From "Connecting Everything" to "Intelligent Everything": AIoT Enters a New Stage of Terminal Intelligence

If you follow the IoT industry, you'll notice a clear shift: a few years ago, the discussion centered on "how many devices are connected" and "how many scenarios are covered." Now the conversation is turning to "what can terminals do" and "where is data processed." Behind this shift is the fact that the integration of AI and IoT is moving from concept to implementation.

In 2026, both policy signals and industry dynamics in the IoT sector point in the same direction: IoT is moving from "connecting everything" to "intelligent everything."

Policy Direction: AI and IoT Integration Becomes a Clear Path

In March this year, the Ministry of Industry and Information Technology and eight other departments jointly issued the "Action Plan for Promoting Innovative Development of the IoT Industry (2026–2028)." The plan sets out two key figures: by 2028, IoT terminal connections should reach the tens of billions scale, and the core IoT industry scale should exceed 3.5 trillion yuan.

More noteworthy than the numbers is the path the policy points to. The plan explicitly calls for accelerating the deep integration of AI, 5G, human-computer interaction, edge computing, and other technologies with IoT application terminals. This means future IoT terminals will no longer be mere "data movers"—they will need a certain level of local processing capability.

The plan also gives clear direction on application scenarios. In the production sector, it focuses on IoT integration in agriculture, energy, transportation, and modern logistics. In the consumer sector, it relies on smartphones, wearables, intelligent connected vehicles, and robots to innovate scenarios such as vehicle-home interconnection, smart supermarkets, and telemedicine.

Industry Response: The Transition from "Connectivity" to "Intelligent Connectivity"

Following the policy direction, industry responses came quickly.

In June 2026, China Unicom released its AIoT ecosystem co-creation strategy, building an end-to-end capability system around four major segments: connectivity, chip modules, platform, and applications. Its Gewu platform was upgraded to version 2.0, with core capabilities including "ubiquitous intelligent foundation, AI-native perception engine, and elastically orchestrated architecture." The direction is clear: the platform no longer just manages connectivity—it manages data and intelligence.

Technology is also changing. A research team at the University of Helsinki in Finland recently demonstrated a hierarchical multi-agent architecture, where a supervisory agent on an edge device assigns tasks to worker agents deployed on Raspberry Pis, using retrieval-augmented generation (RAG) to "anchor" large language model outputs to actual sensor data. Under normal operation, the system achieves 100% accuracy; even with one agent offline, it maintains 88.9% accuracy.

The significance of this experiment: it validates the feasibility of distributed AI inference in resource-constrained indoor environments. For industrial scenarios, this means intelligent processing closer to the data source is becoming a reality.

New Requirements for Terminal Devices: More Than Just "Can Connect"

As AI capabilities sink to terminals, requirements for the devices themselves are changing.

In the past, when selecting industrial routers or gateways, the core metrics were "can it connect stably" and "are there enough interfaces." Now, more and more projects are focusing on whether devices have local data processing capability and whether they support deployment of edge computing tasks.

Take the MovingComm ComIn I3500 5G industrial router as an example. This product integrates Wi-Fi transmission, cellular mobile internet access, precise positioning, serial port pass-through, and remote backend management. It supports NSA and SA 5G network standards, equipped with full gigabit ports and a dual-core processor. In the AIoT context, such hardware configuration means it can not only complete data backhaul but also carry certain data processing tasks at the edge.

The MovingComm ComIn I500 WiFi 6 5G industrial router goes further, adopting 2x2 dual-band Wi-Fi 6 technology with dual-band concurrent rates up to 3000Mbps, equipped with 256MB memory and 32MB SPI Flash. Larger memory means it can run more complex edge applications, while Wi-Fi 6's low-latency characteristics are more friendly to industrial control scenarios.

What these products share: industrial-grade design (wide voltage, watchdog, metal housing) as the foundation, while leaving margin in processing performance, memory capacity, and wireless speed to adapt to the "terminal intelligence" trend.

Scenario Implementation: Computing Power Is Migrating to the Edge

From specific scenarios, the integration of AI and IoT has already produced practical impact across multiple industries.

In manufacturing, predictive maintenance is considered one of the fastest-growing AIoT applications. By deploying vibration, temperature, and acoustic sensors at the device end, combined with edge AI models for real-time analysis, warnings can be issued before failures occur, reducing unplanned downtime.

In the energy and power industries, the need for local processing capability is particularly prominent at distributed sites and remote scenarios. Data doesn't need to be entirely transmitted back to the cloud; instead, preliminary screening and judgment are completed at the edge, with only key information sent back. This not only reduces bandwidth costs but also shortens response time.

In high-risk industries such as mining and petrochemicals, the combination of 5G private networks and edge AI is changing operational methods. Scenarios such as remote control, intelligent inspection, and personnel positioning have high requirements for latency and reliability, and the deployment of edge computing nodes provides the foundation for these applications to land.

A Change That Is Happening

The IoT industry is undergoing a shift in thinking. Over the past decade, the keyword was "connectivity"—connecting devices to the network, transmitting data upward. Now, the keyword is becoming "intelligence"—letting terminals judge, letting the edge decide.

The impact of this shift on device selection is direct. For teams planning or upgrading IoT projects, industrial routers and gateways—these "access layer" devices—are no longer just communication pipes but are gradually becoming carriers of edge intelligence. Whether the device's memory is enough to run a lightweight model, whether the processor can handle real-time data, whether network latency meets control requirements—these questions may not have been selection priorities a few years ago, but they are becoming increasingly critical now.

Policy is pushing, technology is evolving, scenarios are landing. The path of AIoT from concept to scale is becoming clearer.