AI Product Data Enrichment

What Is Product Data Enrichment?

Product data enrichment is the work of completing and improving the information attached to each product: attribute values, classification, descriptions, images, alt text and translations. The goal is simple. A shopper, a marketplace or a search engine should find the right product and understand it without guessing. It is close to data cleansing, but not the same: cleansing fixes values that are already there, enrichment adds what is missing.

In ecommerce product data enrichment, the hard part is rarely writing. It is knowing which attributes matter for each product family, which values are allowed, and who validates them. Missing or wrong values are a customer problem first: a filter that hides the product, a search with no result, a product page that search engines cannot read well, a return because the size was wrong. More on that in why bad product data is a customer problem.

Enrichment usually covers:

• Attributes: technical and marketing values, units and formats, filled from supplier files, existing texts and images.
• Classification: placing each product in the right family and category of your taxonomy.
• Content: product titles, descriptions and bullet points that follow your brand rules.
• Media and accessibility: image tags and alt text.
• Localization: translations and market-specific values.

Why AI Adoption Stalls in Product Data

Most teams start with a tool, not with the data model. A demo generates hundreds of product descriptions in seconds, everyone is impressed, and months later the texts are still in a spreadsheet: no path into the PIM, no quality gate, no owner.

AI amplifies the data quality you already have, good or bad. So the program starts with governance and the data model, and only then with technology.

AI Attribute Enrichment and Classification, With Quality Controls

AI is useful here because the work is repetitive and the rules can be written down. It becomes risky the moment the model is free to invent a value. So every use case is designed around your data model first, and the AI works inside it.

AI Product Attribute Enrichment

The model reads what you already have (supplier data, existing descriptions, technical sheets) and proposes values for empty attributes. For select attributes, it can only pick from the options already defined in your PIM. If the source does not contain the fact, the attribute stays empty and is flagged, instead of being guessed.

On a European DIY retailer's catalog, 200 AI configurations and 950 rules filled 150 previously empty attributes with 80% coverage and an error rate below 5%, because prompts were forced to pick from existing attribute options and never invent values.

How the catalog was prepared for it is described in the AI enrichment case study for a European DIY retailer.

Published research points the same way. On a public benchmark of 565 product offers from 59 websites, Brinkmann et al. (2024), presented at ADBIS, found that GPT-4 extracted attribute values with an F1 score (a measure that balances wrong and missed values) of 74% when it had only the attribute names and descriptions, about 80% when ten example values per attribute were added, and 91% with example values plus worked examples. The clearer the target model, the better the result. Even then, some values were still wrong or missed, which is why we keep human review in the workflow.

Industry research reports a similar balance. In a talk paper at CIKM 2025, Luo et al. (2025) report that their LLM system raised product information completeness by 78% on a random sample of products from one e-commerce catalog, with no major drop in accuracy. Only the abstract is openly available, so the sample size and method details are not known.

AI Taxonomy Classification

Classification assigns each product to the right family and category, based on its name, description and attributes. It matters because the family decides which attributes are expected, so a wrong family means wrong completeness and wrong filters. The model proposes a category from your own taxonomy, never a new one, and uncertain cases go to a person.

The Quality Controls

• Closed lists: values must match an existing option, unit or format, or they are rejected.
• No overwrite: values already validated by your team are never replaced automatically.
• Human review: samples per family are checked before scaling, then reviewers work on exceptions.
• Tracking: rejection rate per use case, with a threshold that pauses the flow and triggers a review.
• Traceability: each AI value keeps its source, so anyone can see where it came from.

How Enrichment Works With Your PIM

Your PIM (product information management system) stays the single source of truth. AI is one step in the enrichment workflow, not a separate tool that lives in a spreadsheet.

1. Select: the pipeline reads, through the PIM API, only the products and attributes that are empty or flagged, family by family.
2. Propose: AI suggests values, categories or texts using your rules and your attribute options.
3. Validate: automatic checks reject anything outside the allowed values, then your team reviews in the PIM workflow.
4. Publish: approved values are written back to the PIM and go to your channels like any other product data.
5. Measure: completeness per channel and the rejection rate are tracked over time.

Supplier data is often where enrichment starts, and where it breaks: why supplier data breaks PIM projects explains the usual failure points. And because AI assistants now read product pages too, structured values also help product visibility in AI search.

What the Program Covers

Phase 1: Opportunity Assessment

We map your product data workflows against AI applicability. Common high-value targets: attribute classification and normalization, product description generation, image tagging and alt text, supplier data onboarding, search relevance. Common traps: using AI for governance design, vendor selection, or any judgment that is not documented.

Output: a prioritized use-case map with data readiness scores and a 90-day pilot recommendation.

Phase 2: Pilot on Real Data

One use case, real data, a production-like environment. Success criteria are set upfront: quality threshold, throughput and cost compared with the manual process. If the pilot misses the threshold, we stop and adjust instead of pushing on.

On a European DIY retailer's catalog, a 260-product pilot came first. Prompts were forced to pick from existing attribute options and never invent values, which kept errors under 5%, before scaling to 2,600+ product texts across 13 categories.

Phase 3: Integration and Scaling

A pilot that never reaches the PIM stays an experiment. Phase 3 covers the integration (API design, quality gates, human review, exception handling), the rollout to other validated use cases, and the governance: who reviews AI output, which rejection rate triggers a review, and how quality is tracked over time.

Tell us your highest-priority product data workflow. We will tell you whether AI is the right answer, and if so, what the path looks like.

Sources: Brinkmann, A., Baumann, N. and Bizer, C. (2024). Using LLMs for the Extraction and Normalization of Product Attribute Values. ADBIS 2024, Lecture Notes in Computer Science, Springer. doi:10.1007/978-3-031-70626-4_15. Luo, G., Han, J., Çeker, H. and Bouyarmane, K. (2025). Using Large Language Models to Improve Product Information in E-commerce Catalogs. CIKM 2025, ACM. doi:10.1145/3746252.3761437.