AI enhances embedded systems by adding intelligence and autonomy to devices, enabling them to make decisions and adapt to changing conditions without human intervention. This is achieved through the integration of AI algorithms and models into the embedded system's hardware and software.

One example is in autonomous vehicles, where AI algorithms process sensor data to make real-time decisions such as steering, braking, and acceleration. In industrial automation, AI can optimize production processes by analyzing data from sensors and making adjustments to improve efficiency and reduce downtime.

AI also enables embedded systems to learn from data and improve over time. For example, in smart home devices, AI algorithms can learn user preferences and adjust settings automatically. This capability is essential for the development of intelligent systems that can anticipate and respond to user needs.

In conclusion, AI is a key enabler of enhanced functionality and performance in embedded systems, allowing them to operate more autonomously and intelligently. As the demand for AI-powered embedded systems grows, acquiring skills through an embedded system certification course can be beneficial for aspiring professionals in this field.