AI product tagging automatically adds attributes to products, such as colour, pattern, material, neckline, fit, occasion or dietary suitability. It reduces the manual work of entering data for every new item in the catalogue.
Computer vision reads the product photo while a language model reads the supplier description and spec sheet. The system proposes tags drawn from your own approved list, and a person can review them before they are published to the website, marketplace or product information management system.
Fashion, homewares and large-range retailers use it to speed up onboarding of new lines, improve filters and site search, and make marketplace listings more complete. Better attributes also feed recommendations and visual search.
Check the accuracy on your range, particularly for subtle attributes like fabric content or sizing, where a wrong tag creates returns or compliance risks. Use a controlled vocabulary so tags stay consistent, and keep a review step for claims such as "organic" or "vegan". Do not let AI invent specifications.
Good product data is the base for search, recommendations and ads, so tagging is a quiet but high-value use of AI. Run a pilot on one category, measure the error rate and decide how much review you need before scaling to the whole catalogue.