AI product discovery can shape a shopper’s shortlist before your brand earns a website visit. Your products may fit the customer’s needs and budget yet still be absent from the options an AI assistant presents.
For retail CMOs, the question is whether the right products appear accurately when shoppers describe a need, compare alternatives or narrow a purchase decision. Brand recognition alone cannot answer that question.
Start with one commercially important category. Document what shoppers encounter, distinguish missing visibility from inaccurate product information, and give each priority an owner. This focused assessment helps marketing, merchandising and ecommerce teams decide what to investigate before committing more budget.
What AI product discovery means
AI product discovery is the process through which shoppers use AI-powered experiences to identify, compare and evaluate products. A customer may describe a situation, budget, preference or delivery requirement instead of entering a short keyword query.
Google’s Merchant Center reporting groups conversational shopping queries into discovery, evaluation and ready-to-buy stages. These stages provide a useful framework for assessing different customer decisions. See Google Merchant Center AI performance insights.
This article focuses on offsite discovery: what shoppers encounter before reaching your website. Onsite search, recommendation widgets and product-page conversion answer different questions. A successful onsite recommendation engine does not establish that an independent AI assistant includes your products.
Technical readiness is another responsibility. Our Agentic Commerce article explains product data, feeds, structured information and commerce-system foundations. Here, the focus is deciding what to investigate and improve.
Separate brand recognition from product consideration
Before measuring AI product discovery, define what your team is observing. A brand mention, a recommended product, a citation and a buying link are different outcomes. A shopper can encounter one without the others. A retailer-specific app is different again.
For example, Target describes shopping experiences across Google and its own app in ChatGPT. Its instructions ask shoppers to select or invoke the Target app. This shows a retailer extending its shopping experience into another environment. It does not establish success in an unbranded, cross-retailer recommendation. See Target’s conversational shopping fact sheet.
Keep these observations separate in leadership reporting:
- Does the response mention the brand?
- Does it recommend a relevant product?
- Does it describe that product accurately?
- Does it provide a usable source or buying destination?
- Did the shopper request the retailer, or did the product appear without that instruction?
Otherwise, a dashboard can make brand familiarity look like new-customer discovery.
Start with one commercially important category
A catalog-wide assessment can produce many observations without a clear investment decision. Instead, choose a product group where the business has a reason to compete and can fulfill demand.
First, consider customer demand, inventory, delivery capacity and product differentiation. Assess contribution potential rather than revenue alone. Then look for questions or information gaps that could affect the buying decision.
The CMO should establish the commercial objective. Merchandising and ecommerce leaders should help select the products and confirm their current facts.
Build the assessment around real customer questions. Useful inputs include customer-service themes, site searches, product comparisons and recurring reasons for returns. Keyword research helps identify language and intent. However, it should not replace an understanding of the decision a shopper needs to make.
The following home-furnishings scenarios are illustrative examples, not findings from a completed retailer audit.
| Customer decision | Illustrative shopping question | Information to verify |
|---|---|---|
| Suitability | Which dining tables fit a small apartment and seat four people? | Dimensions, seating capacity and relevant limitations |
| Budget | What options fit a $600 total delivered budget? | Product price, delivery charges and offer conditions |
| Compare | How do these two models differ in size, materials and assembly? | Correct model IDs and supported differences |
| Purchase confidence | Which suitable options can arrive before my move? | Current availability and delivery information |
Build a repeatable AI product discovery assessment
Next, create a record that another team member can understand and repeat. Asking questions until the brand appears would defeat that purpose.
As a manageable starting workload, consider 20 distinct shopping questions for one category. Repeat checks on the most relevant platforms. Twenty is an operational starting point, not a statistically representative sample or a platform requirement. Keep unique questions separate from the number of repeated observations.
For each check, record:
- Conditions: platform, consumer interface, date, market and relevant account or personalization context.
- The question: exact wording, shopping constraints and follow-up sequence.
- The response: products, brands, sellers, citations and destinations shown.
- Product accuracy: whether the variant, price, specifications and purchase conditions agree with authoritative information.
- The next action: issue to investigate, commercial relevance and responsible team.
Use fresh conversations where appropriate and document exceptions. Also, keep branded and nonbranded questions separate. “Compare our Brand A table with Brand B” tests something different from “Find a dining table for a small apartment.”
Preserve screenshots and linked sources where available so the team can review each observation. Check date-sensitive details against the retailer’s current records. A small prompt sample can reveal useful questions, but it cannot establish universal AI market share.
Turn observations into actionable gaps
A missing recommendation does not explain its own cause. The product may not suit the stated need. Evidence may be incomplete, or the platform may lack current information. The answer may also change on a later check. Investigate before assigning a solution.
Possible access or eligibility gaps
Ask whether priority pages and product information are available through the platform’s supported discovery mechanisms. Have the technical or ecommerce team verify the implementation. Therefore, an absent product alone does not prove a crawling problem or failed integration.
Where technical work is needed, connect it to the foundations in the existing Agentic Commerce article. Avoid treating every missing mention as a reason to rebuild the stack.
Missing or inconsistent product evidence
In contrast, a product may appear with incorrect information. Wrong dimensions, an outdated price, an unsupported claim or a mismatched variant may be more urgent than a missing mention. These errors can create expectations the buying experience cannot fulfill.
Trace the discrepancy to the evidence. Compare the product page, catalog records, current offer and sources cited in the response. Then assign correction to the team that owns the facts. However, content cannot make unavailable stock or an unrealistic delivery promise true.
Competitive fit differences
For example, another product may better satisfy the shopper’s constraints. That is useful commercial intelligence. The response may be a clearer comparison, a more suitable product selection or a different audience.
Google continues to emphasize foundational SEO and useful, distinctive content for generative AI search. Relevant expertise and evidence matter more than generic content volume. See Google’s generative AI optimization guidance.
Measure AI product discovery with platform reporting
Manual observations provide context. In addition, native reporting can add another view, where available. Keep the scope of each source clear.
As of October 22, 2026, Merchant Center’s AI performance insights covers organic conversational shopping visibility in AI Mode and AI Overviews. Google lists English-language availability in five markets, including the United States. Paid-ad traffic is excluded.
Its share-of-voice percentage uses Google’s defined competitor set. Low data volumes or missing competitor data can produce misleading-looking extremes. Therefore, do not equate it with total retail market share or inclusion across your sampled questions. Review Merchant Center’s reporting scope and limitations.
Search Console’s Generative AI performance report measures site-link impressions in AI Overviews and AI Mode. The report may be unavailable when impressions are insufficient. It does not count every product recommendation, brand mention or purchase. See the Search Console Generative AI performance report.
For this reason, use these sources together without merging unlike metrics into one percentage. In leadership reviews, distinguish the documented checks, platform reporting and changes in product accuracy. Then state what remains uncertain. Neither a mention nor an impression establishes incremental revenue.
Turn AI product discovery priorities into owned actions
The most useful output is a ranked action list. For each priority, identify the customer decision, verified issue or hypothesis, affected products and commercial exposure. Also, name an accountable owner and any dependencies. Include the proposed effort, completion standard, retest date and evidence needed.
For example, a verified variant mismatch may require an ecommerce correction. An unclear comparison may need content and merchandising input. Meanwhile, an unsupported claim may need review by the team responsible for product substantiation.
First, correct factual errors and broken destinations. Then repeat the relevant checks under documented conditions. Record changes in price, inventory, promotions and platform behavior that could affect the comparison.
Improvement after a correction is a useful observation. However, without an appropriate test design, it does not prove that the change caused more recommendations or sales.
Questions for the next leadership review
Where should we concentrate?
Which category and customer needs offer the strongest combination of relevance, commercial value and operational readiness?
What should we fix first?
Which verified errors or evidence gaps could prevent suitable products from receiving accurate consideration?
When should we expand the work?
Has the team established a repeatable process, corrected important issues and produced evidence worth taking into a broader commercial evaluation?
Routine factual corrections need not wait for a new investment case. Broader content production or a new integration should have a clearer basis: recurring gaps, relevant demand, likely commercial value and realistic effort.
Keep diagnostic measures separate from acquisition and profitability measures. Product accuracy and observed inclusion belong in the assessment. Revenue, returns, customer acquisition costs and contribution need their own analysis.
An integrated digital marketing strategy connects SEO and AI Search with content, merchandising, ecommerce and analytics around a shared business objective.
Make AI product discovery a business priority
Start with one category and customer questions that matter. Document how important products appear, identify consequential gaps and coordinate improvements. Then retest without overstating the result.
The goal is to help suitable products receive accurate consideration and give leadership better evidence for its next investment decision.
For the broader framework connecting discovery, purchase confidence and commercial performance, download AIS Media’s AI Shopping Playbook. Use its readiness checklist and 90-day plan to focus your team’s next steps.
Need help prioritizing product visibility and content gaps? Discuss your retail brand’s SEO and AI Search priorities with AIS Media.
