Renaming files using AI-generated summaries involves using artificial intelligence tools to automatically create descriptive names based on file content, replacing manual naming. AI analyzes elements like text, audio transcripts, or image features within a file to generate relevant keywords or phrases summarizing its essence. This differs significantly from rule-based renaming (like adding dates) by producing unique, context-rich names reflecting the file's actual content, improving searchability.

For example, a researcher could use document AI tools (like Adobe's Sensei or cloud AI services) to process a folder of scanned PDFs; the AI might generate summaries like "Contract - VendorX - Signed 2025" or "Study - Climate Impact - Coastal Cities" as new filenames. Similarly, photo organizers employ AI (in apps like Google Photos or Adobe Lightroom) to analyze images and rename batches based on identified subjects or themes, such as "Beach Vacation - Sunset Ceremony" or "Conference Talk - Speaker Name".

This approach saves significant time, creates intuitive content-based names, and enhances organization, especially for large collections. Limitations include occasional inaccurate summaries requiring human review and reliance on AI model quality. Ethical considerations involve potential biases in the AI's analysis. As AI accuracy improves, wider adoption across content management systems for documents, media, and research archives is likely.