This report outlines how the convergence of Artificial Intelligence and the Internet of Things is reshaping data requirements, infrastructure needs, and operational models. AI’s dependence on real-world data elevates the role of connected devices, while the rise of AIoT is accelerating the shift of intelligence toward the edge. This creates increased pressure on connectivity to manage larger data volumes, stricter latency demands, and more complex regulatory and security environments. At the same time, organisations seeking to monetise AI are turning to IoT to create tangible services and improved efficiency, reinforcing the strategic value of optimised connectivity. The report finds that IoT connectivity must evolve significantly to meet these new requirements. Next-generation solutions must be designed to be secure across all layers, compliant with fast-moving regulatory frameworks, and flexible enough to operate across global networks and multiple technologies. They must also support deep interoperability across heterogeneous device fleets, orchestrate distributed AI workloads between edge and cloud environments, and enable collaboration across the IoT stack and between connectivity providers. In addition, platforms must become more user-friendly through unified management, automation, and operational simplification. These capabilities collectively define the “Intelligent Last Mile” required for AI-enabled IoT systems. The sections of the report examine: the growing interdependence of AI and IoT; the challenges of delivering data for AI; the functional requirements of the Intelligent Last Mile, including security, compliance, flexibility, interoperability, orchestration, collaboration, usability, resilience, scalability, efficiency, determinism, and observability; and a worked example showing how these principles apply to connected cars.
This report outlines how the convergence of Artificial Intelligence and the Internet of Things is reshaping data requirements, infrastructure needs, and operational models. AI’s dependence on real-world data elevates the role of connected devices, while the rise of AIoT is accelerating the shift of intelligence toward the edge. This creates increased pressure on connectivity to manage larger data volumes, stricter latency demands, and more complex regulatory and security environments. At the same time, organisations seeking to monetise AI are turning to IoT to create tangible services and improved efficiency, reinforcing the strategic value of optimised connectivity.
The report finds that IoT connectivity must evolve significantly to meet these new requirements. Next-generation solutions must be designed to be secure across all layers, compliant with fast-moving regulatory frameworks, and flexible enough to operate across global networks and multiple technologies. They must also support deep interoperability across heterogeneous device fleets, orchestrate distributed AI workloads between edge and cloud environments, and enable collaboration across the IoT stack and between connectivity providers. In addition, platforms must become more user-friendly through unified management, automation, and operational simplification. These capabilities collectively define the “Intelligent Last Mile” required for AI-enabled IoT systems.
The sections of the report examine: the growing interdependence of AI and IoT; the challenges of delivering data for AI; the functional requirements of the Intelligent Last Mile, including security, compliance, flexibility, interoperability, orchestration, collaboration, usability, resilience, scalability, efficiency, determinism, and observability; and a worked example showing how these principles apply to connected cars.
