According to Gartner, the number of enterprise IoT endpoints could reach 5.8 billion units by the end of this year (up from 4.8 billion in 2019). All these devices produce terabytes of data that could help businesses discover and eliminate inefficiencies in their workflows. Yet 73% of enterprise data goes unused for analytics.
Most companies fail to collect and process data coming from IoT devices because of the excessive amount of that data, obsolete or unreliable data acquisition tools, and flawed data analytics practices.
With the introduction of edge computing and cloud platforms with AI capabilities, businesses get an opportunity to uncover additional insights in IoT data that would otherwise get lost, and thus drive more value from existing IoT deployments.
Here’s what you need to know about the Artificial Intelligence of Things (AIoT)—a powerful combination of connected devices and intelligent data processing algorithms.
The Internet of Things (IoT) is a multi-level system where devices and non-electronic objects collect telemetry data using sensors. The things then transmit the data to the cloud over wireless communication protocols.
Artificial Intelligence (AI) is an umbrella term that describes miscellaneous IT systems where algorithms interpret information and make smart predictions.
When we merge AI with IoT, we get connected devices that gather, analyze, and act on sensor data with little to no human involvement, and adapt to the current environment around them.
The Artificial Intelligence of Things exists in two forms:
Choosing an AIoT implementation strategy depends on the gadget’s performance requirements.
An IoT gateway that captures information from soil moisture sensors, for example, doesn’t have to relay data to the cloud every minute. On the contrary, a smart heart monitor cannot possibly wait for a command from the cloud server to realize that a patient’s condition is deteriorating; instead, the gadget needs to make instant decisions based on real-time heart rate data.
Until recently, the CPUs capable of performing data analysis closer to the network's edge were scarce. But the chip industry has made an enormous step forward and is now cutting down CPU costs while maintaining their high performance. The only issue hardware manufacturers have yet to solve is CPU versatility. The Artificial Intelligence of Things solutions may vary in forms and applications, and therefore have different performance requirements. To deploy AIoT solutions at scale, we need integrated circuits that support multiple combinations of computing tasks, including AI-driven data analysis, digital signal processing, and remote device control among others.
In the IoT context, the value of Artificial Intelligence lies in its ability to quickly parse and discern insights from mountains of data that has been previously reviewed by humans.
Without a mobile app, there’s no way you could view your body composition data from a Bluetooth smart scale. Alexa is good at shuffling playlists on Spotify; if you need to book a flight though, you’d better double-check the information from a PC or smartphone.
The Internet of Things and Artificial Intelligence are a perfect example of technologies that complement each other.
When combined, they help businesses maximize ROI on their IoT investments in multiple ways because they can:
By 2022, 80% of enterprise IoT deployments will have an AI component. If you’re thinking of developing an IoT solution today, make sure it works with AI. In case you’re training a custom Machine Learning model, think about how it could benefit from IoT data and connectivity.