Artificial Neural Networks (ANN), sometimes simply referred to as Neural Networks, are computing systems inspired by the biological neural networks that constitute the brain's structure. ANNs mimic the behavior of the brain by processing data through a series of interconnected nodes or "neurons" referred to as nodes. The design aims to simulate the way the human brain analyzes and processes information, which can pattern recognition and decision-making tasks.

Also, ANNs project the following key properties and features:

• Robustness: In the design of ANNs, the network continues to function even when some neurons are impaired or fail to function correctly. This is due to the decentralized and interlinked nature of the neurons.
• Adaptive Learning: Artificial Neural Networks have the ability to learn and adapt from experience. They can improve their performance based on the feedback or the correctness of their predictions.
• Self-Organization: ANNs can automatically create representations and models for the data it processes, thus improving its behavior and performance over time.
• Real-Time Operation: ANNs boasts excellent computational speed, enabling real-time system operations and decision-making.
• Parallel Processing: The structure of an ANN allows it to process information in a parallel manner across multiple nodes, leading to faster computations and more robust system performance.

Artificial Neural Networks are utilized extensively in various industries due to their dynamic learning capabilities, robustness, and ability to handle complex, high-dimensional data.

Implementing Artificial Neural Networks requires a systematic approach that involves analyzing the organization's concrete needs, selecting the most appropriate type of neural network, and training the network with carefully curated training data. It is also essential to continuously monitor the network's performance – making adjustments and providing re-training as necessary to ensure optimum performance.

The adaptability and learning capabilities of Artificial Neural Networks have made them a critical tool in modern computational systems. Thanks to these characteristics, ANNs can effectively process complex, high-dimensional data, making them invaluable in various domains, including data analysis, pattern recognition, and financial systems. Though they are not without their challenges – such as overcoming noisy data, ensuring ample quality data for training, and improving their interpretability – their benefits make them a reliable solution for many organizations. By carefully evaluating the advantages and challenges, you can harness the full potential of Artificial Neural Networks for your organization's success.

Artificial Neural Networks showcase several appealing advantages for various applications, including:

• Dynamic Learning: One primary advantage of ANNs is that they can learn and improve their performance over time. This self-learning property enables them to adapt to changes and improve their performance and accuracy.
• Real-time Operation: ANNs can execute operation in real-time due to their excellent computational speed, which is crucial in many use cases, such as autonomous driving and real-time financial trading.
• Fault Tolerance: Due to decentralized processing, ANNs can maintain functioning even when some neurons or connections fail. This makes ANNs highly reliable and robust.
• Parallel Processing: ANNs can process information simultaneously across multiple nodes, leading to faster computations.
• Ability to Handle Noisy Data: ANNs are highly effective in handling real-world data often full of noise and inconsistencies.

However, the use of ANNs also showcases certain disadvantages:

• Opaque Decision-Making: ANNs often resemble a "black box" when it comes to understanding their decision-making process. The complex interaction between layers and neurons make comprehending their reasoning difficult.
• Overfitting: ANNs run the risk of learning too much from the training data, causing them to perform poorly on unseen data.
• Training Time: Training a Neural Network, especially for complex tasks, can be computationally intensive and time-consuming.
• Data Dependency: The effectiveness of ANNs is significantly dependent on the quality and volume of data available for training.