How to Use AI to Automatically Enrich Product Listings

The problem of incomplete product listings

If you sell online, you know the problem: hundreds (or thousands) of products to describe, categorise, photograph and publish across different platforms. Every marketplace has its own requirements, every language needs adaptation, and listing quality directly affects sales.

The hidden cost is rarely measured. A complete product listing takes between 15 and 30 minutes of manual work: writing the title, description and attributes, categorising, translating. For a catalogue of 2,000 references published in two languages, that adds up to more than 1,000 hours of work. No Belgian SME can dedicate a half-time employee for a year just to type product listings.

The result: most online catalogues are incomplete. Titles copied from the supplier, missing or duplicated descriptions, empty attributes, badly categorised products. Each of these defects has a direct consequence: a listing without attributes never appears in the marketplace's search filters, a duplicated description hurts your search rankings, and a wrongly categorised product is simply invisible to shoppers browsing by category.

For an SME, maintaining a complete, optimised catalogue is a colossal job. It is exactly the kind of task where AI makes a massive difference: high-volume, repetitive and structured, yet requiring an understanding of language and images that classic scripts never managed to automate properly. If you are wondering what else in your e-commerce operation can be automated, our guide to AI for retail in Belgium gives a full overview.

What AI can do in practice

Image analysis

AI can analyse a product photo and automatically extract the colour, material, category, approximate dimensions and other visual attributes. No more typing this information by hand.

This is especially useful when your supplier data is poor. A wholesaler sends you a file with a reference, a price and a photo? Current vision models recognise that it is a gold-tone necklace with a heart pendant, or a long floral-print dress. These detected attributes become the raw material for titles, descriptions and search filters. For a costume jewellery or textile catalogue, where colour and material variants run into the hundreds, the gain is immediate.

One limit to keep in mind: AI estimates, it does not measure. Exact dimensions, weights and precise composition must come from your source data or your supplier. A good practice is to have the AI check consistency between the photo and the declared data, rather than asking it to invent what is missing.

Description generation

From the detected attributes and the raw information you provide, AI generates SEO-optimised descriptions, adapted to your brand voice and ready to publish.

The key point is consistency. A human copywriter producing 200 listings in a week delivers uneven quality: the first listings are polished, the last ones rushed. AI applies the same care to listing number 1 and listing number 2,000. You define the tone (technical, warm, minimalist), the structure (hook, benefits, specifications, care instructions) and the constraints (length, forbidden words, legal mentions) once, and the whole catalogue follows.

Watch out for regulatory requirements, though: some categories (food, cosmetics, toys) carry mandatory information. AI can insert it systematically, but defining the rule is up to you. That is exactly the kind of framing a structured approach sets up from day one, as we explain in our article on AI integration mistakes to avoid.

Multilingual translation

Selling in Belgium usually means publishing in French, Dutch and sometimes English or German. AI translates listings while preserving the commercial intent and the specifics of each language.

The important nuance: this is adaptation, not literal translation. A "cosy jumper for winter evenings" does not translate word for word into Dutch; size conventions, units and even sales arguments differ between Flanders and Wallonia. Current language models handle these nuances far better than the previous generation of machine translation tools, especially when you give them a brand glossary (product names that must not be translated, house terminology). We wrote a full article on AI-powered multilingual translation for Belgian SMEs if this topic is critical for you.

Attribute structuring

Every marketplace (Amazon, eBay, Shopify, Bol.com) expects specific attributes. AI can automatically map your data to the formats each platform requires.

This is often the most underestimated step. Amazon imposes category templates with dozens of mandatory fields and strict allowed values; Bol.com has its own taxonomy; your Shopify store a third one. Without automation, every additional channel multiplies the data-entry workload. With AI mapping, your internal product repository becomes the single source of truth, and each export adapts to the target format: correct categories, correct codes, correct units. File rejections for "invalid value" or "incorrect browse node", familiar to every Amazon seller, drop drastically.

A concrete example

Take a raw data line: "long floral dress blue size M polyester washable 30°".

AI turns that line into:

• Title: Long Floral Dress, Blue, Size M
• Description: Elegant long dress with a blue floral print. Light, flowing polyester cut, perfect for summer. Machine washable at 30°C.
• Structured attributes: Size M (38-40), Colour Blue floral, Material 100% Polyester, Care Machine wash 30°C
• Category: Fashion > Dresses > Maxi dresses
• Available in: FR, EN, NL, DE and more

This is exactly what our platform Universal Data Enricher does, a tool built to process entire catalogues automatically.

Now multiply by 5,000 lines. Processing takes a few hours of compute instead of months of typing, and every listing comes out with the same structure, the same level of detail and the same quality rules. That change of scale, more than the quality of any single listing, is what transforms the economics of a catalogue.

What does it cost, and what does it return?

The return-on-investment question comes down to something simple: what does a manually enriched listing cost, and what does it cost with AI?

Manual: at 20 minutes per listing per language, even valued at a modest internal rate, the unit cost runs into euros, often tens of euros for technical products. AI: the marginal cost of a generated listing is measured in cents, plus the one-off cost of setting up the pipeline (rule definition, testing, validation), which is amortised across the whole catalogue.

The indirect benefits usually outweigh the data-entry savings: complete listings appear in more filters and searches, so they generate more traffic; unique descriptions improve organic rankings; a shorter time-to-market lets you publish new products in days rather than weeks. McKinsey's research on generative AI consistently ranks marketing and sales among the functions where companies report the most value.

To structure your own calculation, our method for calculating the ROI of an AI project applies directly to a catalogue enrichment project.

Marketplaces and multilingualism: the Belgian reality

The Belgian market has a particularity: it is small and multilingual at the same time. A Walloon e-tailer who ignores Dutch cuts itself off from the majority of national purchasing power, and vice versa. The players who succeed publish systematically in FR and NL, and often in EN for international reach.

Then there is the marketplace dimension: Bol.com dominates the Benelux, while Amazon opens access to the French, German and UK markets. Each platform requires listings that comply with its taxonomy and are written in the local language. The Belgian FPS Economy and the Digital Wallonia barometer document, year after year, the growth of Belgian e-commerce and the relative lag of Walloon SMEs in online sales: the barrier is almost never technological, it lies in the capacity to produce quality product content, at volume, in several languages.

That is precisely the bottleneck automated enrichment removes. A five-person SME can maintain a trilingual catalogue of several thousand references with the quality level of a major player. And scale is not an issue for the pipeline: we have supported catalogues of over 50,000 products with the same automated enrichment mechanics, the approach is detailed on our product data enrichment page.

How to plug enrichment into your existing workflow

A successful enrichment project does not replace your tools, it slots into them. The typical setup for an SME:

1. Source: your ERP, your PIM, or simply the Excel/CSV files from your suppliers.
2. Enrichment: AI analyses images and raw data, then generates titles, descriptions, attributes and translations according to your rules.
3. Validation: a human reviews by sampling, especially at the start. You do not approve 5,000 listings one by one; you approve the rules, then spot-check the output.
4. Publication: export to the formats each channel expects (marketplace template, Shopify import, Google Shopping feed).

Two points of attention. First, source data quality: AI enriches, it does not invent; if the supplier file contains price or reference errors, they propagate. Cleaning the data is part of the project. Second, compliance: if your product data contains personal information (customer reviews, for instance), GDPR rules apply to AI processing just like any other processing.

For complex catalogues or custom integrations with an existing ERP, specialised support avoids dead ends; that is the core of our product data enrichment service.

Managing your listings day to day after enrichment

The initial enrichment is only half the story: a catalogue lives. New products, variants, price changes, evolving regulatory attributes, and ever-stricter marketplace requirements (Amazon and Bol.com penalise poorly filled listings in their search rankings, sometimes to the point of removal). Day-to-day management benefits from the same AI levers as enrichment: automatic categorisation of new items into your taxonomy and the marketplaces' own taxonomies, completion of missing attributes extracted from manufacturer datasheets or supplier PDFs, and description updates when a product range evolves.

The key is centralisation: your internal product repository (a PIM, or simply a structured database) remains the single source of truth, and the AI pipeline propagates every change to all channels. Then track the metrics that matter — conversion rate by category, return rate linked to incorrect information, SEO performance of your listings — to continuously refine your quality rules.

Mistakes to avoid

Three traps come up again and again in catalogue enrichment projects:

Automating everything at once. Start with a pilot batch of 50 to 100 representative products. Validate quality, adjust the rules, then scale up. A systematic error across 100 listings is a correction; across 10,000 listings, it is a crisis.

Neglecting deduplication and consistency. If your keywords repeat the title, or two variants of the same product carry contradictory descriptions, the marketplace and Google will notice. Quality rules (no title/keyword repetition, consistent vocabulary per product range) must be encoded in the pipeline, not checked after the fact.

Forgetting maintenance. A catalogue is never finished: new products, price changes, evolving regulatory attributes. Enrichment must be a recurring process, not a one-off operation. Plan from day one how new products will enter the pipeline.

Frequently asked questions

Do I need a PIM to start? No. A simple Excel or CSV export of your products is enough to run a pilot. A PIM becomes relevant once the catalogue exceeds several thousand references or several people maintain it in parallel.

Will the AI write the same thing as my competitors? Not if the pipeline is configured with your brand voice, your arguments and your own data. Generic content appears when you use a consumer tool without framing; a parameterised pipeline produces content unique to your catalogue.

What minimum volume makes it worthwhile? Below a hundred products, assisted manual enrichment (you write with an AI assistant) remains competitive. Above 500 references, or as soon as multilingual publishing is involved, the automated pipeline almost always wins. Our article on AI for Belgian SMEs helps you situate that threshold in your own context.

How to get started

If you have a product catalogue to enrich, three options are open to you:

1. Try our platform: Universal Data Enricher lets you test enrichment on your own data, with no commitment.
2. Run a supported pilot: we take a sample of your catalogue, define the quality rules with you and deliver a measurable enriched batch, as described on our AI for e-commerce page.
3. Talk through your case: if your situation needs specific adaptations (ERP, PIM, multiple marketplaces), request a free AI diagnostic or contact me to define the best approach. The first scoping conversation is free and without obligation.

See also: Product data enrichment