For years, brands focused on the digital shelf basics. Ecommerce teams poured massive budgets into optimizing product descriptions, bidding on high-intent keywords, collecting five-star reviews, and obsessing over conversion rates.
These efforts had one primary goal: help the brand appear on the coveted first page of search results, with the hope that a human shopper would click, read, and purchase.
When a person searched for sunscreen — whether the top results surfaced Banana Boat and Badger or Coppertone and CeraVe — every brand wanted to win the first-page real estate.
But what happens when the shopper stops searching entirely? This blog looks at what it means to be discoverable online and how to make your products AI-ready. Because on the agentic shelf, brands compete to be understood by the AI systems that increasingly sit between shoppers and products.
Search — the long-reliable indicator of strong ecommerce performance — reflected a collection of things working really well together:
Sure, this methodology was reliable for a while, but it’s not today’s new omnichannel reality.
Instead of products ranking from content peppered with the right amount of keywords, it’s a bit more complicated, as shoppers turn to more sophisticated answer engines with increasingly complex queries.
This kind of search happens on the agentic shelf, the next frontier of commerce where autonomous AI assistants shop on behalf of humans. In 2026 (and looking ahead into 2027 AI shopping trends), consumers are increasingly searching differently. According to Salsify's "2026 Holiday Pulse Report," 51% of holiday shoppers are comfortable with agentic shopping tools.
As SEO keywords feed GEO and AEO, traditional search still matters, but instead of typing "reef-safe sunscreen SPF 50," they might ask an AI assistant:
"My family is going snorkeling in Hawaii, and we need a reef-conscious zinc sunscreen stick that blends in easily, under $20, at least SPF 50, no perfumes, and gentle on sensitive skin."
That's not a traditional keyword query. While sorting a variety of needs and preferences, the AI agent has its work cut out. It must interpret the request, determine which product attributes matter, decide whether the brand is trustworthy, and then decide which products actually satisfy the shopper.
Unfortunately, AI has no obligation to recommend the brand shoppers already know.
AI discovery has introduced lots of new abbreviations — AEO, GEO, LLM — but the fundamentals haven't changed much. Brands still need accurate product information, strong content, reliable attributes, good reviews, and consistent experiences across channels.
As Ideen Rahvar, Manager of Ecommerce Data and Analytics at Beiersdorf, put it in a recent conversation with the Digital Shelf Institute: "You want to be systematic about checking different metrics so you can see if it changed or didn't change. Personally, I'm trying to really reverse-engineer what the AI is doing and explain that back to my team so they can understand how the AI is thinking.”
During a live conference demo of ChatGPT recommending sunscreen, Rahvar says it skipped right over Coppertone. He says that moment became his wake-up call. In fact, he's spent the time since that live conference demo decoding how AI evaluates brands and is teaching the rest of his organization to do the same. Rahvar says he now calls search “the golden metric” of ecommerce.
Brands need better product data to get AI-ready — this means optimizing product information that flows into every customer-facing experience. Product data must be:
Think about sunscreen again. The AI needs to interpret lots of things: Broad-spectrum protection, SPF level, water-resistance duration, skin type suitability, fragrance information, active ingredients, size, application instructions, and other relevant attributes.
Here are five tips to ensure an AI shopper can connect a request with the right product.
1. Make sure AI understands your products: Fill the gaps in your product data. Key attributes shouldn't live only in an image, PDF, or sales material.✨ Pro tip: Look at the questions shoppers ask about your category, then audit whether your product data contains the attributes needed to answer them.
2. Make your products easy to distinguish: AI needs enough detail to understand why one product is different from another. Generic claims won't help much when every competitor makes the same ones.✨ Pro tip: Identify your three to five strongest points of differentiation and make sure they're clearly represented in structured product data.
3. Connect products to real use cases: Shoppers don't think in SKUs. Your product information should make real-world connections obvious.✨ Pro tip: Map your highest-value shopper needs to specific product attributes, benefits, and use cases. Then look for gaps in the data.
4. Give AI evidence it can trust: Recommendations need signals. This includes product content, reviews, retailer information, and other sources; they all contribute to how a product is understood.✨ Pro tip: When AI gets your product wrong, don't just fix the answer. Trace the underlying information and find out what signal is missing, inconsistent, or outdated.
5. Measure whether you're showing up: Traditional search metrics still matter, but AI asks you to consider whether your brand appears when shoppers ask relevant questions.✨ Pro tip: Build a repeatable set of prompts around your priority categories and use them as a baseline. Track which products and competitors appear, how they're described, and how that changes over time.
Search visibility today looks nothing like it did a year ago — and the concepts around AI visibility will continue to evolve.
As Salsify's whitepaper, "Mastering the Agentic Shelf: A PXM Playbook for AI-Fueled Growth," states, "The agentic shelf ... is built on context, a deeper, broader and more personalized array of use cases and data required to drive the exact right conversation."
That’s why it’s so important to focus on which attributes now influence recommendations on the agentic shelf. While your brand still needs to win the purchase, it must first win the recommendation.