The future of Shopify SEO: Our strategy for AI search

It might not look like it, but AI discovery is still in an experimental phase. In the tiny bubble of technical marketing we live in, it seems like we’ve been thinking and talking about this forever. With all of this going on, it’s hard not to let the mountain of "thought leadership" distract from the fact that things simply aren’t as developed as they seem.

That’s not to say we’re not excited about the future; we genuinely believe this has the potential to be the biggest change in the world of product discovery since the arrival of Facebook ads. But whenever a shift of this magnitude happens, it brings out all the usual "hustle culture" nonsense. It’s all pretty jarring, and in moments like this, we find it best to keep a specific statement from one of our brilliant partners, Timo Dechau, front of mind:

"Nothing is as simple and powerful as it gets described by vendors, agencies and consultants."

The reality is that we are all operating in a feedback loop. Because we are dealing with systems at Shopify and within LLMs that we can't fully see yet, our approach has to be rooted in adaptability and constant testing. While the "how" of discovery is changing, the core objective remains the same: deeply understanding the user. As AI-driven personalisation kicks in, the human context becomes the only signal that really matters.

Now that we’ve got all of that out of the way, the purpose of this article is to explain how we approach AI discovery. There is plenty of big-picture thinking around AI that is interesting and relevant, but we need to stay focused on our specific thing: large catalogue Shopify brands. To us, anything else is just a distraction.

Is AI search different from SEO?

If you've been unlucky enough like me to have spent any significant time on LinkedIn lately, you’ve probably seen the exhausting debate over this. The arguments usually fall into two camps: either it’s exactly the same and everyone is getting excited about nothing, or it’s completely different and anyone saying otherwise is an idiot.

The reality, however, is that this isn't a binary choice - which, thankfully, means there’s a space for over-excited idiots like us.

Joking aside, there is some real nuance here. Many of the core principles of search remain the same, but the way you execute - and more importantly, where you optimise - is shifting fundamentally.

Traditional SEO is often a top-down exercise: looking at pages and sticking keywords on them. However, AI discovery requires a bottom-up approach. Instead of fixing the page at the end of the line, we are optimising the product data at the source.

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This is a bit of an oversimplification - organising your site better, getting more granular, creating collections that match how people search, and making sure your products are optimised - has been table stakes for a long time. But to do that effectively, you need to make sure your product data is in a good position to allow it. This is what we’ve done for years.

Now, the difference with AI is that it needs that product data even more. It is reading it directly. It’s looking for slightly different things and it needs richer information, but this is a refinement of our existing work, not a total departure.

The era of technical merchandising

We call this approach technical merchandising. It is the vital philosophy that underpins everything we do: Shopify acts as the point of truth across all of your marketing channels.

Technical merchandising is the bridge between raw product data and how that data is surfaced to both humans and machines. It is effectively treating Shopify as a data warehouse for your products. When we optimise that warehouse, the benefits flow across every channel - whether that’s Google Ads, Meta, AI search, or on-site UX.

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Within this realm, we focus on four distinct pillars:

∙ Product data: the "technical DNA"

This is the engine room. At a basic level, this is getting your core attributes right: size, colour, material, shape. But the real value is in how deep you can go beyond the basics. This is where we define the technical DNA of your brand. AI uses this data to build its own case for your product. If a user asks for "the best value dog food," the AI isn't looking for a slogan; it’s looking at your ingredients list and comparing price-per-gram of protein against the competition to build a justification for its recommendation.

∙ Taxonomy: the map of your store

This is a simple but vital concept: how is your store organised? It’s the map that shows where your products fit into parent and child categories. Think of it as a hierarchy that flows from broad to granular, ensuring that both humans and AI crawlers understand the context of every single item in your catalogue.

∙ Granularity: intent and personas

Granularity is how deep that map goes. When your product data is rich, you can build out a taxonomy that matches exactly how people search. This allows us to move beyond broad categories into highly specific sub-categories and personas. Instead of just "Treadmills," we can create specific "shelves" for "Treadmills for Marathon Training" or "Folding Treadmills for Small Apartments," providing the machine with the exact context it needs to match a user's intent.

∙ Depth: the evidence base

Depth isn't just about the words on your product pages; it’s about how that information is applied across the whole site. AI doesn't just read one description; it scans multiple sources to build a consensus: FAQ content, collection descriptions, customer reviews, and supporting editorial content. By providing that specific evidence across multiple touchpoints, you are giving the AI the data it needs to "reason" that your brand is the right recommendation.

Putting this into practise

Shopify is betting heavily on being the data layer that fuels the entire AI commerce ecosystem. With the introduction of the Universal Commerce Protocol (UCP) — an open-standard commerce layer co-developed by Shopify and Google - the platform is effectively translating your store into a format that AI agents can discover, compare, and even use to complete checkouts natively.

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Our guiding principle is to align with Shopify’s native functionality and structure wherever possible.

We have learned that the more you bend or break this native architecture with custom hacks, the more fragile your data becomes. Shopify is increasingly designing its metaobjects, attribute handling, and Knowledge Base to push data directly into this AI layer by default.

While we don't have a perfect window into every "black box" of the platform yet, we believe that staying close to the native "point of truth" is the best way to ensure your store remains machine-readable as the technology evolves.

Our role is to work within this native framework to improve your inference advantage - making it as easy as possible for an AI model to "reason" its way to your products.

Theory into execution

This is where the theory begins to meet the practicalities of the platform. Because the landscape is shifting, we don't treat this as a "one and done" setup, but as an ongoing process of data enrichment and infrastructure refinement. To be successful in the era of AI-commerce, we focus on a few key areas:

∙ Building the "technical DNA"

Our goal is to make your product data as comprehensive as possible. We audit your existing attributes and look for opportunities to go deeper, adding technical metadata points where they add genuine value. This isn't just "filling in boxes" for the sake of it; it’s about building a rich taxonomy that helps AI, organic search, and Google Ads understand exactly what you sell. By aligning with Shopify's native standards, we make sure your data is in the best position to be interpreted by whatever new discovery tools emerge.

∙ Engineering the discoverable storefront

This often involves making that data more accessible to both users and machines. We work on features that allow your taxonomy to surface naturally: secondary navigations and breadcrumbs that help AI understand relationships, and modular theme components that handle complex data without cluttering the user experience. We also look at knowledge harvesting - taking the expertise from your team and structuring it as FAQ pairs within the Shopify Knowledge Base to help fuel the next generation of storefront agents.

∙ Scaling for the answering engine

We take that depth of information - like the FAQs - and look for ways to display it across products and collections. This allows the information to be found by traditional organic search while providing the "evidence" AI needs to reference you in its answers. Rather than manual, page-by-page updates, we focus on building systems that allow this data to flow across your catalogue as efficiently as the current Shopify infrastructure allows.

Off-site: the validation layer

We should probably treat this as a whole separate subject that we’ll be covering shortly, but the core principle is that AI models look off-site to verify what we say. This doesn't apply to every brand, but for those where it’s a fit, it’s about building entity and brand trust.

Because AI tools often look at authority across third-party sources to confirm a brand's claims, we focus on creating consistent "echoes" of your data across the web. This is an area where we are constantly testing, as the move toward AI-driven personalisation makes these external signals increasingly complex to measure. Our current focus includes:

∙ Consistency across touchpoints If your brand DNA - from product attributes to core service descriptions - is inconsistent across the web, AI engines may struggle to match your business to its claims with high confidence.

∙ Authority through corroboration LLMs often treat external mentions in credible formats as confidence indicators. We look for ways to ensure your expertise is referenced in the places where AI "learns," like industry roundups and authoritative niche publications.

∙ Community sentiment Models look to platforms like Reddit to understand unfiltered human experiences. We monitor these community signals to see how they align with your on-site claims, using them as a feedback loop to refine how we present your brand to both humans and machines.

Reporting is the Wild West

If you're looking for a clean report that shows exactly how many sales ChatGPT sent your way last Tuesday, I have bad news: reporting is the Wild West right now.

Traditional tracking is fundamentally broken for AI. GA4 is often blind to this traffic because it relies on client-side JavaScript - if an AI bot scrapes your site to answer a question, that script never fires. Even when users click through, the referral data is often stripped, meaning it just shows up as "Direct".

Our approach to measurement:

∙ Signal monitoring: Tracking directionally via manual prompts to LLMs and monitoring technical markers like schema and data depth.

∙ Correlative tracking: Watching for patterns where improved AI visibility correlates with sudden spikes in branded search and direct traffic.

∙ Wait for the platforms: With Shopify’s UCP updates and Google’s likely response, we expect native tracking and attribution to eventually come from the platforms themselves. We aren't going to commit you to subpar, expensive "AI tracking" tools that are ultimately just guessing.

Final thoughts

The world of AI search is moving incredibly fast. However, the foundational work of technical merchandising - organising your store and enriching your data - is what sets you in good stead. If your data is modelled correctly at the root, you are in a position to adapt as fast as the technology does.