Why being recommended by AI agents could become ecommerce’s next page-one ranking
In the early days of SEO, the brands that won were not always the biggest brands. They were the brands that understood, before everyone else, that search engines were becoming the new gateway to customer intent.
They realised that ranking on page one of Google was not just a marketing advantage. It was distribution. It was visibility. It was traffic. It was trust. And for many businesses, it became one of the most important growth channels they would ever build.
We are now at the beginning of a similar shift.
This time, the new gateway is not a search results page. It is the AI agent.
Consumers are no longer only typing keywords into Google and clicking through a list of blue links. Increasingly, they are asking AI systems for help: “What should I buy?” “Which product is best for me?” “Find me a gift under £100.” “Compare these options.” “Which brand is most sustainable?” “What is available in my size?” “What should I pack for this trip?” “Which supplier should I choose?”
That behaviour changes the rules of ecommerce discovery.
In traditional SEO, the question was: can your website be found, crawled, understood and ranked by a search engine?
In agentic shopping, the question becomes: can your products be found, understood, trusted, compared and recommended by an AI agent?
That is why agentic shopping optimisation may become the new SEO.
From search engines to shopping agents
SEO became powerful because search engines sat between buyers and sellers at the exact moment of intent. If someone searched “best running shoes for flat feet” or “organic skincare gift set”, Google did not just provide information. It influenced demand.
The businesses that appeared first captured a disproportionate share of attention. The businesses that ignored SEO were often invisible, even if they had better products.
Agentic commerce introduces a similar but deeper layer of mediation.
AI shopping agents do not simply return a list of links. They can interpret a shopper’s preferences, compare products, summarise trade-offs, remember context, check availability, factor in price, and in some cases move closer to completing the transaction.
OpenAI has already introduced shopping research in ChatGPT, describing it as a way for users to explore, compare and discover products through personalised buyer’s guides.
Source: OpenAI — Introducing shopping research in ChatGPT
OpenAI has also introduced richer product discovery experiences in ChatGPT, including visual shopping, side-by-side comparisons and merchant integration.
Source: OpenAI — Powering Product Discovery in ChatGPT
Google is moving in the same direction. Its AI shopping experience includes conversational shopping and agentic checkout, and Google says AI Mode is powered by its Shopping Graph, which includes more than 50 billion product listings, with billions updated every hour.
Source: Google — Shopping launches agentic checkout and more AI shopping tools
For merchants, that means a major shift is underway.
It will no longer be enough to optimise only for search rankings, paid ads, social discovery or marketplace listings. Brands will also need to optimise for how AI systems understand their products and decide whether to recommend them.
Why this looks like the beginning of SEO
The early SEO opportunity was powerful because most businesses underestimated it.
Many saw search as technical, niche or secondary. A smaller group realised that search was becoming the infrastructure of online demand. They invested early in content, metadata, links, site structure and authority. Over time, those early advantages compounded.
Agentic shopping optimisation has the same early-stage characteristics.
The channel is still forming. The rules are not fixed. The terminology is not yet standardised. Most merchants are not yet building specifically for AI-agent visibility. Many are still thinking about AI as a customer service tool, a chatbot, or a productivity layer.
But the deeper opportunity is product discovery.
If AI agents become a common route into shopping decisions, then the brands that are easiest for those agents to understand, trust and recommend will have a meaningful advantage.
That advantage may look like early SEO did: invisible at first, then obvious in hindsight.
McKinsey has described agentic commerce as a shift in which AI agents could radically remake the shopping experience for consumers, merchants and platforms.
Source: McKinsey — The agentic commerce opportunity
Academic research is also beginning to examine how AI agents could change markets, search behaviour and ecommerce competition. For example, the paper Agentic Markets: Equilibrium Effects of Improving Consumer Search studies how AI-assisted search may affect consumer learning, welfare and pricing.
Source: arXiv — Agentic Markets: Equilibrium Effects of Improving Consumer Search
Another paper, What Is Your AI Agent Buying?, examines how AI agents evaluate products in ecommerce settings and how product positioning, reviews, ratings, sponsored tags and descriptions can influence AI-mediated shopping outcomes.
Source: arXiv — What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce
This matters because it suggests that agentic shopping is not just a new interface. It could become a new competitive environment.
What agentic shopping optimisation actually means
Agentic shopping optimisation is the practice of making a brand, product catalogue and commercial proposition legible to AI shopping agents.
It is not just “AI SEO” and it is not simply adding more keywords to product pages. In fact, keyword stuffing may matter far less in this new environment.
AI agents need structured, accurate and decision-ready product information. They need to understand not just what a product is, but who it is for, when it is relevant, why it is better, whether it is available, whether it is good value, and whether the merchant can be trusted.
That means the new optimisation layer may include:
- Product feeds that are complete, accurate and frequently updated.
- Structured product data that clearly communicates price, availability, variants, shipping, returns and attributes.
- Product descriptions written for decision-making, not just persuasion.
- Clear use cases, buying guides and comparison content.
- Review signals and trust indicators.
- Brand authority across the open web.
- Consistent data across Shopify, Google Merchant Center, ChatGPT merchant feeds, marketplaces and affiliate channels.
- Partnerships and product bundles that help agents understand contextual relevance.
- Content that answers real buying questions, not just generic category keywords.
ChatGPT’s merchant guidance says that most merchants start with product feeds, which help power how products appear in ChatGPT. It also says feeds can give merchants greater control over accuracy and freshness.
Source: ChatGPT Merchants — Power product discovery in ChatGPT
In SEO, the page was the unit of optimisation.
In agentic shopping optimisation, the unit of optimisation is the product, the merchant and the decision context.
Why product data becomes the new metadata
In early SEO, metadata helped search engines understand a webpage. Titles, descriptions, headings, internal links and schema all played a role in making pages easier to classify and rank.
In agentic shopping, product data plays a similar role.
AI agents need clean and reliable commercial data. A product with incomplete sizing, unclear availability, weak imagery, missing attributes, vague descriptions or inconsistent pricing is harder to recommend confidently.
This is especially important for Shopify merchants.
Shopify stores already contain much of the data AI agents need: product titles, descriptions, images, variants, collections, tags, prices, inventory, shipping settings and customer reviews. But in many stores, that data has been created mainly for human browsing or internal catalogue management. It has not necessarily been structured for AI interpretation.
That creates a new optimisation opportunity.
The brands that treat their Shopify catalogue as an AI-readable product knowledge base will be better prepared for agentic shopping than brands that treat it as a simple online shelf.
The new ranking factor: confidence
Search engines ranked pages. AI shopping agents recommend choices.
That difference matters.
A search engine can show ten blue links and let the user decide. An AI shopping agent is often expected to narrow the options, explain the reasoning and make a confident recommendation.
So the question becomes: what makes an AI confident enough to recommend one product over another?
The answer is likely to include a combination of data quality, relevance, authority, availability, price competitiveness, reviews, fulfilment reliability and contextual fit.
For example, if a shopper asks, “What is the best carry-on suitcase for a football fan travelling to the World Cup?”, an AI agent will need to understand far more than the phrase “carry-on suitcase”.
It may need to know:
- Which country or team the shopper supports.
- Whether the product is officially licensed.
- Whether it is cabin-size.
- Whether it ships to the shopper’s country.
- Whether it will arrive before the travel date.
- Whether the design is suitable as a gift.
- Whether there are variants for different national teams.
- Whether the merchant is trustworthy.
- Whether the product has social proof.
- Whether the price is competitive.
This is where agentic shopping optimisation becomes commercially valuable.
The better your product data and surrounding content answer those questions, the more recommendable your product becomes.
Why this matters for Shopify brands
Shopify brands are particularly exposed to this shift because they rely heavily on discoverability.
A Shopify store does not automatically receive the footfall of a marketplace. It has to create, earn or buy traffic. Historically, that traffic has come from SEO, paid search, paid social, email, influencers, affiliates, marketplaces and partnerships.
Agentic shopping could become another major source of demand.
But it will not behave exactly like those channels.
In paid social, the brand interrupts or inspires.
In SEO, the brand ranks.
In marketplaces, the brand competes inside a controlled platform.
In agentic shopping, the brand is selected by an intermediary that is trying to satisfy the buyer’s intent.
That means the optimisation challenge is different. Merchants need to make sure AI agents can answer the question: “Is this the right product for this specific customer right now?”
For Shopify brands, this creates both risk and opportunity.
The risk is that products become invisible in AI-led shopping journeys because the data is incomplete, inconsistent or not available to the systems making recommendations.
The opportunity is that early movers can build an advantage before the channel becomes crowded.
The partnership angle: why collaborative commerce matters
Agentic shopping also changes the role of partnerships.
In traditional ecommerce, partnerships often drive reach. A brand partners with another brand, influencer, publisher, retailer or platform to access a new audience.
In agentic commerce, partnerships may also become signals of relevance.
If two complementary brands are frequently connected through bundles, co-marketing, shared audiences or contextual use cases, that relationship may help AI systems understand when the products belong together.
For example, a travel accessories brand might become more discoverable when connected to travel content, airline partnerships, event-based shopping guides, luggage retailers, sports merchandise, hotel offers or destination-specific campaigns.
This is where collaborative commerce becomes powerful.
AI agents will not only look for isolated products. They will look for complete solutions. The brands that can position themselves inside useful buying contexts may have a stronger chance of being recommended.
That is especially relevant to the work we are doing with Alvio.
Alvio is built around the idea that ecommerce growth does not only come from isolated stores competing for traffic. It also comes from smarter collaboration between retailers, suppliers, brands and complementary products. In an agentic commerce environment, that partnership data becomes even more valuable because it helps define where a product belongs, who it is relevant to and what buying context it fits into.
If AI agents are going to recommend complete solutions rather than isolated products, then collaborative commerce becomes part of the new discovery layer.
Why this is personal to my work
For me, this shift sits at the intersection of two areas I have spent years working in: Shopify ecommerce and collaborative commerce.
As the founder of Inspira Digital, one of the longest-running Shopify Expert agencies, I have spent more than 18 years helping businesses build, optimise and grow online stores. That work has included Shopify store setup, theme customisation, UX improvement, conversion rate optimisation, ecommerce strategy and technical architecture.
I have also worked as a Solutions Architect and CTO, which means I look at ecommerce from both sides: the commercial side and the technical side. It is not enough for a store to look good. It has to perform. It has to convert. It has to scale. And increasingly, it has to be understood by the systems that shape customer discovery.
As the founder of Alvio, my focus is also on the next phase of ecommerce growth: helping retailers and suppliers create smarter partnerships, improve product matching and unlock new forms of collaborative commerce.
That is why agentic shopping optimisation matters.
It brings together product data, technical structure, brand positioning, partnerships, conversion strategy and AI-led discovery. These are no longer separate areas. They are becoming connected parts of how products will be found and recommended.
Being early matters
The comparison with SEO is not that agentic shopping will work exactly like Google search. It will not.
The comparison is about timing.
In the early days of SEO, brands that moved early built content libraries, domain authority, technical foundations and search visibility before the market became saturated. Later entrants had to spend more money and effort to catch up.
The same pattern could happen with agentic shopping optimisation.
Early-moving brands can start now by making their product data cleaner, their catalogue more structured, their content more useful, their reviews more visible, their partnerships more strategic, and their merchant feeds more complete.
They can start learning how AI systems describe them, where they appear, where they are missing, and which competitors are being recommended instead.
This is the equivalent of checking where you ranked on Google in the early 2000s.
But instead of asking, “Where do we rank for this keyword?”, the new question is:
“Does an AI agent recommend us when a customer describes the problem we solve?”
The brands that win will optimise for decisions
The next phase of ecommerce will reward brands that understand how decisions are made by both humans and machines.
That does not mean replacing brand, creativity or storytelling with data. It means making those assets easier for AI systems to understand and use.
A strong product story still matters. A distinctive brand still matters. Customer trust still matters. Reviews still matter. Partnerships still matter. Content still matters.
But they need to be connected in a way that AI agents can interpret.
In practical terms, that means every ecommerce brand should start asking:
- Is our product data complete and consistent?
- Are our products clearly differentiated?
- Do we explain who each product is for?
- Do we answer real buying questions?
- Are our reviews and trust signals visible?
- Are our shipping, returns and availability data clear?
- Are our product feeds ready for AI shopping platforms?
- Are we present in the sources AI systems use to make recommendations?
- Do our partnerships create useful context around our products?
- Can an AI agent confidently recommend us?
These questions will become as normal as SEO audits once were.
The future of ecommerce visibility
Agentic shopping optimisation is still early. The rules will evolve. AI platforms will change. Merchant tools will mature. Measurement will improve. New agencies, dashboards and optimisation frameworks will emerge.
But that is exactly why the opportunity matters now.
By the time every ecommerce brand is talking about agentic shopping optimisation, the advantage of being early will be gone.
The brands that act now can build the foundations before the market catches up. They can make their products easier to understand, easier to compare, easier to trust and easier to recommend.
In the early SEO era, ranking number one on Google became one of the most valuable positions in digital commerce.
In the agentic commerce era, the equivalent may be becoming the product an AI agent recommends first.
And just like SEO, the winners will not simply be the brands with the biggest budgets. They will be the brands that understand the new discovery layer before everyone else does.
About the author
Mike Harding is the founder of Alvio and Inspira Digital, one of the longest-running Shopify Expert agencies. He is an experienced ecommerce specialist, Shopify consultant and brand strategist with more than 18 years of experience helping businesses build, optimise and grow successful online stores.
Through Inspira Digital, Mike has worked with brands on Shopify store setup, theme customisation, UX improvement, conversion rate optimisation and ecommerce strategy, helping retailers create scalable online experiences that improve customer journeys, increase conversions and support sustainable business growth.
As founder of Alvio, Mike is focused on the future of collaborative commerce, AI-led product discovery, retailer-supplier partnerships and agentic shopping optimisation. His work explores how ecommerce brands can use smarter partnerships, better product data and emerging AI shopping channels to become more discoverable, more relevant and more commercially effective.
Having also worked as both a Solutions Architect and CTO, Mike combines technical expertise with commercial insight, helping ecommerce brands bridge the gap between technology, customer experience and revenue growth.
Mike writes about agentic commerce, Shopify growth, AI shopping optimisation, collaborative commerce, retailer-supplier partnerships and the changing ways consumers discover and buy products online.
Sources
- OpenAI — Introducing shopping research in ChatGPT
https://openai.com/index/chatgpt-shopping-research/ - OpenAI — Powering Product Discovery in ChatGPT
https://openai.com/index/powering-product-discovery-in-chatgpt/ - ChatGPT Merchants — Power product discovery in ChatGPT
https://chatgpt.com/merchants/ - Google — Shopping launches agentic checkout and more AI shopping tools
https://blog.google/products-and-platforms/products/shopping/agentic-checkout-holiday-ai-shopping/ - McKinsey — The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchants - arXiv — Agentic Markets: Equilibrium Effects of Improving Consumer Search
https://arxiv.org/abs/2603.25893 - arXiv — What Is Your AI Agent Buying? Evaluation, Implications and Emerging Questions for Agentic E-Commerce
https://arxiv.org/abs/2508.02630 - arXiv — ShoppingComp: Are LLMs Really Ready for Your Shopping Cart?
https://arxiv.org/abs/2511.22978 - arXiv — Product Information Extraction using ChatGPT
https://arxiv.org/abs/2306.14921 - Google Search Central — Product structured data documentation
https://developers.google.com/search/docs/appearance/structured-data/product - Schema.org — Product schema
https://schema.org/Product - Google Merchant Center Help — Product data specification
https://support.google.com/merchants/answer/7052112
