AI can accelerate the management of product data, provided it is based on a reliable, structured and governed repository.
Since the acceleration ofartificial intelligence generative in companies, marketing, digital and e-commerce teams are asking themselves a very concrete question: how to use AI to go faster?
Quicker to enrich product sheets, translate content, check the consistency of information, identify missing data or find information in a catalog that has become too large.
The promise is attractive. Product catalogs have never been so complex. They bring together technical attributes, marketing descriptions, media, logistics data, prices, variants, publication constraints by channel, supplier information and sometimes compliance rules.
It would therefore be tempting to think that AI will naturally correct problems. Yet when it comes to product data, it’s not just about the tool. The issue is deeper: can we, on the scale of a catalog, have data that is sufficiently structured, reliable and contextualized to be truly exploited by an AI?
AI doesn’t do the cleaning
One could imagine that a good AI engine will be able to work, regardless of the quality of the catalog: incomplete descriptions, half-completed attributes, inconsistent categories, duplicates, missing media or poorly applied publication rules.
In reality, it is often the opposite. AI applied to poorly structured data risks reproducing the initial state, or even making it worse. If the product dimensions are absent, if the materials are not standardized, if the rules of completeness are not defined, the AI does not have a reliable base on which to rely to produce a relevant response.
A product sheet is not just a description. It contains technical characteristics, categories, relationships between products, media, commercial and logistical data, validation statuses and publication rules.
It is this structure that allows teams to work efficiently, channels to disseminate the right information and AI to query data with relevance.
In other words, AI does not eliminate the need for data governance. It makes it even more important.
Generic AI or contextualized AI
Generic AI tools are already very useful: reformulate a text, offer a description, translate a paragraph or generate a first version of content.
But, by default, they do not know the company’s product catalog. They do not know which attributes are mandatory, which completeness rules apply, which workflow is in progress, which media are associated or which constraints vary depending on the distribution channel.
For a one-off need, copying and pasting a description into an AI tool may be sufficient. On the scale of a catalog of several thousand references, this method quickly shows its limits: manual manipulation, lack of traceability, difficulty in guaranteeing consistency, loss of governance.
The difference is therefore not only based on the tool used. It is mainly based on the context in which the AI works.
An AI connected to a structured repository can answer operational questions that a general AI cannot answer alone: which products are incomplete? Which files do not have an image? Which suppliers are certified? Which references cannot be published on which channel?
PIM becomes a basis for AI activation
From this perspective, PIM can no longer be seen solely as a tool for centralizing or enriching product sheets. It becomes a basis for AI activation.
A well-structured PIM provides AI with the elements it needs to function in an operational setting: a data model, standardized attributes, completeness rules, validation workflows, product relationships, associated media, and channel delivery rules.
When this base is reliable, the use cases become much more concrete: generating descriptions from verified attributes, translating content taking into account business terminology, identifying incomplete files, detecting inconsistencies or facilitating the search for information in natural language.
Artificial intelligence can then accelerate repetitive tasks without breaking governance rules. It becomes an aid to productivity and quality, but not a substitute for data management.
Governance remains human
It should also be remembered that AI should not decide alone whether product data is publishable.
In a product environment, an error can have very concrete consequences: false customer information, compliance problems, inconsistency between channels, damaged brand image, product returns, loss of trust.
The right model is therefore not that of blind automation. It is that of an AI integrated into an explicit framework: access rights, data perimeter, validation rules, traceability, human control and editorial governance.
Artificial intelligence will transform product data management, but it won’t erase the fundamentals. The most advanced companies will not only be those who have “added AI” to their processes, but those who have understood that the performance of AI directly depends on the quality, structuring and governance of their product data.
The challenge is therefore not to entrust AI with the mission of repairing the disorder. The challenge is to build a repository that is sufficiently reliable so that it can really contribute to creating value.
