When people talk about Artificial Intelligence, it often gets framed as a black box - data goes in, something comes out, and the process feels like “magic.” But when it comes to managing large product catalogs, magic isn’t enough. Businesses need clarity, control, and repeatability.

AI can deliver on that - if it’s broken into structured steps, each verified before moving on. That’s how product enrichment becomes not only faster, but also more accurate, transparent, and scalable.

Building Blocks of Product Enrichment

1. Image Generation – Possible, but Not Always Perfect

AI can generate compelling visuals, and in some cases, it’s a huge accelerator for creating consistent, on-brand imagery. But image generation still isn’t flawless. Details can be off, and results must be carefully validated. The important part is
knowing where AI can help and where human expertise needs to step in.

2. Text Generation – Rich, Contextual Descriptions

High-quality product descriptions don’t just describe - they differentiate, inspire, and inform. AI can draft these descriptions by blending product data with category-specific context. This makes it possible to enrich hundreds of products with clear, accurate, and engaging content. Crucially, each draft is verified before it’s approved, preventing errors from scaling across the catalog.

3. Translation – Making Products Market-Ready

For businesses selling across regions, accessibility matters. AI-powered translation helps ensure that product stories reach customers in their own language, with consistency across markets. With proper review and cultural checks, the same enriched content can be delivered globally - without bottlenecks.

4. Product Categorization and Filter Sets - Structuring Discovery

A well-enriched product catalog is only useful if customers can find what they’re looking for. That’s where categorization and filter sets come in. Assigning products to the right categories and attributes (size, color, material, use case, etc.) ensures that customers can navigate intuitively and refine their search.

Traditionally, this has been manual and error-prone, especially with large catalogs where attributes vary widely. AI changes this by:

• Auto-suggesting categories and attributes based on product data, images, and descriptions.
• Detecting gaps or inconsistencies where filters are missing or mismatched.
• Learning from customer behavior to refine filters and highlight the most relevant options.

By combining AI-driven suggestions with human verification, businesses can create consistent, scalable, and shopper-friendly navigation structures. This improves product discovery, reduces friction in the buying process, and increases conversion.

Why Verification Is Key

The difference between hype and business value is verification at every stage. By validating each step - whether an image, a product description, translation or categorization - you ensure that mistakes don’t compound.

This creates a workflow where:

• A single product can be enriched within minutes.
• Entire catalogs can be scaled in parallel.
• Human expertise stays in control, with AI acting as an accelerator.

From Black Box to Building Blocks

We’ve put this into practice using PIM/DAM and automation tools combined with carefully selected AI services and large language models. The result is a modular system where enrichment is not random, but structured:

• Generate → Verify → Approve → Scale.
  
  

This approach makes AI practical and trustworthy - not as a replacement for human work, but as a force multiplier that reduces time-to-market and increases consistency across catalogs.

Why It Matters

• Transparency: You see exactly how each step works.
• Scalability: From one product to thousands, the same process applies.
• Accuracy: Verification ensures content quality.
• Trust: AI becomes part of the workflow - not a gamble.

This text was AI-assisted, human-approved.