This report examines the growing convergence of Artificial Intelligence and the Internet of Things, which has emerged recently under the concepts of ‘Physical AI’ and ‘AIoT’. AI has the potential to be a major driver of IoT adoption by increasing the value of connected assets, enabling greater automation, improving decision-making, and creating new applications across consumer, enterprise, and industrial environments. The report explores how AI processing is increasingly moving from centralised cloud environments to edge devices, reducing latency, improving privacy, lowering connectivity costs, and enabling real-time responses. The report also analyses the broader implications of the convergence of AI and IoT for infrastructure, hardware, connectivity, software platforms, and operational processes. It examines the growing importance of cloud-to-edge orchestration, specialised AI chipsets, AIoT platforms, and evolving cellular architectures designed to support distributed intelligence. In addition, it considers how AI can improve the operation of IoT businesses themselves through automation, analytics, and operational optimisation. While the opportunities are substantial, the report identifies significant challenges, including device heterogeneity, security risks, model management, regulatory requirements, and organisational complexity.
This report examines the growing convergence of Artificial Intelligence and the Internet of Things, which has emerged recently under the concepts of ‘Physical AI’ and ‘AIoT’. AI has the potential to be a major driver of IoT adoption by increasing the value of connected assets, enabling greater automation, improving decision-making, and creating new applications across consumer, enterprise, and industrial environments. The report explores how AI processing is increasingly moving from centralised cloud environments to edge devices, reducing latency, improving privacy, lowering connectivity costs, and enabling real-time responses.
The report also analyses the broader implications of the convergence of AI and IoT for infrastructure, hardware, connectivity, software platforms, and operational processes. It examines the growing importance of cloud-to-edge orchestration, specialised AI chipsets, AIoT platforms, and evolving cellular architectures designed to support distributed intelligence. In addition, it considers how AI can improve the operation of IoT businesses themselves through automation, analytics, and operational optimisation.
While the opportunities are substantial, the report identifies significant challenges, including device heterogeneity, security risks, model management, regulatory requirements, and organisational complexity.
Table of contents
1. Five key trends defining the intersection of AI and IoT
2. AI as a driver for IoT
2.1 Example use cases
2.2 Real-time vs batch vs exhaust
3. AI on board IoT devices (AIoT)
3.1 Why deploy AIoT?
3.2 Examples of AIoT-enabled propositions
3.3 Potential for AIoT
3.4 AIoT not all the same
3.5 Managing AIoT in the field
3.6 (Some of) the complexities of Agentic AI
4. Supporting infrastructure and cloud-to-edge processing
4.1 Managing workloads
4.2 AIoT platform requirements
4.3 Impact on network traffic management
5. AIoT hardware
5.1 AIoT chips
5.2 Opportunities for AIoT-optimised propositions
6. Using AI to support IoT operations
7. The example of AI-enabled IoT video analytics
8. Potential speed bumps in the adoption of AI in IoT
9. Conclusions and recommendations
10. Further Reading
11. About Transforma Insights
