The rise of AI shopping agents is creating a new optimization challenge for consumer brands: being discoverable is no longer enough if products cannot be accurately understood, compared and recommended by machines. Azoma, which positions itself as an Agentic Commerce Optimization platform, is outlining the capabilities enterprise brands and digital shelf teams should consider as AI increasingly enters the purchasing journey.

The development comes as platforms including OpenAI, Google, Microsoft, Amazon and Walmart expand AI-enabled commerce capabilities. Harvard Business Review reported in August 2026 that Gartner projects 90% of B2B purchases, representing more than $15 trillion, will flow through AI agent exchanges by 2028.

Market Landscape

Agentic commerce introduces a different visibility problem from conventional search. In traditional SEO, marketers optimize pages to appear in search results. GEO extends that objective to AI-generated answers. ACO takes the concept further by focusing on whether AI shopping agents can understand a product's attributes, evaluate supporting information and ultimately recommend it during a purchase process.

The infrastructure for this shift is already developing. Harvard Business Review notes that OpenAI, Google, Microsoft and Shopify have introduced agentic commerce protocols or related capabilities, while Amazon and other commerce platforms are enabling AI-assisted purchasing experiences.

Azoma's analysis of Q2 2026 data also illustrates why a single optimization strategy may be insufficient. The company reports that different AI shopping agents draw on substantially different source types. Its analysis attributes 41% of ChatGPT citations to earned media and 37% to retailers, while Gemini reportedly showed 41% retailer and 37% earned-media citations. For Walmart Sparky, Azoma reports 36% earned media and 30% brand.com, while Alexa for Shopping reportedly relied heavily on affiliate sources.

These figures are Azoma's own analysis, rather than an independently established industry benchmark.

Strategic Outlook

For enterprise marketing and digital shelf teams, the emerging requirement is therefore cross-agent visibility rather than optimization for a single AI platform.

Azoma identifies five capabilities that organizations should evaluate: visibility tracking, citation-source analysis, competitive benchmarking, product and content optimization, and scalable execution. Its own framework, developed with the Digital Shelf Institute, organizes agentic commerce around five Cs: Completeness, Context, Citations, Correctness and Customer Acquisition.

This represents a meaningful evolution in digital shelf management. Product information has traditionally been optimized for retailer marketplaces, search engines and human shoppers. AI agents add another audience: software systems that synthesize product pages, retailer listings, earned media, user-generated content and other sources before presenting a recommendation.

That makes product-data quality and external brand signals increasingly interconnected. A retailer product page may contain accurate specifications, for example, while third-party content provides the contextual evidence an AI system uses to assess credibility.

Azoma says its platform integrates with systems including Salsify and provides workflows for product-data enrichment and optimization. The company also says it supports multiple AI shopping surfaces and has worked with brands including L'Oréal, Unilever, Mars, Beiersdorf and Reckitt. These are company-reported customer claims.

The larger market opportunity extends beyond Azoma. As AI agents increasingly mediate product discovery, brands will likely need measurement frameworks that connect AI visibility with actual commercial outcomes, rather than treating mentions as the end objective.

The strategic question is shifting from “Can consumers find our products?” to “Can AI agents understand enough about our products to choose them?”

Top Insights

1. Agentic commerce is creating a new optimization layer between product data, AI discovery and purchasing.
2. Different AI agents use different information sources, making cross-agent measurement increasingly important.
3. Digital shelf teams are becoming central to AI visibility, because product data feeds the systems agents evaluate.
4. ACO extends GEO into commerce, with the objective moving from visibility and mentions toward product recommendations and transactions.
5. Measurement will increasingly need to connect AI visibility with revenue, not simply track appearances in AI responses.

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