In this article
The practical answer: Before you chase visibility across every AI platform, test one simple thing: can an assistant understand who your offer helps, explain why it fits and guide a buyer to the next step?
Make this a customer experiment
A founder can spend weeks polishing an offer that remains difficult for someone else to explain. AI-assisted research gives you another way to notice that gap, provided you do not mistake an answer for proof of demand.
The idea was prompted by Olivia Moore's LinkedIn post about observing a shopping agent. In her reported example, access problems and practical buying conditions changed which retailer the agent favored. We have not reproduced that test. The useful lesson for a founder is to observe the whole path from a customer's need to a workable next action.
Your first experiment does not require an autonomous checkout or a new integration. It requires one clear offer, a public page and a way to inspect what happens.
Monday: write the offer's source of truth
Complete five sentences: this is for [buyer]; they are trying to [job]; they receive [deliverable]; it costs [price or honest pricing basis]; the next step is [action]. Add the main limitation or exclusion.
An illustrative offer could be a fixed-scope product-page review for a small Shopify store: three pages reviewed, an annotated report and one working session. State what implementation is excluded. Do not borrow an invented testimonial to make the offer look established.
Publish the facts on a readable offer page. If you cannot explain the offer without a long private conversation, the first problem may be positioning rather than AI discoverability.
Tuesday: prepare buyer questions
Write three non-branded prompts: a problem question, a comparison question and a constraint question. For the example above: “Who can help a small store find why product-page visitors do not buy?”; “Compare a product-page review with a full store redesign”; and “What support fits a founder who needs a prioritized fix list but will implement it themselves?”
Use language from real customer conversations where possible. Do not paste confidential details into a public tool. Add a separate branded question to check whether the assistant accurately describes your page.
Choose one relevant assistant you already have access to. Record the date, surface, location and account or memory settings. A fresh session reduces context carryover, but it does not guarantee an unbiased answer.
Wednesday: observe discovery and understanding
Run each non-branded prompt three times in separate sessions. Save the first answer and the source links. Count how often your offer appears, whether the description is accurate and which alternatives are suggested.
If you are absent, do not conclude that the business idea is bad. A new page may not be indexed or known, the prompt may not match the audience, or the system may choose other sources. An absence is an observation to investigate.
Then show the assistant your actual page and ask it to explain the offer using only what it can verify there. This second exercise tests clarity, not unaided discovery. Keep the results separate.
Using only this offer page, explain who it is for, what is included, what is excluded, how pricing works and how to proceed. Cite the page sections supporting your answer. List unanswered buyer questions. Do not fill gaps with assumptions.
Thursday: repair one missing piece of evidence
Choose the most consequential misunderstanding. Perhaps the assistant calls a coaching session done-for-you implementation, cannot find a deliverable, or assumes the price covers ongoing support. Fix that fact on the page and in any conflicting public description you control.
Keep the change small enough to understand. A clearer scope table, a concrete example of the deliverable or a visible fit statement can be more useful than five additional blog posts.
Google says its AI search features do not need special AI markup beyond the normal requirements. Its guidance supports starting with the foundations. There is no need to buy a promised AI-ranking shortcut to complete this experiment.
Friday: test the next step and ask a person
Follow the page like a first-time buyer. Can you find the action, understand what will happen and reach a working booking or enquiry form? Stop before submitting anything unless you are intentionally running an authorized test.
Ask a prospective customer to explain the offer back to you: what would they receive, what would they still need to do, and what is unclear? Their willingness to discuss a real problem is more useful demand evidence than an assistant praising your concept.
Record one of three decisions: continue with a real customer conversation, narrow the offer, or pause because the problem or audience is still unclear. Do not use a flattering AI answer as permission to invest heavily.
What the week should produce
Keep a single page containing the offer URL, prompt set, dated results, source links, inaccurate or missing facts, the repair, and the next customer action. Note any blocked or incomplete runs rather than deleting them.
This produces a clearer offer and a testable next step. It does not guarantee inclusion in an AI answer, validate willingness to pay or establish a conversion lift.
If you are still choosing the problem, start with validating a business idea before building it. When you are ready to organize the next move, start building with Waymaker. For an established business with journey friction, read the full discoverability audit.
Sources and editorial note
Sources checked October 20, 2026. The inspiration is a reader-supplied LinkedIn excerpt by Olivia Moore (profile); the original post permalink was not supplied. Her reported shopping results are attributed observations. The audit methods and hypothetical examples here are our editorial recommendations, not measured client results.
Ready to put this into action? Waymaker helps you go from idea to your first paying customer, with AI doing the heavy lifting alongside you.
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