OpenAI is introducing new shopping features in ChatGPT that allow users to virtually try on clothing and accessories using their own photos, and to save products in a Favorites library. While the announcement is product-focused, it is also another sign that AI assistants are becoming a more active part of online shopping and product discovery.

What happened

According to the announcement, OpenAI is rolling out shopping-related capabilities inside ChatGPT. The new features include:

• Virtual try-on for clothing and accessories using a user photo
• Favorites, a library for saving products a user wants to revisit

At this stage, the source describes these as newly introduced shopping features. It does not by itself establish how broadly they are available across markets, which retailers or merchants are included, or how product placement works in practice.

Why it matters for European businesses

For European retailers, brands and digital teams, the main significance is not only the virtual try-on feature itself, but the wider direction of travel: AI assistants are moving closer to the point where consumers browse, compare and shortlist products without starting on a retailer's website or a conventional search engine.

This has several practical implications. First, visual and structured product data may become more important if AI-driven interfaces increasingly mediate shopping decisions. Second, product discovery could shift further away from traditional website entry points, affecting traffic patterns, conversion paths and attribution. Third, features such as try-on and saved product lists may raise customer expectations for richer, more interactive shopping experiences.

For marketing teams, that creates a familiar question in a new channel: how products are surfaced, compared and preferred when an AI system becomes part of the customer journey.

Who may be affected

• E-commerce businesses selling apparel, fashion accessories and visually driven consumer products
• Marketing teams responsible for product visibility, conversion and digital merchandising
• Web and IT teams managing product feeds, images, structured data and platform integrations
• Digital agencies advising clients on AI search, commerce UX and channel strategy

The impact is likely to be strongest for businesses whose products benefit from visual evaluation, fit confidence or shortlist-based shopping behaviour.

What companies should consider

• Review product data quality: clear product titles, accurate attributes, sizing information and strong imagery are likely to matter more as AI interfaces expand shopping use cases.
• Monitor AI-driven discovery channels: retailers should track whether AI assistants begin influencing product views, referral traffic or assisted conversions.
• Assess visual commerce strategy: fashion and accessory sellers may want to compare virtual try-on, richer imagery and fit-related UX across their own channels and third-party platforms.
• Check privacy implications: if customer photos are used in shopping workflows, businesses should pay attention to how image data is processed, stored and disclosed to users on any platform they operate directly.
• Avoid overreacting to a single feature launch: companies should treat this as a signal of platform direction rather than assume immediate large-scale impact without evidence from their own customers and markets.

For European businesses, the immediate takeaway is strategic rather than regulatory: conversational AI platforms are continuing to develop into commerce interfaces. Companies that depend on digital product discovery should watch closely how these environments affect visibility, customer expectations and conversion paths.