AI Is Transforming Product Discovery, and Many B2B Product Pages Are Invisible

Your product pages may look perfect to buyers: Polished imagery, clear descriptions, and well-designed layouts, but if AI systems can’t understand them, they might as well not exist. This is the new reality B2B businesses are stepping into when it comes to product discovery. You can build an exceptional storefront, but buyers will only find it if AI-driven discovery tools send them there. And right now, many AI systems scraping websites aren’t recognizing products at all. They see unstructured text, disconnected media, and pages without the machine-readable signals they depend on. No product. No pricing. No availability. No context.

For businesses running Sage or Microsoft Dynamics 365 Business Central, the gap becomes even more noticeable. Your ERP already contains the authoritative source of truth for pricing, inventory, product data, and customer-specific rules. But unless your eCommerce front end structures and exposes that data correctly, AI cannot interpret it, and therefore cannot recommend you.  

This article reflects key insights from a recent in-depth conversation with Carlo Alberto Cuman from Wenstein Beyond Digital, whose expertise in AI-driven product discovery helped shape the core ideas discussed here.

AI Is Now the Primary Gatekeeper of Product Discovery

Traditional search is rapidly being replaced by AI-generated answers. Studies predict that by 2026, more than 60% of product discovery will come from AI-driven tools rather than search engine results pages. Platforms like Google’s Search Generative Experience, Bing Copilot, ChatGPT with browsing, Perplexity, and even voice assistants no longer show ten blue links. Instead, they present a synthesized answer, complete with product recommendations, comparisons, specs, prices, and availability.

If your products aren’t included in that summary, buyers never see you. B2B buyers are already turning to these systems to answer questions about compatibility, performance, inventory availability, and suitability. If AI can’t read your product pages clearly enough to understand what you sell, you will not appear in these results, no matter how strong your product pages look to human visitors.

Why Most Product Pages Fail in an AI-Driven Landscape

Product pages have traditionally been built for human readers. A user can visually scan a page and immediately understand the product name, its features, its price, or whether it’s in stock. AI cannot do that. Without proper structure, AI cannot understand what the product is, where it fits in the category, how it compares to alternatives, whether it is available, or whether it matches a buyer’s specific query.

Research shows that more than a third of all eCommerce sites use no structured data at all, and many that do implement it incorrectly. This is especially true for ERP-driven businesses, where teams assume that because the ERP contains all the right product information, the website automatically reflects it. But unless product pages are structured properly with clear schema markup, AI systems simply cannot interpret the data. And when AI isn’t confident in the information it finds, it moves on to competitors who provide the clarity it needs.

Why This Is a Critical Moment for B2B Sellers

B2B buyers are increasingly using AI-assisted research to identify suppliers, compare alternatives, and validate technical specifications. When a buyer asks an AI assistant for the best replacement part, the top supplier for a specific component, or which distributor has certain products in stock today, AI relies on structured, machine-readable product data, not on unstructured descriptions.

When businesses implement structured data correctly, the results are significant. Product pages with schema markup tend to earn dramatically higher click-through rates, better placement in generative AI results, and more qualified traffic. Competitors who have already invested in these improvements are not just ranking better, they’re being actively recommended by the systems buyers rely on.

How to Prevent Invisible Product Pages with Machine-Readable Structure

Carlo outlines four foundational principles for fixing invisible product pages. These principles become even more powerful when applied to B2B businesses that depend on their ERP as the single source of truth.

1. Make Product Schema Markup Complete and Accurate

Product schema tells AI exactly what your product is. Every product page should include fields such as name, description, images, brand, SKU or GTIN or MPN, price, availability, currency, shipping details, reviews, and ratings. This information should be implemented in JSON-LD format, which is the format most search and AI systems prefer. All markup should be validated using Google’s Rich Results Test to ensure accuracy.

For B2B companies, including proper identifiers is especially important. Many industrial, distribution, and wholesale buyers rely on MPN or GTIN matching. If AI can’t confidently identify your product in relation to others in the market, it simply won’t include you.

2. Write Product Descriptions AI Can Understand and Quote

Most product descriptions are too short, too vague, or too focused on buyers who already know the category. AI needs contextual information that explains what the product is, who it’s for, what problem it solves, how it compares to alternatives, and how it’s used in real applications. A strong product description typically includes 200 to 350 words that clearly articulate this context in natural language aligned with how real buyers search. When AI can extract meaningful, quotable text directly from the page, it becomes far more likely to recommend the product.

3. Strengthen Context with Product-Specific FAQs 

AI tools prioritize content that answers specific user questions directly. Adding three to five product-specific FAQs helps AI understand the details that buyers care about most: Compatibility, sizing, usage, performance, installation, lead times, and more. These FAQs should also be marked up with FAQPage schema, which increases visibility in “People Also Ask” results, voice search, and generative answers. This strategy helps position your page as the most authoritative source for a given product.

4. Keep Pricing and Availability Accurate Across Every Channel

AI cross-checks multiple data sources when evaluating products. If your website says one thing, your marketplace listings say another, and your ERP shows something different, AI loses trust and simply avoids your products altogether. This is why businesses must treat product data as operational infrastructure, not as content to be manually updated across channels.

For ERP-first sellers, this is where commercebuild’s approach becomes essential. Because product data, contract pricing, customer rules, and inventory already live in the ERP, an eCommerce platform that syncs this information in real time ensures that pricing, availability, and structured data stay consistent everywhere. That consistency is exactly what AI systems rely on.

The Impact: Structured Data Drives Visibility and Revenue

The results of implementing structured data are compelling. Sites with strong Product schema see 20 to 40 percent higher click-through rates. The majority of page-one search results include schema markup. Many businesses experience major gains in organic traffic, revenue, and visibility after implementing structured data correctly.

Carlo highlights examples including a full-catalog schema rollout that increased organic traffic by 35 percent in just three months, a B2B supplier that grew revenue by 48 percent year-over-year from organic search, and Rakuten’s improvement to 2.7 times their previous organic traffic by leveraging strong product markup. 

These improvements reflect a simple truth: structured data makes your products visible, both to people and to AI.

AI Can Become an Extension of Your Sales Team

When your product pages provide the right signals, AI systems can confidently recommend your products across dozens of touchpoints. This includes AI chat interfaces, voice search platforms, comparison engines, product finders, and visual search tools. AI can also surface your product data in Google Shopping free listings, automatically categorize your products based on identifiers, and even act as a preliminary salesperson by explaining your product’s value before a human ever interacts with the buyer.

Rather than replacing human sales teams, AI amplifies their reach, when your product data is structured correctly.

Balancing Machine-Readable Data with Human-Oriented Content

Optimizing for AI does not mean abandoning human readers. The most effective product pages marry two layers: a structured data layer for machines and a narrative layer for people. AI extracts facts, technical details, and relationships, but buyers still connect with benefits, context, and brand voice. When both layers work together, the result is a product page that wins visibility and conversions.

Start With a Simple, High-Impact Experiment

You don’t need to optimize your entire catalog at once. Start by selecting five high-value product pages and enhancing them with complete schema markup, improved descriptions, structured FAQs, and accurate pricing and availability. Then monitor impressions, search queries, and click-through rates over the next month. The improvement will demonstrate how impactful machine-readable content can be, especially for ERP-run businesses with large catalogs.

Final Thoughts

The question to ask is simple: would AI clearly understand that your product page is a product page? For many businesses, the answer is no. But with the right structure, clear signals, and consistent data, you can transform how AI systems discover, interpret, and recommend your products.

If you’d like help evaluating your product data strategy or structuring your catalog for AI-driven discovery, the commercebuild team is here to support you. For businesses that rely on their ERP as the backbone of operations, getting product content right is not optional, it’s foundational to future growth.

Carlo Alberto Cuman

Carlo, the mastermind behind Wenstein | Beyond Digital, orchestrates a symphony of digital marketing success. Since 2016, his agency has been a beacon in Search Engine Marketing (SEO and Paid Search), guiding companies worldwide to prominent online visibility and organic digital discovery. Readers who want to explore these concepts in greater depth can find Carlo’s original articles on Wenstein.com