Optimizing SQL databases for AI and machine learning applications involves several strategies to enhance data storage and retrieval efficiency:

1. Indexing: Creating appropriate indexes on frequently queried columns can significantly speed up data retrieval. Use composite indexes for queries involving multiple columns.

2. Normalization and Denormalization: Normalize data to reduce redundancy and maintain integrity. However, for read-heavy operations typical in AI, denormalization might be necessary to reduce join operations and improve query performance.

3. Partitioning: Split large tables into smaller, more manageable pieces. Horizontal partitioning (sharding) can distribute data across multiple servers, improving query performance and scalability.

4. Efficient Query Design: Optimize SQL queries to minimize computational load. Use SELECT statements with only necessary columns, avoid subqueries where possible, and use JOINs judiciously.

5. Materialized Views: Use materialized views to store precomputed results of complex queries. This reduces the need to repeatedly execute resource-intensive operations.

6. Caching: Implement caching mechanisms for frequently accessed data to reduce database load and improve retrieval times.

7. Hardware and Storage Optimization: Utilize SSDs for faster data access, and ensure sufficient RAM and CPU resources to handle AI workloads efficiently.

8. Data Compression: Apply data compression techniques to reduce storage space and improve I/O performance, especially for large datasets common in machine learning.

These strategies collectively enhance the performance and scalability of SQL databases in AI and machine learning applications.