Artificial Intelligence (AI) and the Internet of Things (IoT) are two of today’s most impactful technologies. Increasingly, they’re being combined to become what’s widely referred to as AIoT. In this blog post we review the arrival of AIoT, examine the key benefots and look at the growth in connections.
IoT devices generate large volumes of potentially valuable data. As illustrated below, AI can be applied to this data across various locations: in the cloud, at the network edge, and directly on the IoT devices themselves. Each approach offers distinct opportunities to unlock insights and drive value from IoT-generated data.
When we refer to AIoT, we specifically mean the deployment of AI use cases directly on IoT devices.
Running AI on-device brings a range of benefits including faster performance, improved compliance, enhanced privacy and security, and potentially lower operational costs. These advantages are explored further below.
Alternatively, deploying AI in the cloud enables access to far greater computing power and allows for more complex use cases. AI in the cloud can aggregate data from multiple devices across locations and enrich these inputs with other datasets, including non-IoT sources, to support deeper analysis. It also simplifies AI application management, particularly in terms of version control and continuous algorithm updates.
In practice therefore, AIoT rarely operates in isolation. Data generated by AI-enabled IoT devices is often further processed, either at the edge or in the cloud, using more advanced AI models, creating a layered approach to intelligence across the ecosystem.
As described above, the deployment of AI on board IoT devices can potentially unlock significant benefits including in terms of application performance, enhancing compliance, privacy and security, and reducing operational costs. In the following subsections we discuss these in more depth.
AIoT enhances IoT application performance by enabling local, high-resolution data analysis, reducing reliance on remote processing. For instance, AI-powered CCTV can analyse video feeds in real time on the device, eliminating the need to transmit large volumes of data. This improves detail and responsiveness while preserving bandwidth. By processing data closer to its source, AIoT delivers faster alerts and decisions which can be crucial for latency-sensitive applications like autonomous vehicles or industrial emergency-stop systems. It also boosts resilience, ensuring continued operation during network outages. This is vital for critical systems and also improves reliability and user experience for everyday applications like smart HVAC systems.
AIoT enhances privacy by enabling local data processing, ensuring personal or sensitive information doesn’t leave the device. For example, a smart speaker can interpret voice commands locally, avoiding the need to stream audio, potentially including private conversations, to the cloud. When remote processing is necessary, data can be anonymized before transmission. Similarly, AIoT-enabled surveillance systems can analyse video on-site, reducing or eliminating the need to stream footage elsewhere. In enterprise settings, local processing helps keep confidential data within organisational boundaries, improving compliance and security. This localised approach offers strong privacy protection while maintaining functionality and performance.
AIoT can significantly reduce operational costs across various fronts. By enabling advanced on-device monitoring, optimisation, and predictive maintenance, it enhances the overall performance and value of IoT applications. Local data processing minimises communication costs, especially for data-heavy applications using public cellular or satellite networks. AIoT also enables cost-effective deployments in low-activity or remote areas, such as using smart CCTV to monitor infrequently accessed industrial zones, transmitting data only when necessary and reducing the need for on-site personnel. Additionally, processing data at the edge reduces reliance on cloud infrastructure, cutting both computing and storage costs associated with remote analysis of large data volumes.
The growth potential for AIoT is substantial. According to forecasts from Transforma Insights, the number of AIoT connections is expected to surge from 1.4 billion at the end of 2023 to 9.1 billion by the end of 2033, as illustrated in the infographic, above. This represents more than a sixfold increase over the ten-year period, equating to a compound annual growth rate (CAGR) of over 20%. Such rapid expansion highlights the increasing importance of embedding artificial intelligence directly into IoT devices, enabling smarter, more responsive applications across consumer, industrial, and enterprise sectors.
This growth is not just in total volume but also in annual net additions, which are projected to rise from under 500 million in 2023 to more than 900 million by 2033. The accelerating adoption of AIoT reflects a broader shift toward distributed intelligence, where local processing enables faster decision-making, improved privacy, and reduced data transmission costs. As AI capabilities become more accessible and IoT hardware continues to advance, the AIoT market is positioned to play a central role in the next generation of connected solutions: driving innovation, improving efficiencies, and creating new value across a wide range of industries. The forecasted growth underscores the strategic significance of AIoT in the global technology landscape.
