AIoT for smart city development, energy grid intelligence, and environmental monitoring, supported by the EU’s digital strategy and sustainability goals. Asia-Pacific—particularly China, Japan, South Korea, and India—is witnessing rapid deployment in smart manufacturing, public safety, and precision agriculture, driven by large-scale infrastructure investments and government-backed digital initiatives.

Leading use cases include predictive maintenance in manufacturing, where AIoT systems analyze sensor data to forecast equipment failure and schedule proactive maintenance, thereby reducing downtime. In retail, AIoT enables real-time inventory management, shopper analytics, and personalized promotions using in-store sensors and edge computing. In smart homes, voice assistants and connected appliances are integrating AI for adaptive control and user behavior learning. Logistics companies use AIoT for route optimization, fleet management, and cold chain monitoring. Meanwhile, in healthcare, AIoT is enabling continuous patient monitoring, early anomaly detection, and intelligent hospital resource allocation—making it a vital component of remote and preventive care models.

What Is Fueling the Global Growth of the Artificial Intelligence of Things (AIoT) Market?
The growth in the AIoT market is driven by a confluence of accelerating digital transformation, increasing sensor proliferation, and the need for autonomous, intelligent systems that operate in real-time. A critical driver is the exponential growth of IoT data, which requires AI capabilities to derive actionable insights and automate responses. Businesses are investing in AIoT to enhance operational efficiency, reduce human error, cut costs, and gain competitive differentiation through data-driven innovation. Edge AI advancements, lower hardware costs, and the maturation of 5G and LPWAN connectivity are further enabling scalable AIoT deployments in both consumer and industrial domains.

Government funding, smart infrastructure mandates, and industry-specific standards (such as Industry 4.0, ISO/IEC IoT security frameworks) are supporting the adoption of AIoT across regions. Additionally, open-source AI frameworks, interoperable platforms, and digital twin technologies are simplifying development and integration processes. As AIoT ecosystems expand, with more devices becoming intelligent and interconnected, a pivotal question arises: Can the global AIoT ecosystem deliver truly autonomous, secure, and adaptive intelligence at scale—across industries, geographies, and mission-critical environments?

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence of Things market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Software / Platforms, Services, Professional, Managed); End-Use (BFSI, Retail, Automotive, Transportation & Logistics, Healthcare, Energy & Utilities, Manufacturing, Other End-Uses)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 42 Featured) -

• AIoT Holdings
• AIoT Inc.
• AItoAir
• APLUX
• Aritium Technologies
• Butlr Technologies
• Coderitter
• easemyai
• EPIC iO
• Espressif Systems
• Fogsphere
• GE Digital
• iCHASE
• Intel Corporation
• Kontakt.io
• m`AI Touch Technology
• Microsoft Corporation
• Moon Innovations
• Powerfleet
• Qorvo

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