Asset Management & Discovery

11 min

The Challenge

As content volume grows, finding the right asset becomes increasingly difficult. Teams waste hours searching across folders, drives, and systems. When they can't find what they need, they recreate assets that already exist or use outdated versions that shouldn't be in market.

Manual tagging cannot keep pace with asset ingest rates. Metadata is inconsistent, incomplete, or missing entirely. Search returns too many results or the wrong results, forcing users to scroll through hundreds of thumbnails hoping to spot what they need.

The result: The asset management system becomes a dumping ground rather than a strategic resource. The investment in content creation is undermined by the inability to find and reuse what already exists.

What You'll Explore

AI-Powered Search - Experience natural language search that understands intent, not just keywords. Ask for “summer campaign photos with families at the beach” and get relevant results, not just assets tagged with individual words.

Automated Tagging and Enrichment - Explore smart tagging that automatically applies metadata at ingest. See how AI identifies objects, scenes, colors, and concepts without manual intervention, creating searchable metadata at scale.

Discovery Agent - See how AI Agents proactively locate, recommend, and surface relevant content based on context. Understand how the system learns from usage patterns to improve recommendations over time.

Content Optimization Agent - See how AI Agents can automatically transform assets for target channels. Ask for “LinkedIn versions of our summer campaign images” and receive transformed, channel-ready outputs.

AI Capabilities in Focus

Capability	What It Does
Natural Language Search	Understands queries like “find photos from last quarter's product launch”
Discovery Agent	Proactively surfaces relevant assets based on context and patterns
AI-generated Metadata	Automatically applies descriptive metadata at asset ingest
Content Optimization Agent	Discovers and transforms assets for specified channels in one workflow

Primary Solutions

AEM Assets: Enterprise digital asset management with AI-powered capabilities

Content Hub: Simplified asset discovery and distribution interface

AI Assistant: Natural language interface for search and asset operations

Best For

This module is well-suited for organizations that:

Have large and growing asset libraries that are difficult to navigate

Lose time to manual search, recreation, or use of outdated assets

Struggle with inconsistent or incomplete metadata

Have distributed teams that need consistent access to approved content

Want to reduce the gap between asset creation and asset activation

Audience Considerations

Audience	Focus Areas
Creative Teams	Search experience, collection workflows, asset reuse
Marketing Teams	Content Hub interface, self-service access, sharing
DAM Administrators	Tagging configuration, governance, integration architecture
IT / Marketing Technology	Deployment options, integration patterns, security model

Sample Hands-On Exercises

Depending on engagement depth, participants may:

Search using natural language queries and compare to keyword search

Upload assets and observe automatic smart tagging

Create and share collections with internal and external stakeholders

Use AI Agents to discover and transform assets for target channels

Commonly paired with:

Multi-Channel Web DeliveryMulti-Channel Web Delivery: Follow assets from discovery through personalized delivery

On-Brand Marketer CreationOn-Brand Marketer Creation: See how discovered assets feed into self-service creation

For upstream context:

Workflow Planning & VisibilityWorkflow Planning & Visibility: Understand how campaign metadata connects to asset organization

Prerequisites

For customer experience lab engagements, participants benefit from:

Understanding of current asset management workflows and pain points

Familiarity with how teams currently search for and access assets

(Optional) Sample assets for upload and tagging exercises

(Optional) Existing taxonomy or metadata standards documentation