Using AI for Content Classification and Organization — TdR Guide

AI classification is the process of automatically categorising assets into predefined or dynamically created groups. It can: Identify asset type (photo, logo, brochure, video). Detect content themes (e.g., product, lifestyle, or location). Infer context (campaign, region, or channel). Recognise versions or related assets. Unlike manual tagging, which depends on user input, AI classification learns patterns from data and applies them consistently. Over time, it refines accuracy as more assets are processed.

AI needs structure to function effectively. Before enabling automation, define what “organised” means for your business. Identify primary classification tiers (e.g., brand, campaign, product, content type). Document folder structures and category rules. Define what metadata determines class membership. Determine governance rules for reclassification or review. The clearer your taxonomy, the better your AI system can map new assets correctly.

Each vendor applies AI classification differently. Here’s a neutral overview of current approaches: Aprimo: Combines machine learning and rules-based logic. AI can detect objects, recognise logos, and auto-assign taxonomy values based on brand or campaign metadata. Adobe Experience Manager (AEM): Uses Adobe Sensei to analyse content visually and contextually, grouping assets by theme, colour, or visual similarity for faster curation. Bynder: Employs AI to classify assets by brand category, media type, and visual attributes, automatically generating smart collections. Brandfolder: Integrates visual recognition and clustering to auto-organise related assets, flag duplicates, and maintain consistent categorisation across teams. Widen (Acquia DAM): Offers automatic folder placement and rule-based grouping driven by AI-assisted metadata evaluation. Each system balances automation with governance, allowing human validation to refine classifications and prevent misplacement.

AI cannot impose order on chaos. Preparing your DAM ensures successful automation: Audit your existing taxonomy—remove outdated or redundant folders. Align naming conventions and metadata standards. Ensure core metadata fields (type, campaign, brand) are populated and consistent. Identify classification rules you want to automate (e.g., “All assets tagged with Product A → Folder A”). This preparation gives AI a clean, logical foundation for making accurate decisions.

Depending on your platform, AI classification may use pre-trained models or allow custom training. Upload a sample set of assets grouped correctly by humans. Validate AI-assigned categories and adjust where needed. Use feedback mechanisms—accept or reject AI classifications to fine-tune accuracy. Set confidence thresholds to prevent uncertain classifications from being finalised automatically. Over time, the system learns your organisation’s unique asset relationships and brand language.

For complex DAM environments, hybrid approaches deliver the best results. Rules-based logic ensures compliance with known structures (e.g., “Assets uploaded by Region X must enter Folder X”). Machine learning handles subjective or context-based classifications (e.g., grouping assets by emotion, tone, or aesthetic similarity). Combining both ensures AI remains flexible while adhering to governance standards.

To maximise efficiency: Automate classification at the point of upload or ingestion. Route assets with uncertain classifications to a review queue. Sync classifications with workflows—e.g., auto-notify reviewers when assets enter a “pending approval” group. Enable batch classification for legacy assets to retroactively organise existing libraries. Integration ensures AI classification becomes part of daily operations, not an isolated feature.

AI classification isn’t a one-time project—it’s an evolving process. Conduct quarterly reviews to evaluate accuracy and governance alignment. Track misclassified assets and refine rules. Add new categories as campaigns or product lines evolve. Leverage analytics to see how classification impacts search success and reuse rates. Continuous improvement turns your AI model from a static tool into a dynamic organisational partner.