Most retailers are talking about "adding AI" - chatbots, recommendations, the usual suspects. But the real conversation should be different: is your platform built for how AI shopping works?
There's a gap between understanding AI is important and understanding what AI-ready means. The gap isn't about AI capability - it's about platform readiness. AI-driven shopping isn't just a new channel; it requires fundamentally different infrastructure.
The numbers make this clear. ChatGPT Shopping traffic to retail sites grew 693% year-over-year in 2025, according to Adobe Analytics. This isn't a trend to watch - it's a shift that's already reshaping how people discover and buy products.
Let's start with what's fundamentally changed.
Traditional ecommerce flow: Browse → Search → Product Page → Cart → Checkout
AI-driven shopping flow: Conversation → Recommendation → (potentially) Instant purchase
The difference isn't subtle. Traditional ecommerce assumes people know what they're looking for or at least know where to start browsing. AI shopping starts with a question or a need, and the AI does the discovery work.
Here's the critical shift: AI needs to parse your data, not just display it.
What this means in practice:
Product data needs to be structured for machine understanding, not just human reading
Content needs to answer questions an AI might ask on behalf of a shopper
The "storefront" might not be your website anymore
OpenAI partnered with Stripe and Shopify to launch ChatGPT Instant Checkout. Users can now find a product through conversation and complete the purchase without ever leaving the chat interface. This isn't science fiction - it's happening now.
And it's not a niche behaviour. Research shows 60% of consumers have already used AI to shop in some form - whether that's interacting with a chatbot, getting recommendations, or using voice assistants to order products.
The problem:
Most ecommerce platforms were built for human browsing, not AI parsing. Product descriptions were written for marketing copy, not for answering natural language questions. Structured data - specifications, attributes, use cases - is often missing or inconsistent.
The test question: When an AI asks "Which coffee machine is best for small apartments?", can your product data answer that?
If your product attributes don't include size, counter space requirements, capacity, or use case tags, the AI can't make an informed recommendation. It might parse your marketing copy and guess, but that's not the same as having structured, reliable data.
What I see in practice:
Product specifications buried in PDF downloads. Attributes formatted inconsistently across categories. Critical information that exists for some products but not others in the same category.
This inconsistency doesn't just frustrate shoppers - it makes your products invisible to AI recommendations.
The friction point:
Chatbots exist on many ecommerce sites, but they're often bolted on rather than integrated. There's a gap between "chatting" and "buying" - if an AI recommends your product, can someone purchase it seamlessly within that conversation?
Most platforms still force users back to traditional browse-and-cart flows. You chat with the bot, it suggests a product, then you click through to a product page, add to cart, navigate to checkout, fill in details, and complete purchase. That's not conversational commerce - that's a chatbot acting as a glorified search filter.
Why this matters:
Friction kills conversion. If AI shopping requires leaving the AI assistant and navigating a traditional checkout flow, you're losing the advantage. The promise of AI shopping is convenience and speed. Breaking that flow undermines both.
The platforms that will succeed here aren't necessarily building their own AI - they're ensuring their infrastructure can integrate with AI discovery channels and support seamless purchase flows, wherever those conversations happen.
The consumer concern:
Over 80% of consumers worry about how AI collects and uses their data. This isn't paranoia - it's a legitimate question about how personalisation works and what happens to shopping behaviour data.
The questions retailers aren't asking:
How does AI personalisation align with our privacy commitments?
If a customer asks our AI chatbot why it recommended a specific product, can it explain?
Are we treating AI transparency like payment security - as fundamental, not optional?
Most retailers haven't thought this through. They've implemented AI features without considering the transparency and control customers expect. The assumption seems to be "AI is helpful, customers will trust it" - but trust isn't automatic.
The brands I'm seeing succeed aren't hiding how their AI works. They're explaining it. They're giving customers control over personalisation settings. They're building AI that can justify its recommendations, not just make them.
Being AI-ready isn't just about technology - it's strategic design. Here's what it requires.
Every product should be able to answer three questions:
Who is this for?
What problem does it solve?
How does it compare?
Example of the shift:
❌ Not AI-ready: "Premium leather boots - $299"
✅ AI-ready: "Waterproof leather hiking boots for winter trails, insulated to -20°C, fits wide feet, comparable to Brand X but $50 less"
The second version gives an AI everything it needs to make informed recommendations. It has use case (winter hiking), key specs (waterproof, -20°C insulation), fit considerations (wide feet), and comparative value. An AI can now confidently recommend this product when someone asks "What boots should I get for winter hiking if I have wide feet?"
This level of structure needs to exist across your entire catalogue, not just hero products.
AI-ready platforms support the ability to go from question → recommendation → purchase in one flow. This doesn't mean you need to build your own ChatGPT competitor. It means ensuring your platform can integrate with AI discovery channels through APIs, structured data feeds, and flexible checkout options.
The technical requirements include:
Product APIs that AI systems can query
Structured data formats (schema markup, product feeds)
Flexible checkout flows that work outside your traditional website
Integration capabilities with platforms like Shopify, OpenAI, or other AI commerce ecosystems
Just like we learned SEO (Search Engine Optimisation), there are now GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation). This isn't about gaming AI systems - it's about making your content genuinely useful when AI is trying to help shoppers.
What this looks like:
Product descriptions that answer natural language questions
Structured FAQs that address common queries
Clear, consistent attribute data
Content that explains use cases and comparisons, not just features
The goal is that when an AI assistant searches for "best running shoes for overpronators under $150", your product doesn't just match keywords - it provides the information needed to make that recommendation confidently.
AI-ready platforms have clear data policies, explainable AI, and give customers control. This isn't just good ethics - it's good business.
Why it matters: AI-driven shoppers had 31% higher conversion rates during the 2025 holidays compared to traditional shoppers. But that advantage only holds if customers trust the AI experience. Break that trust with opaque data practices or creepy personalisation, and you lose both the conversion uplift and the customer.
The question isn't "Should we add AI to our ecommerce?"
The question is: "Is our platform built for a world where AI mediates discovery?"
Because that world is already here. In just one month of 2024, 1.93 million U.S. retail visits came directly from ChatGPT. That's not experimental traffic - that's real shoppers using AI as a discovery channel.
You don't need to have all the answers today. But you do need to start asking the right questions:
About discoverability:
Can AI systems easily understand and recommend our products?
Is our product data structured for AI parsing, or just human reading?
Are we optimising for how AI assistants search and recommend?
About transaction capability:
If someone wants to buy through an AI assistant, is our platform ready?
Can we support purchase flows outside our traditional website?
Do we have the APIs and integrations needed for conversational commerce?
About trust:
Are we building trust in how we use AI, or just building features?
Can customers understand why AI recommended something?
Do we give customers control over AI personalisation?
The platforms that will win aren't necessarily the ones with the most AI features. They're the ones built for how people want to shop when AI is part of the journey.
That means structured data that AI can parse. Flexible commerce infrastructure that supports purchases wherever conversations happen. And transparent, trustworthy AI that customers feel comfortable using.
If you're questioning whether your platform is ready for AI-driven commerce, let's talk. We'll help you see where the gaps are - and what a realistic path forward looks like.
Director
Paved Digital’s co-founder Damian Purvis has been supporting digital transformation projects for 15+ years. This experience spans ecommerce, marketplaces, web CMS, retail media and email marketing working with clients in retail, manufacturing and financial services.
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