What Is the AI Shelf?
The concept of the "AI shelf" was articulated by tech writer Deana Burke in the newsletter Boys Club and surfaced in a Modern Retail podcast episode on September 26, 2026. The idea is simple: just as brands once optimized for physical shelf placement in stores, and then for digital shelf placement in search results and marketplaces, they now need to optimize for the AI shelf, the position a product occupies when a consumer asks ChatGPT, Claude, Gemini, or another AI assistant for a recommendation.
Burke tested the concept by creating a completely fictional natural deodorant brand and asking AI bots if they would recommend it. The experiment worked: the AI recommended the fake brand based on the information Burke had placed online.
This is not a thought experiment. It is a snapshot of how product discovery is changing in real time. According to Modern Retail's September 26, 2026 episode, the podcast explored three key questions: How do personalized AI shelves shape CPG trends? How can brands get their products machine-readable for AI agents? And why is this data fundamentally changing organic and paid marketing?
From Physical Shelf to Digital Shelf to AI Shelf
The shift is a natural extension of a pattern that has been playing out for two decades.
| Era | Where Consumers Discover Products | What Brands Optimized |
|---|---|---|
| Physical shelf (pre-2000) | In-store browsing | Shelf placement, packaging, endcaps |
| Digital shelf (2000-2020) | Google search, Amazon, e-commerce sites | SEO, product page optimization, Amazon rankings |
| AI shelf (2025-present) | ChatGPT, Claude, Gemini, AI Mode | Structured data, brand authority, AI-readability |
The AI shelf is distinct from the digital shelf in one critical way: when a consumer searches Google, they get a list of links to evaluate. When they ask an AI assistant, they get a direct recommendation. There is no page two. If your product is not in the AI's answer, you do not exist for that consumer in that moment.
Jessica Wright, SVP of product at Spins Foundry, joined the Modern Retail podcast to unpack how brands can position themselves for the AI shelf. Spins Foundry works with CPG brands on data infrastructure, and Wright's perspective is grounded in the practical realities of how AI models encounter and evaluate products.
How AI Assistants Decide What to Recommend
When a user asks ChatGPT or Gemini for a product recommendation, the AI does not browse a marketplace and compare options the way a human would. It draws on several sources:
- Training data. The model's training corpus includes product reviews, brand websites, news articles, forum discussions, and any other text it was trained on. Brands that appear frequently in high-quality sources have an advantage.
- Search and retrieval. When the AI has access to the web (through search connectors or browsing), it queries for products matching the user's request and evaluates the results.
- Structured data. Product schema (schema.org Product, Offer), consistent identifiers (GTIN, UPC, ASIN), and standardized attributes (ingredients, dimensions, certifications) make it easier for AI models to compare products accurately.
- Authority signals. Brand presence, expert mentions, awards, certifications, and media coverage all contribute to whether the AI considers a brand a credible recommendation.
This means a brand can influence its position on the AI shelf through the same fundamentals that influence Google search visibility, plus a few AI-specific tactics.
Making Your Products Machine-Readable
"Machine-readable" is the phrase that comes up most often in AI shelf discussions. It means structuring your product data so AI models can find it, understand it, and compare it against alternatives. Here is what that looks like in practice:
1. Implement Product Schema on Every Product Page
Schema.org Product, Offer, and AggregateRating markup tells AI models exactly what your product is, what it costs, and what people think of it. If your product page has only HTML and images, the AI has to guess. If it has structured data, the AI knows.
2. Use Consistent Product Identifiers
GTINs, UPCs, and ASINs are the connective tissue of product data. If your product has different identifiers across different platforms, AI models may treat each version as a separate product, fragmenting your presence. Consistent identifiers across your own site, Amazon, and any other marketplace help the AI understand that all these references point to the same product.
3. Build Brand Authority Across the Web
AI models aggregate. A product mentioned in a New York Times review, a Reddit thread, a niche blog, and Wikipedia is more likely to be recommended than a product that exists only on its own website. This is not about gaming the system; it is about being genuinely present and authoritative in the spaces where your customers talk about your category.
4. Provide Rich, Descriptive Product Content
AI models cannot recommend what they cannot describe. Product pages with detailed descriptions, clear ingredient lists, specifications, comparisons, and use-case documentation give the AI the raw material it needs to match your product to a user's query. Thin product pages with a single image and a price will be increasingly invisible.
5. Monitor Your AI Visibility
Ask AI assistants what they recommend in your category. Take note of whether your brand appears, what they say about it, and what competitors show up instead. This is the simplest and most direct way to measure your AI shelf position. It is the equivalent of Googling your target keywords to check your ranking.
Why This Changes Organic and Paid Marketing
The AI shelf reshapes the economics of marketing budgets. Consider the decision journey:
- Before AI: Consumer searches Google, sees your ad, clicks through, evaluates your page, buys or leaves. You pay for the click regardless of intent quality.
- With AI: Consumer asks ChatGPT for a recommendation. ChatGPT suggests three products. If yours is one of them, the consumer may click through. If it is not, no amount of ad spend changes that outcome, because the AI's recommendation is not ad-funded.
This means the return on traditional ad spend may decline as more consumers use AI assistants for product discovery, while the return on structured data, brand authority, and content quality may increase. Brand marketing teams that invest in becoming genuinely useful and authoritative on the web will compound their advantage as AI assistants become more central to product discovery.
The Experiment That Should Worry Every Brand
Deana Burke's fake deodorant brand experiment is the part that should keep brand teams up at night. Burke created a fictional brand and placed enough information online for AI bots to recommend it. This worked because AI models cannot easily distinguish between a real brand with genuine presence and a fabricated one with well-placed content.
This cuts both ways. It means emerging brands can gain AI shelf visibility faster than they could have gained Google search visibility, because the barrier is content quality and structured data, not domain authority and backlinks accumulated over years. But it also means competitors, or bad actors, can create AI presence for brands that don't actually exist.
Conclusion
Read next:
- Amazon's Agentic Double Move: Opening Seller Central to AI While Blocking Meta's Muse
- Microsoft Autopilot: The 24/7 Cloud Agent That Works While You Sleep
- Your AI Agent or Just Automation? The Test Every Marketer Should Run
- AI Visibility in 2026: Which Platforms Actually Matter for Your Brand
Want to measure and improve your brand's visibility on the AI shelf? Contact Vivid to talk through a practical plan.
