Your product data already exists. AI just can’t read it. Here’s what you can do.

AEO is not SEO

Search engine optimization has been the default for product discoverability for decades, but buyer behavior has moved on. AI in B2B e-commerce has made AI assistants a primary entry point for product discovery, and the rules for getting found are different. 

The three optimization disciplines are not interchangeable, and importantly, each one adds a layer on top of the last rather than replacing it.

Schema.org markup has been around since 2011 and was developed by Google, Yahoo, and Bing. In SEO, it improved visibility and click-through rates but had no impact on rankings. 

According to Steve Vink, Principal Business Solutions Architect at Inriver, the same schema data now determines whether you appear in AI-generated answers at all, and you’re competing against other brands based on its presence and quality, not just your product’s merits.

What does AI see on your product page?

A customer visiting your product page sees your brand story, photography, and marketing copy. An AI model scans for something else entirely: schema markup, attribute fields, and technical specifications. AI recommendations are driven by structured data signals, not prose, and that’s where most brands are losing ground without realizing it.

Steve puts it plainly: AI models can’t parse ambition or emotion; they can only parse data. A product description like “maximize your experience the way it’s meant to be heard” carries no signal value to an answer engine. 

A format attribute, a genre attribute, a publication date — those do. Worth noting too that multimodal signals now play a significant role in AEO results. Having images on your product pages is essential, and file names and alt text carry real weight in how answer engines assess your authority on a product.

Think about what you currently have on your product pages. Ask yourself:

• Do your pages have schema.org markup implemented, covering Product, Offer, FAQPage, and AggregateRating?
• Are your technical specifications structured as discrete attributes or buried inside paragraph copy?
• Do your product attributes stay consistent across your brand site, retail listings, and distributor pages?
• Do you have FAQ content structured as machine-readable markup, or only as visible page text?
• If your site runs in multiple languages, does your schema include translations for all languages, not just your default language? 

The gap between what your customer sees and what AI can actually parse is where most manufacturers are losing ground to competitors with cleaner, better-structured data, regardless of which product is actually better.

How AEO-ready are you, really?

Inriver’s research shows that around 30% of manufacturers say they are fully ready for AI-driven discovery. The actual number, measured against how product data readiness is assessed in practice, sits closer to 10%. Less than 12% are even measuring whether AI assistants can find their products at all.

AEO readiness breaks down across five dimensions. Use these as a starting point to honestly assess where you stand:

• AI visibility: Do AI assistants mention your products unprompted, and do they name specific SKUs rather than just your brand?
• Content depth: Do your product pages include technical specs, use cases, FAQs, and comparison data, or are they mostly marketing descriptions?
• Message consistency: Are your product attributes identical across your brand site, retail partners, and distributors? Steve identifies this as the most underestimated dimension — a single specification contradiction on a primary channel caps your score, and a competitor with cleaner distributed data wins that query regardless of product quality. 
• Third-party signals: Do independent reviews, trade press, and certification bodies reference your products?
• Structured data: Do you have schema.org markup correctly implemented across your product catalog, and is it elevating your product data into something AI can actually act on? 

One more thing worth factoring into your self-assessment: structured data isn’t a one-time project. Price changes, new certifications, and updated technical specs all affect how AI responds to queries about your products, so treating your schema as a live asset rather than a setup task is what separates brands that maintain visibility from those that lose it over time.

Most brands score well on one or two of these and have significant gaps on the rest. The five dimensions and how they interact in practice are covered in detail in the on-demand masterclass, along with a tailored AEO readiness report scored specifically against your brand.

Three things you can do right now

Most of the data AI needs to recommend your products is already inside your PIM. The problem isn’t missing content; it’s that your existing product data isn’t flowing into machine-readable outputs. Fixing that starts with three concrete actions.

1. Create an AI-first product description field in your PIM. 

Add a separate, fact-based, attribute-driven description field with no marketing language. Your existing consumer-facing copy stays as it is, but this new field feeds directly into AI ingestion pathways. 

Most brands already have a schema description field on their product pages, but it’s typically a copy of the consumer-facing description, which carries little signal value to answer engines.

2. Map your technical attributes into structured data blocks. 

Dimensions, weights, classifications, audience, use cases, and technical specs all need to flow into schema properties so AI can use them for comparison and consideration queries. A properly configured PIM enrichment workflow handles this as a mapping exercise, not a rewriting exercise.

3. Deploy FAQPage schema across your catalog. 

FAQ schema is currently an open gap across most product categories, and the brands that act on it first capture that featured snippet real estate. You don’t need new content to do this, only structured markup around the questions your customers already ask. Your existing customer reviews are a reliable starting point for identifying those questions.

Getting these three things right is where AI for B2B product discoverability starts, and where most manufacturers still have room to move before competitors close the gap. A modern PIM with AI enrichment capability handles all three at scale, moving products from created to AI-indexed in days rather than weeks. 

Given where agentic commerce is heading, particularly in B2B, where buyers rely on AI to shortlist and compare products across complex catalogs, getting your structured data right now puts you ahead before the window closes.

Put your product data where AI can actually find it

Your competitors are working with the same constraint you are: product data that exists but isn’t fully connected to what AI can read. If you can close that gap early, you win visibility that compounds over time, because AI systems reward consistent, well-structured data with continued citation. Getting your product data into shape now puts you ahead of most manufacturers who haven’t acted yet.

Watch the on-demand masterclass AEO: What AI Sees When It Visits Your Product page to go deeper on all five dimensions and receive a tailored AEO readiness report for your brand within 48 hours of viewing.