Agentic commerce is going to make "who checked that?" a much more interesting question in your next marketing meeting.
Imagine a backpack sold as fitting 16-inch laptops. The product page says it. The feed repeats it. A video confidently says it too.
Then a customer asks whether their laptop fits with its protective case on.
Suddenly, three published claims turn out to be one unverified assumption copied three times. This is a hypothetical example, but the management problem is clear: distribution made the claim louder before anyone made it more precise.
A shopping assistant adds another place for that answer to travel. Someone at the merchant still needs to know whether it is true.
The interesting number doesn't answer the budget question
Shopify reports that 71% of its AI-attributed orders in 2025 came from categories outside the top 100 by gross merchandise volume. It defines AI attribution as a buyer's discovery path including an AI-powered channel. The analysis uses aggregated platform data and Shopify's category taxonomy. Shopify's methodology and findings.
That is an interesting signal about specialized products. It does not mean 71% of all Shopify orders came from AI. It also does not establish how many purchases would have happened anyway.
Similarweb's September 2026 report release says shopping research involving AI also involved search 89% of the time. These are estimated metrics, and the release does not provide enough detail to transplant that figure into a particular merchant's funnel. Similarweb's report announcement.
My reading is fairly boring, which is useful here: expect customers to carry specific questions between places. Make sure your answer survives the trip.
Neither statistic tells the finance team what percentage of next month's budget belongs to AI. Neither measures autonomous checkout adoption. Treat discovery, recommendation and purchase as separate things until your own evidence connects them.
Give the claim a name and an owner
The backpack question belongs with whoever can verify the product, before it belongs with whoever writes the ad.
Start with five buying claims support hears repeatedly. Fit, compatibility, cleaning restrictions or what changes between variants are good candidates. Use the customer's actual wording, with personal information removed.
For each claim, record the exact SKU and revision, supporting evidence, limitation, accountable owner and last verification date. Then list every public surface repeating it.
That final field matters. A correct product page cannot fix an old voiceover by wishing hard enough.
For our hypothetical backpack, the record might say the sleeve measures 36 × 25 × 2 cm and a particular bare laptop passed a physical fit check. The protective case was not tested. Those measurements and results are invented for this illustration; a merchant must supply actual measurements before using the claim.
The approved answer would be narrower than "fits all 16-inch laptops." It would identify the tested device and explain the case limitation.
That is a better answer for a buyer. It is also much easier for a reviewer to approve without becoming an amateur product engineer during a campaign deadline.
Separate missing evidence from conflicting evidence
These problems look similar in a dashboard and need different work.
If nobody has measured the sleeve, you have missing evidence. Assign the measurement. Until then, say what remains untested.
If the sleeve was measured but the page and feed disagree, you have a publishing problem. Locate the maintained product record, resolve which value is current and update the surfaces that use it.
If the facts are correct but nobody can find them, you have a communication problem. Put the answer where the buying question arises, in language a customer can understand.
A bigger content budget cannot settle which measurement is true. An AI recommendation cannot do the physical fit check for you. Get the product owner into the room instead of handing marketing another vague instruction to improve visibility.
Test whether a stranger can answer the question
Here is a small diagnostic I would run before adding another tool.
Choose ten real buying questions across five products. Give the public pages to a colleague unfamiliar with the catalog. Ask for an answer, a source URL and the exact fact supporting each answer.
Score a pass only when the answer is correct and supported. Keep missing facts and contradictions in separate columns.
Six supported answers out of ten means a 60% answerability score for this specific test. It is an illustrative calculation, not an industry benchmark or a conversion forecast.
Fix the product record, then repeat with a fresh reader. Preserve the original questions so the comparison remains useful. If questions change, explain that too.
You can separately ask a shopping assistant the same questions and record the system, date, response and cited sources. Keep that observation separate from the human check. Assistant answers vary, and a different recommendation after an edit does not prove the edit caused it. Accurate information guarantees no recommendation.
The change rule is where ownership becomes real
Suppose the supplier changes the backpack sleeve. Now the claim owner needs a list of what to review, not a general reminder to keep content fresh.
Check the product page and feed first. Find any image text, video scene or spoken line containing the previous promise. Decide which assets need an edit, regeneration or removal. The media owner then verifies the replacement in the live placement.
At QuickAds, our Catalog Ads workflow supports feed-to-creative production and review of changed products before selected regeneration and export. Launch stays in the ad account. The merchant owns the truth of the underlying product claim; the workflow does not inspect the physical bag.
That division of responsibility is worth writing down before the first batch goes out.
Then measure discovery and sales on their own terms. An answerability improvement shows your tested questions became easier to answer accurately. Referral visits, qualified purchases and returns need their own records and observation windows.
Start with the five claims your support team is tired of explaining. Get an owner and evidence behind each one. When the next customer asks a very specific question, your brand should have a better response than a confident sentence copied from the last campaign.
All backpack details are hypothetical. Platform statistics are attributed to their publishers and are not presented as causal results.