Most Brands Aren’t AI-Ready: Get Ahead With Feedonomics Data Enrichment
Written by
Annie Laukaitis
Key Highlights
Gartner® predicts By 2030, 20% of digital commerce transactions will be executed through AI platforms using on-platform check-out or by AI agents. Digital commerce site traffic originating from AI platforms was up 805% YoY on Black Friday in 2025.
Most brands are unprepared because their product data is too incomplete or unstructured for AI platforms to accurately interpret, evaluate, and recommend their products.
Gartner outlines four categories of product data AI platforms draw from. We see that most brands have significant gaps in semantic, outcome-based, and organizational data.
Feedonomics Data Enrichment uses AI-powered automation to fill catalog gaps, generate on-brand content, and optimize product data across every channel at scale.
Feedonomics is mentioned in the Gartner report.
The search bar isn't the only place shoppers start anymore.
AI platforms are becoming a primary layer in how products get discovered, compared, and purchased — and they make recommendations based almost entirely on the quality and structure of product data.
Brands with complete, well-organized catalogs get recommended. Brands with gaps often don't.
Gartner recently published research on exactly this shift: Optimize Product Data for Agentic Commerce. The findings are clear to us: product data quality has gone from a back-office concern to a front-line competitive factor.
The Gartner research comes with a projection that's hard to ignore: “by 2030, 20% of all digital commerce transactions will be executed through AI platforms.” That includes platforms where shoppers are already asking questions, comparing options, and completing purchases.
The growth is happening faster than most brands realize. Digital commerce site traffic originating from AI platforms was up 805% YoY on Black Friday in 2025.
That's a lot of purchase intent flowing through channels most brands aren't optimized for yet.
According to us, the core finding by Gartner is that most organizations aren't ready. Their product data is too incomplete or unstructured for AI platforms to work with accurately. That creates two real risks:
AI platforms don't recommend products at all
Shoppers receive inaccurate recommendations, which erodes trust in the brand
It comes down to how AI agents actually process information. Human shoppers fill in gaps — they make inferences, overlook missing details, and proceed anyway. AI agents don't work that way. They surface what's there. If key attributes are missing, the recommendation simply doesn't happen.
What AI-ready product data actually looks like
Gartner outlines four categories of product data that AI platforms draw from, and the list is broader than most catalogs currently support:
Master data: Identifiers, dimensions, materials, compliance certifications, and country of origin
Non-master data: Pricing, inventory, marketing descriptions, return policies, and lead time
Semantic and outcome-based data: Use cases, problems solved, benefits, and product ontology — this is where most brands have the biggest gap
Organizational data: Sustainability commitments, mission, and geographic presence
That last category is worth a closer look. Gartner notes that "While the consumer may not repeat those preferences when searching for products, as AI platforms learn consumer preferences and develop greater memory capabilities, they may seek and return that type of information when searching for products." Brands that proactively surface their sustainability practices or regional presence position themselves to match those signals.
Feedonomics frames the path toward that completeness as a five-level data maturity journey:
Level 1 — Dynamic feed attributes: Core product data that makes items searchable and shoppable across standard feeds.
Level 2 — Structured catalog data: Organized attributes and taxonomy that give AI systems the context they need to categorize products accurately.
Level 3 — Marketplace-ready: Logistics and operational data that enables localization and marketplace selling.
Level 4 — Brand-enriched data: Brand-authenticated assets that support long-form, comparison-based AI queries.
Level 5 — Contextualized and brand-rich: User-generated content and contextual detail that gives AI a full picture of your brand and product experience.
Most brands land somewhere between levels one and three. Getting to four and five is where the real competitive separation happens — and where enrichment does its most important work.
How Feedonomics Data Enrichment closes the gap
Closing those gaps, moving from level three toward four and five, is exactly what Feedonomics Data Enrichment is built to do.
It uses AI-powered automation to enrich, standardize, and optimize product data across every channel, reducing the manual burden on marketing and ecommerce teams while improving catalog quality and consistency at scale.
The five use cases below address different layers of that challenge — from the structure and completeness of catalog data to the content that helps AI platforms understand and recommend products accurately.
Here's how Data Enrichment's five core use cases work.
Branded copy generation.
Feedonomics generates on-brand product titles, descriptions, feature bullets, attributes, and taxonomy that reflect brand voice guidelines — at scale and without manual copywriting effort.
It also supports content generation in eight languages: English, Chinese, French, French Canadian, German, Hebrew, Korean, and Spanish.
Attribute and taxonomy completion.
Raw product data rarely arrives complete. Feedonomics automatically creates a taxonomy of attributes per product dataset, combining existing data with AI-generated attributes. The result is a consistent, searchable catalog that performs reliably across every system and channel.
Channel-specific optimization.
Every channel has different rules. Feedonomics generates platform-ready titles, descriptions, keywords, and fields for Amazon, Google, Facebook, eBay, and Instagram so each product meets the standards required to compete.
SEO and metadata enrichment.
Feedonomics generates image alt tags, meta descriptions, visual tags, search tags, and social appeal tags to improve organic performance across the site, ads, and marketplaces.
Answer Engine Optimization (AEO).
AEO takes AI visibility a step further by optimizing specifically for how large language models discover and recommend products. Feedonomics generates AI snippets and Q&A content that helps those models accurately surface and represent products in platforms like Perplexity, Microsoft Copilot, and ChatGPT. In fact, the Gartner research report mentions Feedonomics.
The final word
AI is becoming a primary layer in how products get discovered and evaluated, and enriched product data is what determines whether a brand shows up accurately, ranks competitively, and converts across every channel where that discovery happens.
Brands that build toward complete, structured, semantically rich catalogs now will have a real advantage as agentic commerce grows. The good news is that commerce teams can start working on that today, with tools like Feedonomics Data Enrichment.
Gartner, Optimize Product Data for Agentic Commerce, By Jason Daigler, Sandy Shen, 15 January 2026
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