For eCommerce Directors, CMOs, and heads of digital commerce that want to understand how AI shopping assistants and agents now decide which products to recommend — and make sure their catalog is the one being chosen.

 

A new layer of retail visibility is being built, and most retailers are about to find they’re losing demand inside it: ChatGPT, Perplexity, Gemini, Claude, and the first shopping agents now read product catalogs and decide which brands get recommended to a shopper. We built a Catalog AI-readiness Diagnostic and ran it across UK and European retail catalogs, and found the gap between catalogs ready for the AI shelf and catalogs that look healthy but are invisible to AI shoppers is rarely a data hygiene problem. It’s a category problem the PIMs built for data governance were never designed to solve. This session shows what separates the catalogs winning AI-driven demand from the ones losing it, and makes the case for the AI-native PIM as the system retail needs next.

 

What you can expect to take away:

- How LLMs and shopping agents now choose which brands get recommended, and the signals that determine whether you capture AI-driven demand or lose it to a competitor

- The five highest-leverage fixes that turn an invisible catalog into a recommended one, ranked by revenue impact and effort, drawn from real audits across multiple retail verticals

- A real case study: how one multi-brand retailer launched a full new vertical of 8,361 SKUs in seven days with a single catalog manager, and what that velocity unlocked in demand capture

- What separates an AI-native PIM from the catalog systems most retailers run today, and how to tell whether yours is built for growth or just governance, plus a free Catalog AI-readiness audit you can run on any URL

 

Level it’s for: Director / Head of / C-level