How retailers can stay 'findable' as search goes agentic

More and more, consumers are shifting away from typing queries into a search box and sifting through pages of blue links. 

Instead, they’re asking AI directly: "What's the best women’s running shoe for flat feet under $120?" Or "What’s a good weekend bag that works with airline carry-on rules?" AI typically answers with a shortlist of items (sometimes with a sponsored placement) based on the user’s query, what it knows about the user, and what it infers as a best match to what the user wants.

Getting shortlisted product answers quickly is just the beginning of the shift to AI-enabled shopping. The next phase, agentic commerce, takes automation of shopping even further with an “agent” finding what it deems the best product and completes the purchase with little to no human intervention. 

Companies including Perplexity and Amazon have launched pilots of an agentic commerce experience while Meta has already inked partnerships with Gap, Walmart and Best Buy for its Muse agent. Wizard's agent offers built-in purchase capabilities to allow consumers to complete transactions from within the platform. It's early, but it’s clear that shopping is already on a path from "AI tells me what to consider buying" to "AI buys it for me." 

This change might understandably cause some panic for retail marketers and lead to a number of questions:

• What does it mean if an answer engine’s algorithm or an autonomous shopping agent, is deciding what gets shown or making the purchase)? 
• Does this mean our years of SEO work are obsolete? 
• Is there a real risk of our products becoming invisible to consumers?

The good news is the answer to all of these questions is no. What AI is ultimately doing hasn't changed. Whether it's a search engine crawling the web in 2015, a chatbot providing shortlisted recommendations today, or an agent completing the entire purchase (with a human-in-the-loop) the objective is the same: surface trustworthy, relevant recommendations, and soon to act on them for a shopper who’s trying to decide what to buy. 

Retailers that spent years building high-quality, robust, findable product content have a head start. What has changed is the format that "findable" now requires.

Findable means machine-readable now

SEO-trained retailers to think about how to get their products favored by Google and ranked for searchers based on keywords, meta descriptions, and technical site structure with the ultimate goal of showing up on a page designed for a human shopper to click through. 

AI works differently because it doesn’t just rank pages based on relevance to a keyword. It’s synthesizing an answer most relevant to the user’s prompt. It needs to parse, verify and trust the underlying facts about products before it recommends products for a response shortlist or an agent makes a purchase.

Product information, such as pricing, sizing, materials, and availability, needs to be structured for a “machine” to consume. In addition, shipping services, return policies, and customer service terms need to be clear and consistent everywhere they appear. An AI system pulling together a shortlist or completing a transaction will weigh it all to decide whether it can trust the retailer and make a recommendation. 

As more of the checkout process is delegated to AI agents, the need for machine-consumable data goes one step further: retailers should make sure their product feeds are clean, complete, regularly updated, and in the correct format for AI to ingest. Every product needs accurate pricing, live inventory, and variant data (size, color, SKU) provided to AI through defined formats. It goes way beyond well-written product detail pages: If an agent never “views” a product page, the page is effectively irrelevant.

All told, it's the same strategy retailers have been following – quality, accurate, relevant data about their products – but translated into a new, richer format made for AI to consume.

Retailers face a fragmented shopping journey

Another question retail marketers face is no longer just how well their brand ranks, it’s where it needs to show up and whether product information is accurate in all those places.

The consumer shopping journey used to consist of a fairly predictable channel mix: Start with a search engine or a marketplace, visit a retailer's own site, or check through a coupon site. Today, the journey might start in an AI chatbot, get refined through a price comparison site, touch a browser extension or a rewards platform, and an agentic checkout flow before (or instead of) ever reaching a merchant's website directly. 

Product data being out of sync in just one of those touchpoints may result in the brand falling out of consideration entirely.

The takeaway

Retailers don't need to rebuild the whole house. The goal AI systems are optimizing for is the same goal search engines always optimized for; connecting a trustworthy, relevant answer to a person. And increasingly, completing the transaction on their behalf. 

Instead, retailers must keep investing in providing accurate, well-structured product information, clean product feeds, and clear customer-facing policies such as their shipping minimums and return policies, to help AI continue recommending and buying their products. The strategy hasn't changed, but the format now demands much more rigor, across more channels, including the systems doing the buying.

Tristan Barnum is chief marketing officer and head of AI innovation, Wildfire Systems.