Automating Metadata Tagging with AI — TdR Guide

AI tagging combines computer vision, NLP, and speech recognition to analyse different types of content: Images (Object, scene, logo, and text recognition); Videos (Frame-by-frame analysis, facial recognition, and transcription); Documents (Keyword extraction, entity recognition, and summarisation); Audio (Automatic transcription and sentiment tagging). By using pretrained models, AI can apply thousands of descriptive tags instantly—such as “mountain,” “blue sky,” “conference,” or “product packaging.” This dramatically reduces human workload while improving consistency and scalability.

AI tagging works best where asset volume and tagging complexity are high. Start with areas that bring immediate benefit, such as: Marketing and creative teams (Automating campaign asset tagging); E-commerce (Categorising product photos and lifestyle imagery); Video libraries (Generating searchable transcripts and visual metadata); Corporate communications (Tagging event, employee, or training footage). Each department may use the same DAM differently, but AI tagging creates a unified metadata layer that connects them all.

Each DAM vendor handles AI tagging differently. Staying vendor-neutral, here’s how leading systems approach automation today: Aprimo (Integrates with Microsoft Azure Cognitive Services and Google Vision to automate image recognition, logo detection, and OCR (optical character recognition). Metadata fields can be mapped directly to AI outputs); Bynder (Provides AI tagging through proprietary and partner engines, allowing batch recognition, automated keyword suggestions, and smart filters that evolve as assets are used); Adobe Experience Manager (AEM) (Uses Adobe Sensei for visual tagging, smart cropping, and context-aware metadata enrichment. Its integration with Creative Cloud ensures automatic tag updates during asset creation); Brandfolder (Employs Brand Intelligence for object recognition, duplicate detection, and semantic tagging, reducing redundancy and improving accuracy); Cloudinary (Offers auto-tagging through AI models trained for media optimisation—identifying scenes, objects, and emotions in both images and videos). When selecting or enabling AI tagging, review factors like tag accuracy, model flexibility, human validation options, and language support.

AI tagging works best when it’s guided by a structured metadata schema. Before implementing automation: Define mandatory fields (e.g., title, description, keywords, usage rights); Align taxonomy with business functions (campaigns, products, regions); Establish controlled vocabularies to reduce inconsistent terms; Decide how AI tags will map to existing fields (e.g., “AI Tags” vs “Keywords”). Without this structure, AI-generated metadata can become messy or redundant, reducing search quality rather than improving it.

AI models learn from examples. In some systems, you can train or fine-tune models based on your content: Upload sample assets with high-quality metadata; Correct AI-generated tags to refine accuracy; Review confidence scores to identify improvement areas; Use feedback loops so the system learns from accepted or rejected tags. Over time, this continuous calibration makes AI tagging more precise and aligned with your organisation’s specific content.

Even the best AI needs oversight. Create review steps for metadata managers or librarians to validate AI outputs before they become permanent: Enable an “approve/reject” process for new tags; Allow users to suggest edits or confirm tag accuracy; Keep logs of changes to track AI performance over time. This ensures quality control while building confidence in the automation. Eventually, human involvement can decrease as accuracy improves.

To maximise efficiency, automate tagging at the moment assets enter the DAM: Configure ingestion workflows to trigger AI tagging automatically; Apply metadata templates based on upload folders or user roles; Route assets to review queues for metadata verification; Store AI tags separately from user-generated metadata for auditing. Real-time tagging ensures assets are instantly searchable without waiting for manual updates.

Once AI tagging is in place, measure success continuously. Track metrics such as: Tag accuracy (compared to human tagging); Average time saved per asset; Search success rate improvements; Asset reuse rate. Use insights to refine taxonomy, retrain models, or expand automation to new asset types.