We all know AI search is changing how customers discover Shopify stores and merchants wonder if they need to have a totally new strategy apart from traditional SEO or not. I personally think AI search should be an additional part to traditional SEO, not a replacement or a completely different path.
What I think work best (at least for me) is
Intent-driven content
Use extractable structure - headings, bullets, short FAQs
But to be honest, I haven’t seen much of the results from AEO, traditional SEO still dominates. I don’t know if it’s because the above moves are helping mostly SEO not AI search optimization or not
Curious what you’re doing right now:
Are you optimizing mainly for Google, AI answers, or both?
Have you seen any traffic/mentions from AI tools yet?
@Lyn-Bui ,
I am optimizing for both. Traditional SEO is still my biggest source of traffic but I have started making content easier for AI to understand by using clear headings, FAQs, schema, and well structured answers.
I havent seen a huge amount of AI driven traffic yet but I do think the sites that invest in both SEO and AI friendly content now will have an advantage as AI search continues to grow.
I completely agree with you. You do not need a totally different strategy. Optimizing for AI is really just doing traditional SEO perfectly. Most of us are optimizing for both at the same time because the foundation is exactly the same. As for traffic, almost everyone is seeing very few direct clicks from AI tools right now. This is because AI gives the customer the answer directly on the screen, so they do not need to click a link. Instead of looking for traffic, the real goal of AI search is just getting your brand name mentioned as a trusted recommendation.
Your checklist is exactly what you need to be doing. Things like bullet points, clear headings, and strong schema data are exactly what both Google and AI bots want to read. Since keeping up with all that technical clarity can take a lot of time, I highly recommend using SearchPie: SEO, Speed & Schema. It automatically builds advanced structured data (JSON-LD schema), cleans up your meta tags, and keeps your site speed fast. This gives your store the perfect technical foundation to be recommended by both Google and new AI search tools without the extra headache.
Hi @Lyn-Bui
I think the best approach is to optimize for both. Strong traditional SEO is still the foundation, and many of the same practices also help AI search.
I’ve been focusing on:
Clear, intent-based content
Well-structured pages with descriptive headings and FAQs
Schema markup and strong internal linking
Fast page speed and a good user experience
While AI referrals are growing, they’re still a small portion of traffic compared to Google for most Shopify stores. My focus is on creating content that’s easy for both search engines and AI systems to understand, rather than maintaining separate SEO and AEO strategies. This approach seems to be the most future-proof.
Honestly the flat results are probably a measurement thing more than a strategy thing. PieLab’s right that AI usually answers on the page without a click, so it barely shows up in your traffic reports even when it’s working. What I do now is just ask ChatGPT, Perplexity and Google’s AI the actual buying questions someone in my niche would type, and check whether my store gets named. That’s the real scoreboard, not sessions.
On your other question, the moves that help AI most on a product page are the same ones that read cleanest for a person: make the first line of the description say plainly what the thing is, who it’s for, and the one reason to pick it, and keep the specs as real text rather than baked into an image. AI quotes sentences, so anything sitting inside a spec graphic or a fuzzy intro just never gets pulled. That overlap is why it feels like one job instead of two.
I’d treat it as both, with traditional SEO still doing most of the work right now.
For Shopify stores, the practical approach in 2026 is usually:
keep building for Google/discovery in the normal way
make the content easier for AI systems to extract, summarize, and attribute
So I don’t see AEO as a separate channel with a completely different playbook yet. Most of the things you listed help both: clear intent, strong heading structure, concise FAQs, consistent entity signals, clean product data, and schema.
Where I think it becomes more specific for AI answers is:
making product and collection pages explicit about what the item is, who it’s for, and how it differs
using short, direct copy that answers comparison-style and problem-style queries
keeping merchant, brand, policy, shipping, and return info easy to parse
reducing ambiguity across variants, bundles, and similar products
The hard part is measurement. A lot of “AI search” impact seems to show up indirectly through branded search lift, assisted conversions, and occasional referral traffic rather than a clean AEO report. So if you’re not seeing a big standalone traffic bucket, that doesn’t necessarily mean the work is doing nothing.
If I were prioritizing, I’d do this:
Keep core SEO as the foundation.
Tighten structured product data and on-page clarity.
Add FAQ/comparison content only where customers actually ask those questions.
Watch Search Console, referral sources, and branded query trends rather than expecting obvious AI attribution.
So yes, I’d optimize for both, but I would still allocate most effort to traditional SEO until AI referrals become more measurable and material.
I think it’s becoming less about choosing between SEO and AI search and more about building content that works for both. Most of the things AI systems seem to favor—clear structure, strong topical authority, helpful content, and well-defined entities—are also things that have been good SEO practices for years.
Personally, I still focus primarily on traditional SEO because that’s where the majority of measurable traffic comes from. However, I’ve started paying more attention to FAQs, structured data, and creating content that directly answers specific questions. Even if it doesn’t immediately increase AI mentions, it improves the overall quality and discoverability of the content.
So far, I’ve seen far more results from Google than AI tools, but I do think laying the groundwork now will be valuable as AI-driven discovery continues to grow.
I scan a lot of small Shopify stores for exactly this (I build in this space, hence the username, so grain of salt) and the pattern I see is that strategy is rarely the problem. The catalog is. Most stores I check have a decent homepage and blog, and then two thirds of the product descriptions are under 80 words. When an assistant decides which specific product to name, it’s working from the product page, and a two line description gives it nothing to work with. So before adding anything new I’d fix the bottom half of the catalog first: descriptions with the material, the sizing, who it’s for, meta descriptions that aren’t auto generated, alt text on the images. Boring work, but it’s the difference between being parseable and not.
And I’d underline what dropfeed said about measurement. Asking the assistants your own buying questions is the real scoreboard right now, traffic reports miss most of it.
Hi @Lyn-Bui, I’m definitely in the optimize for both camp. Traditional SEO is still bringing in the most traffic for my store by far. I’ve seen almost no direct clicks from AI tools yet, but like others said, I think the goal for now is just getting our brand mentioned when AI answers a question.
What I’m doing specifically to make my content more AI-friendly, beyond the standard SEO stuff:
I go through my product data in Shopify like a hawk. Every field, vendor, type, tags, variant names, has to be super clean and consistent. AI looks at all of this, not just descriptions.
For key products, I add a short, direct sentence or bullet point explaining why someone should pick my version over a competitor’s. AI likes to compare.
Making sure my shipping, returns, and warranty policies are super clear and easy to find on their own pages. AI systems are great at summarizing these for users.
It’s hard to track but I’m trying to get ahead. On the side, I’ve been working on a tool, ClickFrom.ai, that helps stores structure their product data for AI and track when they get recommended by AI answers. It’s an experiment but it feels like the direction things are going.
I’m treating AI search as an extension of SEO rather than a separate strategy. Since improving content structure, using clear entities, and answering user intent better, my organic rankings have improved, but AI-driven traffic is still a small fraction compared to Google. It feels like solid SEO is still the foundation, with AI optimization building on top of it.
I would add one thing on measurement. I don’t treat a single manual check as evidence of progress.
For a useful baseline, I use a fixed set of buyer-intent questions, run each across several AI systems more than once, and track mention rate, first-recommendation rate, cited sources, and factual conflicts. This makes it easier to separate a recurring pattern from one lucky answer.
SEO is still the foundation. The AI-specific work starts when you compare the store’s official facts with the sources the models actually use, then fix the gaps.