The Latest Gartner® Research Explores What's at Stake for Brands in AI Commerce

AI platforms are already influencing purchase decisions — and the criteria they use to surface products go well beyond what most brands have prepared for. 

It's not just about having the right product. It's about whether the right data exists to make that product findable, recommendable, and trustworthy to an AI system. For commerce leaders, that's a meaningful shift in what it takes to compete.

The recent Gartner research report, Optimize Product Data for Agentic Commerce, maps exactly what that gap looks like and what it takes to close it. The findings go further than most commerce leaders expect.

The Gartner research examines a gap most brands are missing

The Gartner headline projection is significant: by 2030, 20% of digital commerce transactions will be executed through AI platforms using on-platform check-out or by AI agents.

For business leaders, that number reframes product data from an operational concern into a strategic one. The brands positioned to capture that share are the ones preparing now.

According to us, the core finding is that most organizations aren't ready — not because they lack good products, but because their data infrastructure doesn't meet the bar AI platforms require. The consequence is direct: AI platforms don't recommend those products, or they recommend them inaccurately, which erodes the trust that drives conversion.

"Early movers with complete product data can gain superior positioning in recommendation and transaction workflows, making it harder for competitors to catch up."

— Gartner, Optimize Product Data for Agentic Commerce research

Good enough isn't good enough.

Gartner identifies four categories of product data AI platforms draw from:

• 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

• Organizational data: Sustainability commitments, mission, geographic presence, core values, and ESG activities

Most brands have the first two covered. The gaps show up in the other two, and the last category is the one most commerce leaders haven't considered. 

As AI platforms develop memory capabilities, they factor in a shopper's previously expressed values when surfacing recommendations — even if the shopper doesn't repeat those preferences in a given session. Brands that proactively surface their organizational story position themselves to match those signals. Brands that don't may not surface at all.

For decision-makers, the implication is clear: AI readiness is no longer just an ecommerce operations project. It's a cross-functional priority that touches marketing, brand, and leadership.

Closing the AI readiness gap with Feedonomics Data Enrichment

Feedonomics, a Commerce company, built its Data Enrichment solution to address exactly the kind of catalog gaps the Gartner research identifies. It uses AI-powered automation to enrich, standardize, and optimize product data across every channel — helping brands move from incomplete, fragmented catalogs toward the structured, contextually rich data that AI platforms require.

Five core use cases work together to cover the full scope of what AI-ready product data requires:

• Branded copy generation: On-brand product titles, descriptions, feature bullets, and taxonomy — generated at scale and consistent with brand voice guidelines.

• Attribute and taxonomy completion: Automatically fills structural gaps in product data so catalogs perform reliably across every system and channel.

• Channel-specific optimization: Platform-ready content for Amazon, Google, Facebook, eBay, and Instagram, built to meet the specific requirements of each one.

• SEO and metadata enrichment: Image alt tags, meta descriptions, search tags, and social appeal tags that improve organic performance across the site, ads, and marketplaces.

• Answer Engine Optimization (AEO): Optimizes product content specifically for how large language models discover and recommend products in platforms like Perplexity, Microsoft Copilot, and ChatGPT.

Together, these use cases give brands what they need to show up accurately and consistently wherever AI platforms are making recommendations. 

Gartner specifically names Feedonomics in this report — which, to us, is validation that the product is built for exactly this moment.

The final word

AI commerce isn't just raising the bar on product data. It's raising the bar on brand identity.

The brands that compete most effectively will be the ones that have built toward complete, structured, contextually rich catalogs — and made sure their organizational story is part of that picture.

We see the Gartner research make the case clearly. The window to act is still open.

To learn more about preparing your catalog for AI commerce, read the full Gartner research report.

Gartner, Optimize Product Data for Agentic Commerce, By Jason Daigler, Sandy Shen, 15 January 2026

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