What is ‘Edge AI’? What does it do and what can be gained from this alternative to cloud computing?
On March 24 2026
“Edge computing”, which was initially developed to make big data processing faster and more secure, has now been combined with AI to offer a cloud-free solution. Everyday connected appliances from dishwashers to cars or smartphones are examples of how this real-time data processing technology operates by letting machine learning models run directly on built-in sensors, cameras, or embedded systems.
Homes, offices, farms, hospitals and transportation systems are increasingly embedded with sensors, creating significant opportunities to enhance public safety and quality of life.
Indeed, connected devices, also called the Internet of Things (IoT), include temperature and air quality sensors to improve indoor comfort, wearable sensors to monitor patient health, LiDAR and radar to support traffic management, and cameras or smoke detectors to enable rapid-fire detection and emergency response.
These devices generate vast volumes of data that can be used to ‘learn’ patterns from their operating environment and improve application performance through AI-driven insights.
For example, connectivity data from wi-fi access points or Bluetooth beacons deployed in large buildings can be analysed using AI algorithms to identify occupancy and movement patterns across different periods of the year and event types, depending on the building type (e.g. office, hospital, or university). These patterns can then be leveraged for multiple purposes such as HVAC optimisation, evacuation planning, and more.
Combining the Internet of things and artificial intelligence comes with technical challenges
Artificial Intelligence of Things (AIoT) combines AI with IoT infrastructure to enable intelligent decision-making, automation, and optimisation across interconnected systems. AIoT systems rely on large-scale, real-world data to enhance accuracy and robustness of their predictions.
To support inference (that is, insights from collected IoT data) and decision-making, IoT data must be effectively collected, processed, and managed. For example, occupancy data can be processed to infer peak usage times in a building or predict future energy needs. This is typically achieved by leveraging cloud-based platforms like Amazon Web Services, Google Cloud Platform, etc. which host computationally intensive AI models – including the recently introduced Foundation Models.
Read the full article on the Institut Mines-Télécom’s I’MTech blog.
