AI can read your store. That does not mean it will recommend you.

Answers the question: Can I make AI assistants recommend my products?

If shoppers increasingly rely on AI shopping agents, software that researches and compares products on their behalf, appearing in those conversations could become a valuable discovery opportunity. Confusing readability with recommendation, though, can lead businesses to spend money on tactics that promise outcomes no merchant can reliably control.

The first challenge is whether AI can understand your products

Before an AI system can accurately evaluate a product, it needs access to information about that product.

Consider a store selling nutritional products. A shopper asks an assistant to compare two options based on ingredients, serving size, price and dietary requirements. For the comparison to be meaningful, the system needs to retrieve and interpret relevant information. Product titles, ingredient lists, specifications, pricing and other attributes must be sufficiently clear and accessible.

If those details are missing, contradictory or difficult to retrieve, the system may not form an accurate understanding of the product. This is where technical readiness matters. Product feeds, structured data, accessible page content and supported interfaces can help expose the information that automated systems need. JSON-LD, code that labels product details for machines, can provide structured product information, while catalogue integrations can offer another way for supported systems to access product data.

These measures address the first challenge: making the store’s information understandable. They do not determine what an AI assistant ultimately chooses to recommend.

Understanding, retrieval and recommendation are different stages

It helps to separate AI-assisted product discovery into three distinct stages.

Stage 1: understanding

Can the system interpret the product’s attributes, price, availability and relevant policies? Merchant influence here is direct, through data quality, structure and accessibility.

Stage 2: retrieval

Can the system discover or access the relevant product through its available search, browsing, feed or integration mechanisms? Merchant influence here comes through supported access routes, crawlability and integration compatibility.

Stage 3: recommendation

Does the assistant select or present the product for a particular shopper’s request? Merchant influence here is indirect. The assistant’s own selection and ranking processes also matter.

These stages are related, but they are not interchangeable. A product may have well-structured data and still not be retrieved for a particular query. It may be retrieved but not selected as a recommendation. A product that is recommended in one context may not be relevant in another. The merchant can improve the foundations, but cannot dictate every downstream decision.

Why the promise of a single AI optimisation hack is misleading

The search for a shortcut is not new. Ecommerce has always attracted tactics promising rapid improvements in visibility and conversion. AI commerce has introduced a new set of potential shortcuts: publishing a particular configuration file, adding a specific prompt instruction, or implementing a technical standard and expecting guaranteed recommendations.

The problem is not that technical standards or configuration files are inherently useless. The problem is the assumption that declaring information or enabling a capability forces an external AI system to behave in a particular way.

• Publishing llms.txt does not guarantee that AI systems will retrieve or prioritise a store.
• Adding structured product data does not guarantee that an assistant will recommend those products.
• Making a website technically accessible does not guarantee that every AI platform will visit it.
• Improving AI readiness does not guarantee a particular conversion rate or revenue increase.

A technical improvement should be evaluated according to what it actually enables, not what a marketing claim says it will produce.

Why the distinction matters for ecommerce businesses

This distinction affects how teams allocate resources. If a business assumes that AI recommendations can be purchased or engineered through a single tactic, it may overlook the underlying quality of its catalogue and website. A more disciplined approach starts with the factors the business can assess directly.

1. Establish data quality. Review product titles, descriptions, variants, prices, stock information and attributes for accuracy and consistency.
2. Assess technical accessibility. Determine whether relevant product pages and structured information can be accessed through the mechanisms used by the systems being evaluated.
3. Review operational clarity. Make sure delivery conditions, returns, payment options and geographical limitations are accurately communicated.
4. Test and monitor. Where practical, examine how different systems interpret representative products and queries. Document the findings, recognise that results can vary, and avoid treating a single successful test as proof of universal visibility.
5. Separate readiness metrics from outcome metrics. A technical audit measures the state of a store. AI-referred visits, assisted conversions and sales measure different downstream outcomes.

This framework helps teams prioritise work based on evidence instead of promises.

Measuring what you can control

The shift toward AI-assisted commerce creates a need for more structured ways to assess ecommerce stores. Anphonic’s free scan checks what an AI shopping agent can actually read on your public pages: whether crawlers can reach them, whether product details are labelled in structured data, whether checkout and delivery details are clear, and whether product facts are stated plainly. It reads public pages only, needs no login and never attempts a purchase. It measures readiness, which is the part you control, and never predicts a recommendation.

For ecommerce leaders, this creates a more useful starting point than asking whether a particular tactic can guarantee a recommendation. Instead, ask:

• What information about my products is available to automated systems?
• Are key product details structured and consistent?
• Are there technical or accessibility issues that need attention?
• Which gaps should be addressed first?
• How will I distinguish improvements in readiness from changes in actual business performance?

These questions lead to a more measurable and realistic approach to AI commerce.

Build the foundations, then measure the outcomes

AI-assisted product discovery is changing how shoppers may find and evaluate products. Ecommerce brands should prepare for this shift, but preparation needs to be grounded in what they can actually control.

Merchants can improve the clarity of their catalogues, the accessibility of their websites and the accuracy of their product information. They can evaluate supported integrations and monitor what happens as AI discovery evolves. What they cannot do is guarantee that a particular assistant will recommend their products.

AI commerce readiness should be treated as a measurable technical and operational discipline, not a promise of rankings, recommendations or instant revenue. You cannot control every AI recommendation. You can take responsibility for how ready your store is to be understood.