AI tools can effectively identify and manage duplicate data entries. They go beyond basic exact matching by using algorithms to detect near-duplicates based on patterns, similarities in text, images, or data fields. This is more efficient than manual review, as AI can handle large volumes and subtle variations that humans might miss, like minor wording differences or compressed images.

In practice, these tools streamline workflows. Customer relationship management (CRM) systems like Salesforce use AI deduplication to prevent multiple records for the same contact. E-commerce platforms also employ it to merge near-identical product listings from different vendors, ensuring cleaner catalogs and better search results for shoppers.

The main advantages are significant time savings, improved data accuracy, and reduced storage costs. However, limitations include potential false positives/negatives, requiring careful algorithm tuning and sufficient training data. Ethical considerations involve ensuring the AI doesn't perpetuate biases present in the data. Future developments focus on improving accuracy across complex data types (audio, video) and real-time detection, enhancing trust and adoption in data-intensive fields.