AI Auto-Tagging

AI auto-tagging refers to the automatic tagging of digital assets using AI models. In the DAM context, this primarily involves two key areas of application:

Image recognition: Identification of objects, scenes, moods, colors, or generic categories in image material
Language analysis: Transcription of audio and video material as well as automatic translation («Auto-Translate») of existing metadata into other languages

With these tools, AI is capable of handling a significant portion of content indexing. However, what standard models do not provide «out of the box», is context-related information:

Copyrights and usage rights – who created the asset, to what extent may it be used, and until when do licenses apply
Company-specific assignments – assignment to specific products, brands, or campaigns. While standard AI can recognize generic categories (e.g., «sneaker,» «smiling woman»), it cannot identify a company’s proprietary product names or brand associations. Such company-specific recognitions are technically possible but require extensive custom training of AI models using the company’s own product data.
Internal classifications and approval statuses – such as workflow stages, quality levels, distribution approvals
Context for specific business use – for which channel, target audience, or region an asset is intended

Successful DAM programs therefore combine AI auto-tagging for content cataloging with structured, manually or semi-automatically maintained metadata for context and rights. Those requiring company-specific recognition invest additionally in training their own AI models – a step that often pays off with large asset volumes and standardized product portfolios.

Read more in our article Optimize Your DAM with AI-Based Auto-Tagging.