AI is acting as a driver for IoT adoption, says Transforma Insights in a report looking at the intersection of the two technologies, known as AIoT, and now manifesting itself under the concept of physical AI.

The report focuses on AI as a driver for IoT, AI on board IoT devices, supporting infrastructure and cloud-to-edge orchestration, AIoT hardware, and the use of AI to support IoT operations.

One of the main reasons AI is seen as being so interesting for IoT is it will act as a driver for IoT adoption. Clearly, given AI’s ability to crunch large volumes of data, there are significant implications for removing some of the barriers to deploying IoT. Furthermore, there is greater value derived in AI at the point where the physical and digital worlds intersect, where IoT operates, for instance in autonomous driving, defect detection, health and safety.

AI-enabled IoT has the potential to create more tangible value than many purely digital AI applications, as well as reducing the cost of deployment. However, there are challenges. IoT is already a multi-disciplinary exercise involving hardware, software and connectivity. Adding AI into the mix isn’t going to make it any easier to roll out.

One of the key elements of the convergence of AI with IoT is the increasing deployment of AI onto the edge device itself, known as AIoT. Applying AI to IoT data on board the source IoT devices can bring significant benefits, including improved performance, enhanced compliance, privacy and security, and potentially reduced operational costs. The report examines the motivations for deploying AI onto IoT devices, examples of propositions, the complexities of managing it in the field, and the need to balance edge and cloud processing.

In quantitative terms, based on Transforma’s AIoT forecasts, total AIoT connections will grow from 1.8bn at the end of 2024 to 11.3bn at the end of 2035.

The advent of more AI on IoT devices has a number of implications for supporting infrastructure. It reduces raw data streaming to the cloud and necessitates frequent updating and lifecycle management, often across heterogeneous fleets. It changes the nature of data storage architectures and increases the importance of security and governance issues. This necessitates a change in approaches to traffic management. As a result, there is an increasing requirement for supporting infrastructure, most prominently AIoT platforms featuring a range of new functionality.

The report also examines the implications for hardware with the increasing prevalence of AI-optimised hardware, and the use of AI internally by IoT suppliers to support their operations. It considers some of the key challenges that will hinder deployment, including model management, security, resource constraints in IoT devices, technical complexity, and the difficulties of integrating such deployments into business processes.

For more on the report, visit transformainsights.com/research/reports/ai-iot-implications-convergence-technology-domains.