Support Vector Machines (SVM) is a powerful and versatile Machine Learning algorithm that offers solutions to both classification and regression problems. Developed in the 1990s, SVMs are part of the supervised learning models that have become a significant tool in the field of artificial intelligence (AI).

Primarily designed for binary classification, SVM uses a unique method—creating a hyperplane or line that most suitably segregates the data into two categories or classes. This process is instrumental in the machine's ability to learn from data and make critical decisions. The algorithm can also handle multi-classification tasks and regression problems, proving SVM's versatility.

• Distinct Functionality: SVM categorizes data points using a hyperplane, ensuring maximum distance between closest points in the given categories, hence maximizing the margin for reducing errors.

• Kernel Trick: SVM employs the kernel trick, allowing the algorithm to transform and classify non-linear and high-dimensional data if linear separation is not feasible in the existing dimensional space.

• Margin Maximization: SVM is well known for ensuring an optimal hyperplane by focusing on margin maximization. It sets the boundary so that the gap between different classes or categories is as wide as possible.

• Effective in High Dimensional Spaces: It is particularly effective in cases where the number of dimensions is greater than the number of samples.

The implementation of SVMs includes several stages. It starts with a thorough analysis of the problem and data at hand, identifying the machine learning task(s) that SVMs could tackle–may that be a classification or regression problem, or both. Afterwards, you should process and clean your data, ensuring that the algorithm can interpret it correctly.

Bear in mind the characteristics of SVM; the most suitable applications are those with a smaller, less complex dataset and require a high degree of accuracy. Examples include face detection, handwriting recognition, and image classification. However, SVMs are highly versatile that a wide range of sectors and business domains can utilize it for different applications, given that the data and problem align with this model’s distinct features. Moreover, it is crucial to carry out testing and evaluation procedures to ensure accuracy and efficiency, identifying relevant adjustments as required.

• Robustness: While SVM is effective on a large scale, the standout feature is its robustness when faced with high-dimensional data - where the number of features exceeds the number of observations.

• Effective in Non-linear Data Classification: SVM uses the Kernel trick to transform the input into a higher-dimensional space, enabling the classification of non-linear data sets.

• Accuracy: SVMs provide accurate predictive models. They have a small estimation error because of the capacity control obtained by the regulation of the model's complexity.

• Handling Outliers: SVM is less affected by outliers. It is known for its remarkable robustness.

• Complex Parameters: SVM is harder to use because of complexity in understanding the applied mathematics. It's not as straightforward to interpret as some other models.

• Inefficiency in Large Datasets: Training an SVM model on a large dataset could be computationally intensive and time-consuming.

• Binary Classification: By design, SVMs are suitable for binary classification tasks, which means that it requires modifications when applied to multi-class classification problems.

• Lack of Probability Estimates: The SVM algorithm does not directly provide probability estimates; these are calculated using expensive five-fold cross-validation.

• Kernel Selection: Choosing the right kernel can be a challenge because the choice of kernel depends heavily on the data. It requires expert knowledge and experience to select an appropriate kernel for non-linear SVM.

In conclusion, SVMs represent a considerable choice for specific machine learning tasks, especially those related to classification and regression on smaller datasets. Their robustness against overfitting and high-dimensional data makes them an excellent option for tasks that require high precision. However, careful planning and thorough knowledge of their disadvantages are essential for successful implementation. Ultimately, recognizing these conditions will allow you to leverage the power of SVMs in your AI endeavors.