How to win AI product discovery with automated attribute enrichment

The AI revolution in shopping is here, and it's powered by data. Is your catalog ready?

When people ask ChatGPT, Gemini, or Google's AI Mode what to buy, AI assistants tasked with product discovery answer with specific products. Whether yours are among them comes down to your product data.

This guide covers how shoppers find your products through AI assistants and AI-powered search, and how the state of your attributes determines whether they ever do.

The pain points behind weak AI visibility are not mysterious. They are incomplete fields, messy imports, and manual work that stopped scaling a few thousand SKUs ago. Here is where these gaps block you, and how automated attribute enrichment closes them.

Key takeaways

• AI assistants and AI-powered search match shoppers to products through structured attributes. Products with missing fields get skipped, no matter how good the product itself is.

• The stakes are already measurable: Salesforce counted $262 billion in AI-influenced online sales during the 2025 holiday season, and Adobe found AI referrals converted 31% better than other traffic.

• The same attribute gaps that hide you from AI assistants get your listings disapproved on Google Shopping, Amazon, bol, and Meta.

• Spreadsheet fixes decay as your catalog grows. Automated attribute enrichment with human review keeps data complete at scale.

• Enrichment only holds as a continuous system: extract missing values, validate them with rules, and sync them to every channel on every update.

How you enrich your catalog with Channable's Product Content Generator

Channable's Product Content Generator turns catalog enrichment into a working pipeline inside your feed setup.

You select the product fields the artificial intelligence may use as input, such as brand, material, size, and specifications, define the prompt, and choose the output field and language.

Because Channable's AI text generation is grounded in your own data, the output describes what the product is instead of inventing details. This feature helps you rewrite thousands of titles and descriptions to match the conversational way people ask AI assistants for advice.

With Channable, you're in control at every step. You can review suggestions product by product, approve in bulk, or set auto-approve rules for new items as they enter your feed, and run it all on your own OpenAI or Gemini account.

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It works alongside Channable's AI attribute enrichment, which extracts missing values like color, material, and weight. Everything flows into the same rules, channel templates, and quality checks that push your feed to 3,000+ channels.

As AI capabilities in search keep expanding, that combination of complete attributes and natural language content is the foundation of durable AI product discovery.

But here's the reality: if your catalog lacks the attributes AI systems need to match shoppers to products, the generator is only half the battle. The other half is understanding exactly which gaps are costing you visibility.

How incomplete product attributes block AI discovery

AI and agentic commerce influenced 20% of global online sales during the 2025 holiday season, worth $262 billion. Over the same period, Adobe measured a 693% year-over-year increase in traffic from generative AI tools to US retail sites, with those visitors converting 31% more often than shoppers from other sources.

Every one of those sales started the same way: an AI system read structured product data and chose which products to recommend. Here's what happens when attributes are missing or inconsistent.

Why AI assistants and search need structured attributes to find your products

Google says its Shopping Graph holds more than 50 billion product listings, refreshed over 2 billion times per hour, each carrying key details like price, availability, reviews, and color options. That graph is what AI Mode filters when someone describes a use case in plain language.

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OpenAI takes the same approach: merchants push a structured product feed that ChatGPT ingests and indexes, where required fields make a product displayable and recommended fields improve its relevance in results.

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Let's say a shopper asks for a waterproof hiking jacket with a hood, under €150, good for autumn in Scotland. The assistant filters on material, feature, price, and seasonality attributes to assemble relevant answers. If your feed says "Jacket, blue, €129" and nothing else, there is little to match on, and the model recommends a competitor whose data is more complete.

Shoppers are increasingly handing that filtering and comparing over to AI assistants, which moves decision-making toward the listings with data the machine can read.

The behavioral data says this shift in user behavior is accelerating faster than catalogs are adapting. Adobe's April 2026 AI traffic report found AI referrals to US retail sites grew another 393% year over year in the first quarter of 2026, converting at record rates. Despite this, the average retail product page scored just 66% on machine readability. That means roughly a third of product page content is currently invisible to the systems shoppers now turn to.

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How missing fields get your listings rejected on ad channels

Google Merchant Center disapproves or limits items that lack required attributes, such as a GTIN where one exists, or color, size, gender, and age group for apparel in many countries.

Disapproved items cannot serve in Shopping ads or Performance Max at all. Marketplaces run the same game: Amazon and bol block or suppress listings missing category-required fields, and Meta rejects catalog items without core fields like availability, condition, and image links.

Most teams find out at export time, when the error report lands and a slice of the catalog silently stops selling.

Plus, the damage doesn't stop there. Listings that omit size, material, or compatibility details generate avoidable returns and support tickets, because shoppers bought on incomplete information.

Why your data gets messier as your catalog grows

We've all been there: your catalog grows, and suddenly, you're drowning in a sea of inconsistent data.

Every new supplier import arrives with its own field names, units, languages, and gaps, so the same red jacket enters your system as "bordeaux," "wine red," and "burgundy."

Each new channel adds required fields your source data never contained. Each new market adds a language your attributes were never written in. Variants multiply rows, seasonal ranges churn the assortment, and historical data from old imports sits in half-standardized formats nobody owns.

Many teams add channels and suppliers faster than they add data processes. The result is a catalog in which the gap between what channels require and what the feed contains widens every quarter, quietly shrinking the share of products that are even eligible for discovery.

Why fixing it in spreadsheets doesn't scale

Exporting the catalog, filling blanks by hand, and re-importing may work at 300 products. At 30,000, it collapses.

Manual entry introduces new errors while fixing old ones, and the export is stale before the work is even finished; prices, stock, and assortment kept moving.

Shared files complicate team collaboration: two people edit different copies, and you spend more time trying to align stakeholders on which version is current than improving the data. Endless alignment meetings replace fixes.

The structural flaw is that spreadsheet corrections live outside your feed. Nothing validates values against channel specs, nothing pushes the fix to the other channels you sell on through separate tools, and the next supplier import overwrites the manual effort entirely.

How you can automate attribute enrichment to improve AI product discovery

AI product enrichment works as a layer on top of your existing feed rather than a replacement for it. AI extracts or generates the missing values of each product, rules validate them against channel requirements, and automated syncs carry them to every channel. Three steps, running continuously.

How you extract missing attributes automatically

Most missing values hide in plain sight. The color, material, and gender absent from your structured fields usually sit inside titles, descriptions, spec sheets, and other qualitative data from suppliers.

AI extraction reads those fields and proposes structured values for the empty ones, at catalog scale, in minutes instead of weeks of manual effort.

Generic AI models with no business context will invent plausible-sounding values. Extraction grounded in your own product data pulls out what is verifiably there and leaves a gap where nothing supports a value, which is exactly what you want. Human judgment stays in the loop through review queues that add the human context a model lacks: approve, reject, or edit suggestions before they touch a live feed.

Test ideas on one category first, refine your prompts based on what the review queue shows, and switch on auto-approval once accuracy is proven. That feedback loop builds continuous learning into the workflow instead of leaving quality to chance.

How you sync enriched data across channels to keep it healthy

Enrichment means little if the fixed data only reaches one channel.

Each platform needs the enriched values mapped into its own template, in its own allowed values and formats, and rules let you automate compliance with those specs instead of reformatting by hand.

From one source of truth, the corrected color reaches Google as "red," the marketplace as its permitted value, and the ad channel in the right character limit.

Health then becomes a monitoring job rather than a rescue job. Automated quality checks flag new gaps on every import, before a channel rejects the item, and frequent syncs keep attributes, prices, and stock current everywhere you sell.

Watch success metrics like disapproval rate, active item share, and impressions per channel.

Emerging patterns in error reports become actionable insights when you can trace a failure to a field and fix it with a rule that applies to every future import.

How structured, natural language attributes make your products easier to surface

Structured fields do the filtering. Natural language does the matching. Assistants respond to conversational prompts, and Google's November 2025 AI Mode update was built for exactly that: surfacing products from plain-language questions about use cases and environments.

So pair clean attribute values with titles and descriptions written the way your target users ask: contexts, use cases, and benefits, in their words rather than internal jargon.

You already have a source for that language. Ongoing user feedback in reviews carries user pain points, user sentiment, and the exact vocabulary of your customers, and AI can surface patterns and emerging themes in it far faster than manual reading.

Feed those phrases back into your attributes and copy. When an assistant compares two jackets on warmth or packability, those AI insights come directly from the fields and text you supplied. Writing to real customer needs is what earns the recommendation.

How to improve product discovery with AI: Getting started with automated enrichment

Run a quick competitive analysis: ask an AI assistant to recommend products in your main category and note whether you appear and what data the winners expose.

Audit your required and recommended fields per channel to quantify the gaps, then enrich your highest-revenue categories first with review switched on.

Channable brings extraction, validation, and syncing into one platform, so your catalog stays complete everywhere you sell.

Try Channable for free

Sources

• Adobe
• Google
• OpenAI