Every week, someone pitches you a different AI tool they “guarantee” will get your products in front of more buyers. The pitch is always the same: Pay for our service, and we’ll make sure the AI finds you.
The problem with that pitch is that it skips over the real issue.
AI discovery doesn’t reward the company that buys the shiniest new AI tool. It rewards the company with the cleanest possible data for the AI to find. If your underlying product data is low-quality or incomplete, that’s not something an AI platform can correct for you.
According to the 2026 Clutch brand discovery survey, nearly half (47%) of the 408 consumers surveyed expect AI tools and smarter search to be the largest force shaping how they discover new brands in the coming years. In fact, it was the highest-ranked future channel identified in the study, making it a blazing neon sign pointing directly to the urgency of the matter.
With percentages like that, every catalog operator should be nervous. Instead, most of them are wasting time worrying about the wrong thing. Your brand doesn’t have a content problem for AI to solve; the problem is with your data, and only you can solve it.
The brands that rank at the top of AI-driven discovery in catalog-heavy categories aren’t necessarily experimenting with the newest tools. Rather, they’ve done the less glamorous work: structuring, standardizing, and maintaining product data so AI systems can confidently surface their products.
We’ll break down why catalog businesses face unique exposure in the AI discovery era. We’ll also detail the five product data practices that determine AI visibility, as well as a quick audit you can run this week.
A small direct-to-consumer (DTC) brand with 80 products can get away with a lot. Someone on the team can manually update product descriptions with missing specifications and clean up inconsistencies before they become a serious problem.
Catalog-heavy businesses don’t have that luxury. When you’re managing thousands of SKUs, small data issues multiply exponentially. That’s just one reason AI discovery poses different challenges for catalog operators than for smaller brands.
Many catalog categories depend on precision. A shopper buying a T-shirt might tolerate vague product details. It’s an entirely different story for someone buying an aftermarket automotive part, an HVAC or electrical component, a marine battery charger, or a piece of industrial equipment.
Shoppers in those categories need answers to specific questions:
Purchasing decisions hinge on these details, and so does AI’s confidence in recommending your SKU.
When product data lacks details on fitment, compatibility, dimensions, or technical specs, large language models (LLMs) have less information to work with. Rather than risk surfacing the wrong product, AI systems will recommend a competitor’s listing that provides the necessary information.
Before data quality becomes a technology challenge, it’s already an operational challenge. A catalog business might manage tens of thousands of products across dozens of categories. New inventory arrives constantly. Manufacturers change specs and expand product lines. These factors make product data maintenance and content marketing two completely different animals.
Companies that perform well in AI discovery treat product data the same way they treat other operational challenges like inventory management or pricing controls, which means:
A quarterly cleanup project isn’t enough to solve a data quality issue and keep your SKU at the top of AI visibility.
Most catalog businesses don’t maintain product information in a single location. The schema usually begins with data in a PIM system. From there, it flows to multiple locations, including:
Problems arise when the data in one place no longer matches the data in another.
For example, consider a product that appears as “2.5 inch” on your website, “2.50 in” in an industry feed, and “63.5 mm” on a marketplace listing. While a human can generally recognize that those values describe an identical spec, machines don’t always make that assumption.
When an AI system encounters conflicting information across sources, its confidence in your product data suffers. Lower confidence translates into lower visibility.
Catalog operators aren’t falling behind because they’re slow to adopt the latest and greatest AI tools. Most are dealing with a bigger issue that they might not even recognize.
The data foundation you need for AI visibility becomes much more challenging to maintain as your catalog grows. When you understand that reality and treat product data as an operational asset rather than a marketing task, you’ll have a much easier time earning top visibility as AI-fueled discovery continues to expand.
The highest-impact data improvements rarely require a major software purchase. Far more often, they involve fixing issues in your catalog. If you want stronger visibility in AI discovery, these areas are worth prioritizing.
Product titles and descriptions are essential, but they aren’t enough on their own.
AI systems rely heavily on structured product information to evaluate whether a SKU matches a user’s query. Various category-specific attributes establish confidence, such as:
A single audit question can tell you a surprising amount about catalog health: What percentage of your SKUs have every category-relevant attribute populated? Many organizations assume the number is high until they measure it. Missing values accumulate over time, especially in large catalogs with multiple suppliers.
Handle attribute completeness like any other fulfillment metric.
Some best practices to follow:
By holding someone accountable and making attribute completeness reportable, you’ll know it’s receiving the attention it needs.
Machines don’t interpret product pages the way people do. Schema markup provides the structure that AI systems and search engines need to validate product information. These machines confirm the information on your product detail pages (PDPs) through:
Most catalog sites either skip schema entirely or implement it inconsistently across templates, leading to AI discovery issues.
Beyond your homepage, audit every PDP template for a valid, populated schema. To validate at scale, run pages through Google’s Rich Results Test or the Schema.org validator. Pay close attention to:
These fields often contain missing or invalid data, especially in older product listings.
Product information flows through your website, PIM, Google Merchant Center, distributor networks, manufacturer feeds, industry databases, and internal systems. Every one of those locations provides information for AI systems to evaluate.
That’s why every spec on every one of these sources must agree. Unfortunately, they usually don’t.
Small inconsistencies can spread quickly. You might update a spec on your website, but the update never reaches your marketplace listing. A manufacturer may quietly revise a measurement, but the older value still appears in a feed. These discrepancies erode AI’s confidence in surfacing your SKUs.
Centralize attribute syndication via a single source of truth and create a schedule to regularly audit feeds.
Some category structures make perfect sense to internal teams but almost no sense to customers. Buyers don’t search for “Tier 2 Subcategory 14B.” They search for things like:
Category structures that facilitate human and AI navigation are those that reflect buyer intent rather than internal taxonomy.
In this area, catalog businesses have an advantage over DTC competitors. Large catalogs naturally create opportunities to connect related products through:
These connections help shoppers find what they need and help AI systems understand product relationships.
Review your highest-performing organic landing pages, and consider whether the URL structure and category path alone would help an LLM understand what the page is about without reading the body content.
If the answer is no, make changes.
LLMs heavily prefer content from authoritative sources. For catalog businesses, these include:
A general content-hub article about marine batteries helps a category, but a manufacturer's installation guide attached to a specific SKU leads AI to that exact product.
Review content also receives substantial weight. Structured, schema-tagged reviews from verified buyers help both shoppers and AI systems confirm that the product matches the PDP’s claims.
Audit your highest-revenue SKUs to ensure each has at least one link to associated authoritative content. The products lacking supporting documentation need improvement.
With these five quick checks and a spare half hour, you can easily identify your catalog’s biggest AI discovery weaknesses. You can do it easily by working through these five quick checks.
None of these checks requires fancy new tools, outside consultants, or budget requests. They simply require a willingness to examine your data for shortfalls and fix them.
Many companies consider AI discovery a technology challenge, but for catalog operators, it usually isn’t. The real challenge is closer to home: data discipline. Fortunately, catalog businesses are particularly well-positioned to solve this problem if they’re willing to stop treating it as a marketing initiative and start treating it as an operational one.
It’s time to reallocate your budget away from AI experimentation and pump it instead into PIM hygiene, attribute coverage, schema implementation, and feed consistency. Doing so today will push your brand to the forefront of AI discovery a year from now.
The company with the flashiest new AI platform won’t dominate the future of e-commerce search. The top spot will go to the brand with the cleanest data the AI search tools can find.
