Much like how our brains are composed of interconnected neurons to process and store information, Artificial Intelligence (AI) harnesses neural network architectures to mimic these cognitive processes computationally. These structures are powerful tools in Machine Learning (ML), constituting a keystone in the victuals of Deep Learning (DL). Neural network architectures form a substantial part of AI by deciphering data complexities at unprecedented levels. They allow the transformation of raw data into valuable insights, ultimately streamlining decision-making processes and productivity.

• Intricate Design: Neural network structures are modeled with layered nodes, analogous to the human mind's neurons. These nodes are interconnected, and the complexity of the network is defined by the number of layers (deepness) and nodes (width).
• Learning Capabilities: These architectures can learn from data inputs and adjust their weights (parameters) to improve the prediction accuracy. This learning capability is iterative, improving over time with increased exposure to data.
• Adaptability: They can adjust according to varying complexities in data patterns, exhibiting a high degree of flexibility.
• Autonomous Discovery: Neural networks can autonomously learn and extract features from raw data, bypassing manual feature engineering.
• Multi-functionality: A neural network architecture can produce various outputs against a single input, mimicking the behavior of biological neurons.

The implementation of any neural network architecture necessitates meticulous planning. Underlying needs and objectives must be understood, followed by the selection of the most appropriate architecture. This process may involve experimentation with different models and parameter setups.

Furthermore, one needs to evaluate the trade-off between the model’s complexity and interpretability, and the requirement of computational resources. Training and deploying these architectures may require sophisticated hardware and software environments, and skilled technical personnel. Proactively managing these resources and orchestrating a strategic plan is critical for a successful implementation.

Post-implementation, rigorous validation and testing are crucial to ensure its performance aligns with the sought-after results. Regular monitoring and maintenance to update changing data patterns will enhance the model's integrity and reliability.

• High Accuracy: Because of their capacity to learn from vast amounts of data and adjust their parameters accordingly, these architectures frequently provide more accurate predictions than traditional machine learning models.
• Function with Unstructured Data: Neural network models can effectively function when presented with unstructured and semi-structured data like text, images, and audio.
• Fault Tolerance: In neural networks, information is distributed across all neurons. As a result, they retain their knowledge even if some nodes fail, making them highly fault-tolerant.
• Parallel Processing: A neural network's ability to execute parallel processing makes it suitable for tasks that entail handling multifaceted data characteristics concurrently.
• Scalability: Neural network architectures scale well with increasing data size and evolving complexities.

Despite the advantages, there are few limitations embedded with these architectures:

• Computational Complexity: They often require high-performance hardware and longer training times due to their processing complexity.
• Interpretability: It's generally challenging to comprehend how these models make predictions, famously earning them the epithet of "black boxes."
• Parameter Selection: Neural networks are sensitive to parameters such as learning rate and the number of hidden layers, making their selection vital but often complex.
• Overfitting: Due to its complexity and adaptability, it has a tendency to overfit the training data, thereby failing to predict unseen data accurately.
• Dependence on Data Quality: The performance of the neural network is highly susceptible to the quality of the input data.

Ultimately, the real-world feasibility of neural network architectures is a function of its potential benefits vis-à-vis the level of resources and know-how required. Therefore, organizations need to tread carefully, balancing their AI ambitions with pragmatic considerations.