Scaling the impact of AI and IoT at the edge in the modern business ecosystem.

AI and IoT are technologies in today's digital world all set to transform every aspect of a business and society more profoundly. Since most enterprises focus their main resources on engineering their product, application software, or system, these technologies are likely to revolutionize their way of performance. The power of AI is significantly intensifying the widespread adoption of IoT as businesses seek to derive greater value from the vast datasets collected by connected devices.

While companies are pouring massive capital towards digitization, they are implementing AI into their IoT strategy, assessing potential new IoT projects, and seeking to reap more value from an existing IoT deployment. Applications of AI for IoT can enable companies to dodge unexpected downtime, boost operating efficiency, spawn new products and services, and improve risk management.

Achieving Business Value 

The emergence of AI-powered IoT solutions has the potential to help improve operational efficiency. They can also foresee operating conditions and recognize parameters to maintain relevant outcomes by crunching constant streams of data. IoT solutions are deployed to find patterns which can be difficult to see with the human eye. Thus, in this way, an AI-driven IoT solution gets implemented effectively and profitably.

IoT solutions produce large volumes of data, moving, storing and evaluating these voluminous data can be challenging for companies. This is where the Edge has a role to play. It can be quite wide and could mean anything from the edge of a gateway to an endpoint. An AI-based edge solution is able to identify and alleviate points of failure, poor performance and human error.

According to Wolfgang Furtner, senior principal for concept and system engineering at Infineon Technologies, 'The term edge AI inherits its vagueness from the term 'edge' itself.' "Some people call a car an edge device, and others are using the term for a small energy-harvesting sensor with low-power wireless connectivity. Edge is used in relative ways and distinguishes the more local from the more central. But indeed, there is a need to distinguish between the various kinds of things that you find at the edge. Sometimes, you hear terms like 'edge of the edge' or 'leaf nodes.' Edge AI can be many things, including a compute server in a car," he said. "The key is that endpoint AI resides at the location where the virtual world of the network hits the real world, where sensors and actuators are close."

Why AI at the Edge?

Edge AI is typically a self-reliant intelligence likely to dominate the market of semi-autonomous cars and smart retail systems. By leveraging AI at the edge costs for data communication will significantly be reduced. It will enable real-time operations including data creation, decision and action. Real-time operations are crucial for autonomous cars, robots and many other areas.

Most AI applications require a lot of computational power in order to process algorithms and device data. However, there is also a need for edge computing architecture when real-time response and low latency is critical. By leveraging AI at the edge, companies can detect and mitigate maintenance and repair issues. They can also make predictions to optimize the maintenance schedule to avoid redundant machine servicing.

Comprehensively, Edge AI with IoT solutions is becoming a reality across every industry application. And it is likely to benefit through predictive and preventive maintenance, quality control, and downtime.