The “things” in the Internet of Things are getting smarter, with profound implications for organizations and the way people work and live.

It isn’t just the software making AI-powered edge devices possible. From a hardware standpoint, today's devices are increasingly capable of supporting the power and capacity requirements of AI algorithms. Sensors themselves are supporting significant memory and processing capacities within very tiny footprints, which is mitigating, if not eliminating, the need for transmitting data to central servers.
 
“Technical or energy constraints make it impossible to stream all that data to the cloud where the AI resides,” according to Rudi Cartuyvels, lead for Imec’s Smart Electronics & Applications R&D. “In addition, there are also use cases where patterns should be recognized instantaneously, such as with radars that need to detect people or vehicles in the path of a self-driving vehicle. There, the time delay of a round-trip to the cloud is simply prohibitive.”
 
Examples of applications benefitting from AI-charged edge computing include medical devices, manufacturing systems and vehicles. Medical devices, in particular, have an acute need for at-the-edge intelligence – critical data in the operating room, for example, has to be processed in a timely fashion in order to provide the right information for physicians or doctors to act upon.
 
Another potential area is machine vision, through cameras and visual analytics. For example, a camera can be positioned in a distribution facility to monitor and manage – in an instant – goods that are moving between trucks and pallets. With the combination of inexpensive and high-capacity hardware supporting intelligent software, organizations have only begun to explore the possibilities for achieving highly efficient operations and superior customer satisfaction.
 
The advantages of AI-enhanced decision-making at the edge include the following:
Edge-based AI is highly responsive, and closer to real time than the typical centralized IoT model deployed to date. Insights are immediately delivered and processed, most likely within the same hardware or devices.
Edge-based AI ensures greater security. Sending data back and forth with Internet-connected devices subjects data to tampering and exposure even without anyone being aware. Processing at the edge minimizes this risk, with an additional plus: Edge-based AI-powered devices can include enhanced security features. 
Edge-based AI is highly flexible. Smart devices support the development of industry-specific or location-specific requirements, from building energy management to medical monitoring.
Edge-based AI doesn't require a PhD to operate. Since they can be self-contained, AI-based edge devices don't require data scientists or AI developers to maintain. Required insights are either automatically delivered where they are needed, or visible on the spot through highly graphical interfaces or dashboards.
Edge-based AI provides for superior customer experiences. By enabling responsiveness through location-aware services, or rerouting travel plans in the event of delays, AI helps companies build trust and rapport with their customers.
 
As we move forward into the highly connected digital economy, inevitably, intelligence will move to the edge. The powerful combination of AI and the IoT opens up new vistas for organizations to truly sense and respond to events and opportunities around them.