Example Concept: Integrating AI models requires addressing compatibility between the AI model's requirements and the existing system's architecture. This includes ensuring data formats align, managing model inference latency to maintain performance, and handling model updates without disrupting the system. Additionally, developers must consider the security implications of integrating AI, such as data privacy and model robustness against adversarial inputs.