Convolutional Neural Networks (CNN), a class of deep, feed-forward artificial neural networks, are primarily used for processing images. They utilize variations of multilayer perceptrons to require minimal preprocessing. CNNs have revolutionized the field of image recognition, opening up new possibilities for innovations.

• General Applicability: CNN is widely used in the field of Artificial Intelligence (AI). It finds applications in diverse fields from face recognition to self-driving cars.
• Efficiency: CNNs make fewer connections and parameters than traditional neural networks, making them more efficient in terms of pooling and convolution.
• Sophisticated Features: A key characteristic is the ability to automatically learn feature representations from raw data, eliminating the need for manual feature extraction.
• High Accuracy: CNN’s layers have a unique arrangement, enabling them to outperform other competing technologies in terms of accuracy.
• Scalability: CNN’s are scalable and can handle large amounts of data.

The implementation of a CNN involves the initial configuration of its layers and parameters. Then, the model is trained using an annotated dataset until it reaches satisfying performance levels.

To implement a Convolutional Neural Network successfully, it’s essential to understand the problem to be solved, the nature of the data, and the resources at your disposal. Adequate time and attention must be given to the training process, ensuring that the model does not overfit the training data. Consideration should also be given to the computational resources required, as this will impact the time and cost of the model's implementation.

• Efficiency in Handling Image Data: CNN’s are designed explicitly for image data, which has a three-dimensional input field. This structural advantage allows Convolutional Neural Networks to process large data volumes with less computational cost.
• Automatic Feature Learning: The deep learning capability enables automatic learning of useful features directly from primary data, eliminating the requirement of manual feature extraction.
• Real-time Processing: CNN’s ability to recognize patterns in an image facilitates real-time processing capabilities, vital for numerous AI applications.
• Better Performance: CNN’s have self-learning capability that can enhance their performance without any guidance.
• Versatility: CNN’s versatility enables them to be used in a variety of applications, like image and video recognition, recommendation systems, and natural language processing.

• High Resource Requirement: CNNs require a vast amount of computational resources and data to train effectively, which may pose significant challenges for organizations without sufficient infrastructure.
• Overfitting Risk: Overfitting is a common problem in machine learning, where a model performs exceptionally well on training data but poorly on unseen data. CNNs, due to their complexity, are prone to overfitting if not correctly trained.
• Complex Architecture: CNNs have a relatively complex architecture compared to other AI models, making them difficult to understand.
• Lack of Transparency: As with most machine learning models, CNNs are often criticized for their lack of transparency or explainability, also known as the black box problem.
• Time-consuming Training: The large number of parameters in CNNs can lead to a time-consuming training process.

In conclusion, CNNs have become a fundamental aspect of image processing and other related fields. However, their implementation does present specific challenges. Therefore, prospective users should carefully evaluate whether CNNs are the right solution for their needs, taking into account the potential benefits and drawbacks. With careful planning and execution, however, CNNs can undoubtedly be an instrumental tool in advancing your AI initiatives.