Hey, Scott Austin here.

I want to start today with a number from two of my clients. Right now, about 7 percent of their sales are coming from AI. Not from Google. Not from paid ads. From people who asked ChatGPT or Claude or Gemini a question, got pointed at these brands, and bought something.

What that 7 percent does not include is Shop. Shopify counts the Shop App as an agentic channel in its reporting. I have stripped that out. This 7% is only the outside AI platforms.

That is the high-water mark across the stores I work on. Most are lower. But two stores at 7 percent of revenue is not a rounding error, and it did not exist as a channel two years ago.

So today we are going to talk about Answer Engine Optimization, or AEO. What it is, whether it is worth your time right now, and what the work actually looks like on a Shopify store.

I am going to show you three real client stores. All three got the same on-site work from me. All three ended up in different places in AI results. 

The Future of AI commerce

That is today. The stronger argument for paying attention to AEO is what the next few years may look like.

Bain, the consulting firm, put out a forecast that AI will play a role in most online shopping journeys by 2030.  Most. Not a niche, not an edge case. The default path to buying something online is going to run through AI.

And that is already underway. Bain estimates that 30 to 45 percent of US consumers are using AI tools right now to research products and compare options. Salesforce measured AI influencing 3 billion dollars of US Black Friday sales in 2025.

Notice what all of that is describing. Not checkout, but Discovery. Figuring out what to buy. Research and education are common use cases for AI.

So the question is not whether your customers will use AI to decide what to buy. A lot of them already are. The question is whether AI knows your brand when they ask.

Is AI Overhyped?

You will read headlines about AI referral traffic growing 138 percent year over year, or 393 percent in a quarter, or 8x on Shopify's own platform data. Those numbers are real. Adobe and Shopify both published them.

But they are growth rates, and growth rates off a small base are still low.

Here is the absolute number. AI referral traffic is still well under 1 percent of total retail traffic. So when somebody tells you AI has already transformed ecommerce, that is not what the data says. Not yet.

But here is where the data is interesting. The traffic that does arrive is unusually good. AI-referred visitors have been measured converting at 54 percent higher rates, and generating 53 percent more revenue per visit than non-AI visitors. Shopify's own Q1 2026 platform data showed AI sessions converting around 50 percent better than organic search.

Small channel. Better conversion.

AI is a small channel today. It is the fastest-growing one. It converts better than anything else you have. And independent forecasts say it is heading toward a meaningful share of ecommerce this decade.

That is not a reason to bet your business on it. It is a very good reason to plant a flag now.

How AEO is different from SEO

I want to draw the line between AEO and SEO, because they are related but they are not the same.

The old SEO model was links. A backlink was a vote. Somebody linking to you passed authority, and you went out and built links.

AEO has the same DNA: third-party validation, but the mechanics changed in a few specific ways.

1. The link is optional now. An AI reading a page does not need a link. If a trusted care guide says your product is good, that association gets absorbed whether or not anyone linked to you. A mention with no link was close to worthless in old SEO. In AEO, it is the main unit of value.
2. What gets said matters, not just that you were mentioned. A backlink carried no meaning. A citation does. If a review says a product is dust-free and safe for respiratory health, that is a specific claim the model can repeat back to a shopper. The words in the mention are part of the value.
3. Frequency beats any single hit. In SEO, you could sometimes rank on a handful of powerful links. In AEO, it is about how often, across how many trusted sources, your brand comes up alongside the topic. It is closer to reputation building than to link acquisition.
4. And the sources are not the ones you would guess. Studies of AI citations consistently find community sources at or near the top. Reddit, YouTube, and LinkedIn. Not Instagram, not TikTok. Those two get cited far less.

Reddit in particular has been the giant. Through the first part of this year, studies were finding Reddit accounting for a huge share of AI citations, in some measurements close to half of what Perplexity was pulling from.

And I want to spend a minute on Reddit, because two separate things are happening to it.

Here is why Reddit mattered in the first place. It was authentic. Real people giving real opinions with no incentive to sell you anything. That is exactly what an AI wants when someone asks which product is good, and it is why a Reddit recommendation carries weight that a brand's own website never will.

Then everybody figured that out.

The Verge reported on skincare brands, record labels, and weight loss apps seeding Reddit threads. An entire cottage industry of Reddit agencies appeared. One of them charges brands 2,500 dollars a month to produce somewhere between 35 and 100 brand mentions. Researchers at Cornell Tech found that as few as 13 words in a Reddit comment can steer an AI answer toward a particular product.

Think about what that does. The thing that made Reddit worth citing was that nobody was gaming it. That is getting less true every month.

Reddit is fighting it. In July, they announced they are now catching around 25,000 spam posts and comments a day, using their own AI to spot the coordinated patterns their older filters missed. Moderators are pushing back too. One community wrote that companies were using them for AEO, and restricted new posts to a single weekly thread.

Additionally, Reddit's citation share is wildly unstable. In the middle of August, its share of citations in ChatGPT search fell off a cliff, from around 3.8 percent down to about half a percent in the space of a few days. Forbes, Axios, and Search Engine Land all covered it.

Nobody knows why it happened. OpenAI has never explained it. The technical change everyone blamed occurred six days earlier and does not account for the sharp drop. The firm that measured it says it cannot rule out a problem with its own data collection.

And this has happened twice before. In September of 2025, Reddit's ChatGPT share fell from around 7 percent to about 1 percent, then recovered to around 3 percent within two months. In June of this year, it fell from about 7 percent to under 1 percent, then climbed back to around 5 percent. Both times it came back.

There is also evidence that ChatGPT never stopped reading Reddit. It kept pulling Reddit pages in heavily while citing almost none of them. One consultant found a single conversation where 84 of the 221 pages ChatGPT retrieved were Reddit threads, and not one of them showed up as a citation.

So I am not going to tell you Reddit is dead, because that is not what the data says. What the data says is that your visibility on Reddit can drop by 80 or 90 percent in a week, for reasons nobody outside OpenAI can explain, and it has now done that three times in eighteen months.

That is the lesson, and it applies well beyond Reddit. You do not control the platforms, you do not control the engines, and you will not get a warning. So if you were paying an agency 2,500 dollars a month to plant Reddit mentions, understand what you were buying. Those mentions are still sitting there. Whether they are worth anything this month is somebody else's decision.

One last piece of context. Google signed a deal with Reddit in early 2024, reported at 60 million dollars a year, for access to Reddit content. OpenAI signed a similar one, reported at 70 million. That is a big part of why Reddit got so dominant in the first place. And Reddit is reportedly weighing whether to renew with Google, because AI summaries have been cutting the traffic that used to flow back to them.

So the source mix you optimize for today is not the source mix you will be optimizing for in eighteen months. The best practices are going to change rapidly, just like everything with AI.

The AEO strategy for Shopify stores

Here is the framework I use, and it has two parts. 1 is on-site. 2 is off-site.

1. On-site controls how you are represented. Structured data, product content, FAQ content, buying guides. This is making sure that when an AI looks at you, it gets accurate, complete, machine-readable facts.

2. Off-site controls whether you are recommended. This is citations and third parties saying you are good.

Which matters more depends on the question being asked.

For factual questions, like what size does this come in, or is it waterproof, or what does it cost, structured data does the work. The AI just needs your facts.

For recommendation questions, like what is the best hiking boot for wide feet, or which espresso machine should a beginner buy, citations do the work. All the on-site structured data in the world will not make an AI call you the best. Only third parties saying so do that. 

Now, sequencing. Do on-site first.  It is controllable. You own it, you can do it in weeks; there are no gatekeepers, and very little budget is required.

Then, once on-site is clean, shift the bulk of your ongoing effort to off-site, because that is the slower, higher-ceiling work.

I covered the onsite work in Episode 182, a practical guide to Shopify's agentic storefronts. If the on-site side is new to you, go back and listen to that one first, then come back to this. Getting your product categories and your category metafields in place is the prerequisite for everything else in this episode.

In addition to structured data on product pages, other types of content in your store can be useful for AI, including: 

1. Buying guides. The how-to-choose X article. How to pick the right size. What to look for in your first one.
2. Comparison content. X versus Y. Your material versus the cheaper material everyone compares you to.
3. FAQ content, in real question-and-answer format, because so many discovery queries are literally questions.
4. Use case and problem-solution content, aimed at the problem the shopper has before they know the product category.

On the FAQ piece specifically, Episode 183 goes deeper. That one is about the Shopify Knowledge Base app, and the part worth your time is the query log, which shows you the questions AI is being asked about your store and, more usefully, the ones it could not answer. Those unanswered questions are your FAQ content, handed to you.

Three stores, same on-site work, different results

So let's look at how this AEO work performs in the real world.

I did the same on-site work across three client stores. Same approach to structured data, same product data health standards. I used my app, Datify, on all three, which is what makes the comparison clean. Datify is a product data management app; it helps you with the structured data that AI needs, and running it across all three stores means the on-site variable is controlled in a way it usually is not when you compare brands.

I am going to show you the structured data for all three on screen in a minute, and you will see it is good on all three.  And yet the AI results are different. That difference is entirely off-site, and that is the lesson.

Store one: Mulberry Park Silks

Query: I'm looking for high-quality silk sheets.

Mulberry Park shows up strongly. When you look at why, their own content is doing its part. Their pages are specific in a way AI rewards. Not premium silk, but Grade 6A mulberry silk, at 19, 22, and 30 momme, OEKO-TEX certified, with content walking through how to choose the right momme for how you sleep. That is spec-precise, certification-backed, and consistently worded across the catalog.

But the thing that puts them in AI answers is the citation layer. Forbes Vetted named their 22 momme set the best silk sheets on Amazon. Women's Health named the same set best overall. US News rated them best overall in a silk sheet ranking. Healthline named them best four-piece set.

Multiple trusted publications naming the same brand, often the same specific product. That co-citation is a very strong signal.

Store two: ReptiChip

Query: is coconut substrate good for my ball python.

ReptiChip also shows up well, and the pattern is similar but the source type is completely different. Their authority is not glossy magazines. It is the reptile keeping community. ReptiFiles, a major ball python care resource, names them. Small Pet Expert ranks them and quotes an owner. PetEducate calls them a favorite and highlights that they are dust free, which matters for respiratory health in snakes.

Their own pages hold up their end too. They have a page specifically about ball python substrate that answers the exact question, talking about moisture retention and microbial resistance.

The lesson from ReptiChip: authority is category specific. For a luxury bedding brand it is Forbes and US News. For a reptile substrate it is the care guides and the hobbyist community. You do not need national press. You need to be embedded in whatever the trusted source set is for your category.

Store three: Healing Home Foods

Query: what are good raw food crackers for snacking.

This is the one with work to do, and it is the most useful example in the episode.

Their on-site side is fine. Same structured data work, and their content is genuinely good. Raw, air dried at low temperatures, sprouted almonds, kale, goji, gluten free, with real ingredient specificity. They have distribution too, Amazon plus stockists.

What is missing is the citation layer. When I go looking, I find their own site, retail listings, and Amazon. What I do not find is the independent best raw crackers roundups, the healthy eating blogs, the dietitian recommendations.

And that is decisive, because the query is a recommendation query. Retail listings tell an AI that the product exists and what is in it. They do not tell it the product is good, which is exactly what the shopper asked.

 

The Citation Work

So all three stores are in the game, because all three have the on-site foundation. Where they place is being decided by forces that live outside the store.

That is the honest boundary of what any app, including mine, can do for you. The on-site half is the half you control, and it is the half worth doing first because it is doable. The other half is earned.

For a small brand with no PR team and no PR budget, like most Shopify stores, the top tier press is realistically out of reach. Those slots mostly go to brands with representation and existing relationships. So here is the tier that is actually reachable.

• Micro influencers and niche creators in your category. Many will post for free product. Their content gets indexed and uses the exact language you want associated with your brand.
• Registered dietitians, or whatever the equivalent expert is in your category, approached directly rather than through a magazine. You get the authority signal without the gatekeeper.
• Independent bloggers who publish their own roundups. Far more accessible than editors, and each one is a citation in exactly the right context.
• Reddit and community forums, done honestly. Show up as yourself, not as a fake customer.
• Small podcasts. Founders telling their story, which generates a mention, show notes, and often a transcript.
• Accurate listings on product databases and the newer AI ingredient scoring sites, where a clean label product can score well purely on merit.

This is a months-long grind, and it only works if the product is good enough that people want to talk about it.

How to tell if it is working

Do not guess at this. Measure it, and it costs nothing but time.

Pick five queries a real customer would ask that would lead them to your products. Mix the phrasings. For a reptile substrate brand, that might be:

• Is coconut substrate good for my ball python
• What is the best substrate for a ball python
• Best coconut substrate for reptiles
• What should I put in the bottom of my ball python enclosure
• A brand comparison query like ReptiChip versus coconut fiber.

Run all five in ChatGPT, Claude, and Gemini. Same queries, every engine. Then record:

• which brands get named
• in what order
• whether you appear
• what source the engine cites for the mention.

Score the results. Three points if you are named first, two if you are in the top three, one if you are mentioned at all, zero if you are absent. Add it up per engine, and you have a baseline number you can retest in ninety days.

Summary

I learned a lot in preparing for the podcast episode.  Here's my summary of AEO for Shopify stores today.

AEO is real, the audience is real, and it is small today. It is a growing channel; it converts better than most traffic, and independent forecasts put it at a meaningful share of ecommerce by 2030. Being early here is cheap. Being late will not be.

It is content-heavy and technical work, like SEO. But the differences matter. Mentions rather than links. What gets said about you, not just who points at you. Reddit and YouTube rather than Instagram and TikTok. And a discovery layer that rewards guides and comparisons rather than product pages.

Start on-site, because it is the half you control and you can have it done in weeks. Clean structured data, question-shaped content, buying guides that answer the category question rather than describing your product. That is the foundation, and it is what puts you in the game.  And if the structured data side is where you want help, that is what Datify does.

Then start the slow work of getting other people to say your name and recommend your products. 

Run the five query test this week and write down your score. Then you will know where you actually stand instead of guessing.

Thanks for listening.