Can AI fill my product data?

AI can fill product data at scale for 4 tasks, and it still needs human control for the rest. In PIMvendors selections, the useful and scalable AI use cases are attribute extraction, translation, auto-classification and channel-specific descriptions.

Where AI is reliable enough to scale:

• Attribute extraction from supplier spreadsheets, PDFs and datasheets.
• Translation of product content into extra languages, with glossary control.
• Auto-classification into your own taxonomy or standards such as ETIM or GS1.
• Channel-specific descriptions generated from approved attributes.

Where AI remains unreliable:

• Regulatory and compliance data, such as PPWR packaging data or safety information.
• Consistency across attributes, for example dimensions that match the variant and the datasheet.
• End-to-end agentic workflows that create, approve and publish without a human step.

Three requirements make AI usable in a PIM. Every AI output carries a status such as suggested or approved. The PIM logs which model produced which value. The quality rules that apply to people also apply to AI.

Test AI claims on your own data during scenario-based demos. Give every vendor the same 20 to 50 supplier files, and compare accuracy, review effort and cost per product.