How To Scale Product Catalogs With Gen AI

By Oli Kashti

Published May 6, 2026

A woman sitting in front of a laptop computer trying out personalization testing.

AI Summary

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Scaling up is a key aim of most ecommere brands. This can be as a result of expansion to new marketplaces, increase of product variants or introduction of user-generated listings. Either way, the transformation needs to be achieved seamlessly.

This transition is not without it's challenges though. For example:

Inconsistent product data
Manual enrichment bottlenecks
Poor search relevance and discovery

These challenges can be overcome by Gen AI. Scaling your business shouldn’t mean worse experiences, but unfortunately, today this is what seems to be happening. Intelligent scaling through Gen AI is the answer.

What Is Scaling A Catalog?

Scaling a catalog can be understood in four points:

Volume (SKUs, variants, suppliers)
Quality (attributes, descriptions)
Speed (time to publish/update)
Discoverability (search, filters, recommendations)

If any of this data isn’t up to scratch, the results will be clear through the lost conversions. You need optimized data to facilitate an optimized search and boosted conversions.

Challenges With Traditional Search

Rule-based enrichment within search just doesn’t scale once. Manual tagging is slow, if possible, and will encounter errors and inconsistencies. For example, onboarding 50 SKUs from ten suppliers each, all with different formats and missing attributes; this is not something you want to be dealing with. Your brand has far more important issues than that.

Using Gen AI To Scale

Gen AI has the capacity to fix this issue. These involve:

Attribute extraction from unstructured data
Product description generation (SEO optimized)
Variant normalization
Image-to-attribute understanding

All of these means that you can relax: Gen AI has it under control. They are able to understand what the new SKUs are, create descriptions for them, apply attributes for them, and do so in a way that benefits your brand.

Gen AI doesn’t just fill in the gaps, it actually creates uniformity across the board. It takes what could be a very challenging project, and turns it into an efficient and optimized task.

The Benefits Of Using Gen AI To Scale

Now we’ve seen how efficient and easy it can be for Gen AI to complete this task, let’s take a look at some of the benefits that can be achieved from this:

Generate missing attributes
Normalize inconsistent fields
Fix naming inconsistencies
Create high quality product descriptions
Remove duplicated content
Improve filters

These functionalities create benefits for your business across the board. From catalog standardization, to SEO-optimized descriptions at scale, to smart tagging, to visual AI for catalog expansion. All areas are covered.

The Impact Of Gen AI On Search, Merchandising and Conversion

Better data ensures better search relevance, and this is what Gen AI can ensure. The cleaner the attributes, the stronger the filtering usability will be, which will help customers find customers easily and efficiently, boosting conversions and brand image. The richer signals will result in improved personalization, and the faster onboarding of new SKUs, the faster the improved revenue. The result? Significantly increased conversion rates.

Gen AI Moving Forward

Your catalog is no longer static- it is a dynamic structure. With all of the reasons and methods to scale, real-time catalog enrichment is what’s coming next. Expect to see the rise of self-healing catalogs which autocorrect any data issues, to ensure that no inconsistencies appear across your site. On top of this, hyper-personalization will also play a role as each user will see product feeds tailored to them specifically; the store of one.

Conclusion

Scaling catalogs is no longer a question of manpower, it’s about intelligence. Gen AI ensures that scaling can be done quickly and efficiently with your businesses best interests in mind. The result will be improved conversions, better search performance and successfully scaling.