By Aaditya Raghavendran, Vertical Head – Retail & Marketplaces, LatentView Analytics
As more than 30% of consumers use Generative AI before purchases, according to a Deloitte Holiday Report, the new retail battle becomes about visibility in AI-assisted searches. Shopping powered by AI agents moves toward a world in which AI anticipates buyer needs, leverages purchase history, and calibrates decisions — all in alignment with human intent, yet acting independently through multistep chains of actions enabled by reasoning models.
Several US retailers like Walmart, Etsy, and Shopify have partnered with OpenAI to ensure they appear at this new point of discovery. In India, MasterCard gave a glimpse of autonomous shopping showcasing its AI agents buying products without manual intervention at the India AI Summit.
For brands, retailers, marketplaces, and payment providers, this brings both opportunity and risk — demanding new approaches to product visibility, trust, and engagement.
From SEO to GEO: Why the Rules of Discovery Changed
Search Engine Optimization (SEO) is built around ranking webpages for keyword-based search results. AI assistants work differently — they interpret intent, synthesise information, and deliver a direct answer instead of a ranked list. When a shopper tells an AI model, “Here’s my budget, tell me the best television I can buy,” the system aggregates expert reviews, compares listings across retailers, and evaluates pricing and availability. And AI-generated answers already drive up to 10% higher engagement, shows a BCG Report.
This is where Generative Engine Optimization (GEO) becomes critical. It is a focused way of structuring content so that AI systems can interpret and summarise it. If the AI cannot ingest product data cleanly, it will be invisible to the autonomous shopper. AI ranking signal now considers:
Relevance and Recency: Is the information current, and does it directly answer the query’s intent?
Authenticity and Authority: Does the source carry high trust (e.g., industry experts, recognised publications, high-volume user reviews)?
Depth of Information: The AI needs structured facts. It uses granular product specifications, comprehensive FAQs, and a consistent history of user ratings.
As retailers rethink readiness for Agentic Commerce, data signals must be strengthened to support AI agents’ decisions. This means ensuring product information is accurate, transparent, and verifiable through strong data quality, lineage, governance, and ethical standards for agents to recognise brands as safe and reliable.
Three Must-Haves for AI-Aware Retailing
For retailers and sellers, this shift from SEO to GEO requires an immediate, operational pivot in digital merchandising. Here’s how you can get started:
1. Optimise Data Architecture & Product Data
AI systems prefer consistency and structure. For marketplaces, where multiple sellers contribute content, and for retailers managing large catalogues this is critical. Retailers must ensure real-time APIs for price and inventory. For example, when an AI agent searches for a laptop model, a retailer’s data should be updated with the latest price, stock status, and delivery date. If unavailable, it should show alternatives.
Product information must also be clearly structured with standardised attributes, detailed specifications, complete FAQs. Schema markup must be used to label data. If a product has multiple reviews, ensure the metadata is consistently formatted for easy aggregation. This allows AI agents to interpret, compare, and summarise products without ambiguity.
2. Leverage Rich Media as Data Points
Visual content, such as product images and videos, are no longer just persuasive assets, they are visual data points that Generative AI models use to filter, validate, and recommend products. This means high-resolution images that show scale, common use cases, and every relevant angle. Product videos need crisp transcripts and labels for AI to match visual cues and descriptions to shopper needs (e.g., “blue running shoes with arch support”).
3. Continuously Measure and Experiment
After implementing GEO changes, retailers must track how often AI agents surface their products (their AI shelf share), whether AI-referred shoppers convert at higher rates or show higher average order value than traditional search users, and what percentage of overall traffic now originates from AI agents or AI browsers. Equally important is validating that structured updates are accurately reflected in chatbot summaries.
Ensure Trust and Safety to Protect Brand Visibility
In this world of AI shopping for humans, trust remains the ultimate ranking signal. AI systems can hallucinate product attributes, misinterpret claims, or amplify manipulated reviews, and once that misinformation enters an agent’s summary, it can persist across countless consumer interactions, shaping perception long after brands have corrected the source. This creates a new kind of reputational risk as a brand also becomes defined by what information AI systems pick up about it.
Retailers must check for misrepresentation, fake reviews, inconsistent metadata, and drift between what the brand publishes and what AI systems display. In an agent-mediated marketplace, brand protection is no longer a marketing concern — it’s a data governance mandate.
Courting a full cart
When humans outsource shopping, retailers lose out on direct customer interactions, loyalty, and the ability to shape decisions. Retailers now need to understand not just what consumers buy, but why their AI agents select certain options. This deeper insight enables faster adaptation across pricing, promotions, and experiences, ensuring brands remain visible, relevant, and preferred in AI-mediated shopping journeys.
For Indian retailers, the first step now is alignment. They should prioritise establishing a single, verified Source of Truth for all product data, ensuring every team — data, merchandising, e-commerce, and R&D — is working from the same structured, machine-readable foundation.
In the US, AI-led traffic is converting 30% higher than traditional search, shows an Adobe study and this number will likely double by the end of the year. Companies that don’t become agent-ready won’t just lose visibility; they’ll lose their market share.