Firstly, it is important to understand the basic concept of Artificial Intelligence. AI is a broad field that encompasses several sub-fields like machine learning, natural language processing, computer vision, and more. It aims to develop intelligent machines that can mimic human behavior, decision-making, and cognitive abilities.

When we talk about applying AI to Data Sciences, we are essentially referring to the use of AI algorithms and techniques to analyze and extract insights from large datasets. This is particularly useful in fields like finance, healthcare, and e-commerce where businesses have access to vast amounts of data but struggle to make sense of it.

Some of the common applications of AI in Data Sciences include:

1. Predictive analytics - AI can be used to predict future outcomes based on historical data. This is particularly useful in areas like finance and marketing where businesses need to make informed decisions about future investments.

2. Natural Language Processing - AI can be used to extract meaning from unstructured text data like social media posts, customer feedback, and more. This is useful for sentiment analysis and understanding customer preferences.

3. Image recognition - AI can be used to recognize patterns in images and classify them accordingly. This is useful in fields like healthcare where doctors can use AI to diagnose diseases based on medical images.

4. Fraud detection - AI can be used to identify fraudulent activities in financial transactions by analyzing patterns and anomalies in data.

5. Recommendation engines - AI can be used to analyze customer behavior and recommend products or services based on their preferences. This is useful in e-commerce where businesses can personalize their offerings to increase sales.

To develop AI content for Data Sciences, individuals or teams can start by gaining a solid understanding of the different AI techniques and algorithms. This can be done through online courses, books, and tutorials. It is also important to have a good understanding of programming languages like Python and R, which are commonly used for AI development.

Once the basics are covered, individuals or teams can start working on real-world problems by building models and analyzing datasets. This requires a good understanding of data preprocessing, feature engineering, and model evaluation techniques.

To summarize, AI has enormous potential in Data Sciences, and its applications are only limited by our imagination. With the right skills and knowledge, individuals and teams can develop cutting-edge AI solutions that can transform businesses and industries.