What is the Internet of Things (IoT), and how does it relate to AI?

Explained

The Internet of Things (IoT) is the set of physical devices, from sensors and meters to machines and vehicles, that measure something about the world and send that data over a network. On its own, IoT produces streams of readings; AI is what turns them into decisions. IoT data feeds predictive maintenance, anomaly detection, demand forecasting and autonomous systems in manufacturing, logistics, energy and smart buildings. The combination is sometimes called AIoT, and it increasingly runs part of the AI on the device or at the edge, close to where the data is produced.

How IoT and AI fit together

• Sensing: devices measure temperature, vibration, location, consumption, images or other signals.
• Communication: readings travel over networks to a gateway, an edge server or the cloud.
• Processing: data is cleaned, stored and analysed, often in real time.
• Decision and action: AI models detect patterns, predict events and trigger actions, from an alert to an actuator that changes a setting.

NIST describes these systems as combining sensing, computing, communication and actuation.

Enterprise use cases

• Predictive maintenance of machines, vehicles and infrastructure.
• Quality control on production lines with cameras and sensors.
• Tracking assets and shipments across the supply chain.
• Energy and building management that adjusts heating, cooling and lighting to use.
• Remote monitoring of equipment in the field, such as pumps, meters or turbines.

What to watch

IoT multiplies the number of devices connected to the network, and each one is a possible entry point. Security needs to cover device identity, firmware updates and network segmentation. Data quality is the other common issue: sensors drift, fail or send gaps, and models trained on that data inherit the problem. Deciding what to process on the device and what to send to the cloud is also a trade-off between latency, cost and privacy.

Frequently asked questions

How to put it to work

• How to do predictive maintenance with AI

Further reading

• Efficient On-Device AI: The New Frontier for Autonomous Systems

Related terms

• Automation Having software carry out tasks without a person doing each step, increasingly with AI to handle decisions and unstructured inputs.
• Autonomous Systems Systems that sense their environment, decide and act on their own in real time, without a person supervising every step.
• Big Data Data sets so large, fast-moving or varied that traditional databases and tools cannot store or process them efficiently; a key input for AI.
• Context Engineering The practice of deciding what information a model sees at each step (instructions, retrieved documents, tool results, memory) and fitting it into the context window.
• Corpus A collection of documents or texts used to train a model or to serve as the knowledge base an AI system retrieves from, as in RAG.
• Data Lake A central repository that stores structured and unstructured data in its raw form, as a source for analytics, machine learning and AI.