Optimising Your Shopify Store for AI Discovery

There is a lot of noise around AI & Shopify and how brands should be preparing for how search behaviour is and will continue to change. With features like ChatGPT Shopping rolling out and Shopify quietly building the infrastructure to connect directly with AI platforms, we’re entering a new era of product discovery.

SEO is expanding into broader areas we’ve never seen before and as a result SEO is no longer just about keywords. Customers are starting to ask more conversational based searches such as:

“What’s the best waterproof running shoe under £100? For someone with wide feet”

Instead of scrolling through Google results or ads, shoppers increasingly expect a curated, conversational response. If your store isn’t ready for this shift, your products risk being invisible. But for Shopify brands who prepare, this is a huge opportunity to stay ahead of the curve.

The Shift to Conversational Commerce

For years, conversational search was more theory than practice. However it’s becoming the new default for discovery. AI-powered assistants like ChatGPT and Gemini can interpret intent, context, and nuance in ways that traditional search engines can’t.

This shift doesn’t just affect how people shop it changes the foundations of visibility. If your product data, collections, and content aren’t structured for AI comprehension, your store will lose discoverability.

How to Prepare Your Shopify Store for AI Discovery

Although there are still a lot of changes happening within AI and how users are searching on eCommerce the below outline some of my personal areas of focus when talking to Shopify brands and how they can drive incremental growth through these channels.

• Improving product data 

Today’s customers — and AI systems surfacing your products — want more than just the what. They want to understand the why. Why this material over another? Why does this product suit their specific need, lifestyle, or budget?

To optimise for both AI discovery and real customer intent, brands need to think beyond features and consider the full buying journey. For example:

• Materials & Quality – Does the product use organic cotton, recycled fabrics, or waterproof technology? Why does that matter to the customer?

• Use Cases – Is this jacket for a daily commute, hiking trips, or nights out?

• Buying Behaviour – Are people comparing this product on price, durability, ethics, or convenience?

By layering this type of context into your product content, you don’t just help customers make decisions you give AI the detailed signals it needs to recommend your product in the right buying scenario.

•  Structure Data with Schema

SCHEMA markup has always been important, but I see it having a further value although a lot of Shopify stores do have product SCHEMA implemented by default this implementation can be expanded much further with additional attributes that are consistently been amended within the SCHEMA vocabulary.

By expanding your schema with attributes such as category, material, colour, size, audience, and additional properties, you provide richer signals about your products. 

For example, knowing that a jacket is waterproof, made from recycled polyester, designed for women, and positioned under “outdoor hiking gear” makes it far more likely to be surfaced in relevant conversational queries.

These elements can be implemented if not already available using the additional property values.

• Build Collections Around Real Language

Collections are a huge opportunity in Shopify SEO while most brands do have the basic of collections developed they usually stop there without thinking more granular about search intent and the opportunity around how real customers shop.

Take moisturisers as an example. Too often I see vague collections like “Moisturisers.” These don’t reflect how customers actually search or think about their buying decisions.

A better approach is to build intent-led collections that align with different needs, preferences, and benefits by developing deeper collections such as “Moisturisers for Dry Skin,” “Moisturisers for Oily Skin,” “Moisturisers for Sensitive Skin.” Or think of breaking these down by benefit “Anti-Ageing Moisturisers,” “Hydrating Moisturisers,” “Brightening Moisturisers.”

By organising collections in this way, you’re not only helping customers navigate buying decisions more easily, but you’re also giving AI the right context to match queries like:

• “What’s the best cruelty-free moisturiser for dry skin under £30?”

The more logical and benefit-driven your collections, the more discoverable your products become in conversational search and AI-driven recommendations.

• Optimise for Conversational Intent

AI queries are not only becoming more conversational but also much longer in overall length meaning brands need to optimise for longer tail search instead of just optimising for “moisturiser under £30”, think about “What’s the best cruelty-free moisturiser under £30?”

You can prepare for this by:

• Adding FAQs to product and collection pages.

• Writing content that addresses decision-making questions (e.g. ingredients, use cases, comparisons).

• Using natural, question-based phrasing alongside traditional SEO content.

• Prioritise Site Speed & Mobile UX

Traditional does still matter we know there are already a lot of benefits when it comes to improving site speed for marketing as an overall channel. Brands need to focus on ensuring legacy apps are removed, code is clear and images and optimised for usability and key compliance metrics in the US.

• Remove legacy apps and scripts that add unnecessary bloat.

• Audit your theme code to strip out duplication or unused elements.

• Optimise images and media for fast load times without sacrificing quality.

• Prioritise Core Web Vitals (LCP, CLS, FID), as these remain key usability signals in both SEO and AI-driven systems.

• Increase Reviews 

Reviews are trust signals, which we know help drive conversions are now also taken into account from LLM’s. Not only is it becoming more important to increase reviews but it’s also becoming increasingly more important to ensure those reviews are crawlable, structured, and visible & not hidden in iframes or not accessible unless crawlers are accessing that content with JS.

It's important to help encourage feedback after purchase to increase ongoing trust-building. Structured, fresh reviews help your brand appear more credible in AI-driven recommendations.

• Keep Product Feeds Clean

Merchant Centre feeds/Product feeds are likely to become the backbone of shopping integrations. Inconsistent or incomplete feeds mean poor recommendations and could result in lower CTR from search and overall less visibility across large language models.

Audit your Shopify feeds (and Google Merchant Centre if you use it) to ensure they’re rich in detail, error-free, and aligned with schema best practices.

• Traditional SEO Still Matters 

While AI-driven commerce is changing the landscape, the core pillars of SEO still apply and as much as the noise and SEO Twitter space otherwise traditional SEO does still matter especially if your brand hasn’t managed to get the basics implemented. 

• Build authority through links & digital PR

• Continue to fix foundational SEO issues

• Develop in depth content that creates topical authority 

• Focus on trust, clarity, and user experience.

Final Thoughts

AI shopping isn’t something to wait and see on. It’s already here, and it’s going to define how customers discover new products.

The brands that win won’t be those who chase the latest gimmick — they’ll be the ones who focus on structured data, customer-first content, and SEO fundamentals, all optimised for conversational discovery.

If you’re not sure whether your Shopify store is AI-ready, now’s the time to review your product data, collection structure, and overall content approach. Because in an AI-first world, visibility is everything.