Today, Model compression is scattering its roots in the realm of artificial intelligence (AI) to provide powerful yet efficient models. Model compression strategies are being utilized to promote computational and storage efficiency, improve reliability, and minimize energy consumption.

• Resource-effective: AI model compression reduces the computational resources requirement (like memory storage and processing power) without significantly compromising the model's accuracy.
• Flexibility: Model Compression techniques are adaptable and suitable for different AI model classes and architectures.
• Accuracy Maintained: Model Compression aims for negligible loss in performance, meaning resources are saved without sacrificing the model's ability to make accurate predictions.
• High-Fidelity: Emphasizing on retaining high fidelity, model compression compresses models to maintain the quality of model outputs.
• Cost-Effective: It's a cost-effective solution for decreasing the burden of implementing AI models, especially within the Internet of Things (IoT).

• Space-efficient: It reduces the size of models, making them easier to store and manage.
• Decreased latency: Smaller models ensure reduced latency and accelerated inference time crucial in real-time scenarios.
• Improved Portability: Smaller models are more easily deployed across a range of devices, from powerful servers to constrained devices such as smartphones and embedded devices.
• Reduced power consumption: Smaller and compressed models require less computational power, thereby reducing energy consumption, which is particularly important for mobile and edge devices.
• Increased reliability: Efficient model compression can also contribute to the robustness of the model, making it less affected by noise or changes in input.

• Accuracy tradeoff: While compressing the model, it's crucial to maintain accuracy. This balance might be challenging to achieve in all scenarios.
• Compatibility: The compatibility between the original AI model and the compressed one might be challenging at times. This can pose a difficulty in migrating the models.
• Increased complexity: Certain model compression techniques can increase algorithmic and implementation complexity.
• Limited influence: Users of compressed models exert little influence on the process and results of compression, facing potential misalignment with their specific needs.

The implementation of Model Compression in AI necessitates thorough understanding of the models and meticulous selection of the appropriate compression technique. Post this; a careful evaluation concerning model’s performance before and after compression is crucial to ensure that the trade-off between computational efficiency and performance is balanced. Periodic monitoring of the compressed models for possible biases and unwarranted deviations should also be in place to guarantee overall efficacy.

Thus while Model Compression creates a pathway towards making AI models more efficient and accessible, a well-rounded approach to implementing it ranges from profiling the computation and communication footprints of the model, selecting the appropriate method of compression that aligns with the model's requirements, and continuous monitoring and evaluation of the system's performance post-compression.

The summary of this approach entails careful study, evaluation, and customization for meeting specific needs is the foundation of successful AI model compression implementation. Without these, deployed AI models might not live up to their full potential. Even though model compression introduces an added layer of complexity to AI deployment, the benefits usually outweigh the challenges for many organizations. As AI continues to advance and become more ingrained in our everyday lives, model compression will likely play an even more significant role.