How should retailers prepare for AI search and shopping?

Prepare for AI search and shopping by making your products easy to understand, your offer accurate and your buying journey dependable. Then assess which distribution channels and integrations deserve investment. For an ecommerce business, the useful outcome is a customer finding a suitable product and completing a purchase the business can fulfil profitably.

That requires decisions across merchandising, marketing, technology and operations. A visibility report alone cannot tell you whether those parts work together.

Define which part of the buying journey you are improving

Separate three situations: an assistant helping someone research a purchase, a shopper arriving on your website from an AI experience, and an agent interacting with a store on someone’s behalf. Each creates different requirements for information, measurement and control.

For an illustrative furniture retailer, research might involve checking dimensions, materials and delivery constraints. The subsequent buying journey needs the correct variant, current availability and a delivery promise the retailer can honour. An integration that can place orders introduces further questions about authorisation, cancellations, payments and support.

Agree which situation matters to your customers before commissioning a solution. Making product information clearer is a different scope from enabling transactions through another platform. Each should have its own commercial case and acceptance criteria.

Make the catalogue useful for a buying decision

Review a representative category with the people who understand the products and customer questions. Identify the facts needed to judge suitability: measurements, compatibility, materials, care requirements, included components and meaningful differences between variants.

In the furniture example, a polished description will not resolve whether a chair fits under a particular table or can remain outdoors. Those answers need reliable product facts. If the source information is incomplete, generating more descriptions can make the uncertainty harder to spot.

AI can be considered for drafting, classification or flagging gaps, with an agreed review process. Product claims, identifiers and operational promises need authoritative sources. Establish who can approve a correction and how it reaches the storefront, product feeds and customer service material.

Prioritise gaps using customer questions, returns reasons and category importance. A small, commercially meaningful range can be a better starting point than rewriting the entire catalogue.

Connect the information you publish

Google’s product structured-data guidance describes two complementary ways to supply product information: markup on product pages and feeds in Merchant Center. Using both helps Google understand and verify the data and can broaden eligibility for shopping experiences. It does not guarantee a particular appearance.

Give your team a concrete reconciliation task. For selected products, compare the visible page, structured data, merchant feed and checkout. Check identity, variant, price, availability, shipping and returns information. Decide which system owns each field and how quickly a change must reach the other places it appears.

Map the actual stack. A retailer may hold product content in a product information management system, stock in an ERP, and the customer-facing offer in an ecommerce platform. Ownership and update rules matter more than adding another layer that repeats conflicting information.

Google’s generative AI search guidance says established SEO practices remain relevant. It does not require special AI markup or an llms.txt file for Google Search. Check each other channel’s current requirements separately; Google’s guidance is not a universal specification for every assistant.

The linked Google documentation was reviewed on 25 September 2026.

Test the route from discovery to a fulfilled order

Ask a reviewer to complete realistic customer tasks on mobile and desktop. Can they find the correct product, distinguish variants, understand the full delivery cost and get help with an exception? Test an unavailable variant and a delivery address with restrictions as well as the straightforward purchase.

For any proposed agent integration, test what the system can read and what it can change. Define approval requirements, authentication, duplicate-order protection and the response to a partially completed transaction. Public product information and a customer’s account or order details require different access decisions.

Include customer service and fulfilment in acceptance. An experience that promises an unsuitable replacement or misses a delivery restriction can transfer the work into complaints and returns. Assign responsibility for resolving the underlying information or process problem.

Measure discovery alongside commercial results

Choose measures that distinguish visibility, customer behaviour and financial outcomes. Review relevant referrals and landing pages alongside product engagement, completed purchases, contribution, returns and support demand. Use consistent definitions and allow for the time between ordering and the eventual return or service outcome.

Google’s Generative AI performance report reports impressions for its supported AI search features. Use that as visibility evidence alongside business records, while keeping attribution gaps explicit.

If you use third-party prompt or citation monitoring, ask which prompts, locations, products and interfaces were tested, and how the sample was selected. Treat it as a defined observation you can investigate. Establish what decision would change if that number rose or fell before committing ongoing budget to it.

Protect work that helps customers recognise and choose your business directly: a clear offer, useful advice, consistent service and a reason to return. Assess AI discovery within the wider acquisition and retention plan.

Sequence the work across the business

Start with one category and an agreed set of buying tasks. Record the information gaps, channel requirements, customer friction and operational dependencies. Give each an owner and compare the likely benefit with other work competing for the same people and budget.

That review may justify better catalogue information, an integration, a customer experience change or a more focused experiment. My AI advisory and fractional digital and ecommerce leadership work connects those choices with commercial priorities, delivery and the teams responsible for the result.