The question for any retail leader right now is whether your products are actually legible to the agents that are deciding what to recommend.
If you ask most retail leaders, they may believe that their data is ready for AI. But, in reality, what they might actually have is a collection of product pages built for humans to browse. AI doesn't browse. It reasons across structured data, matches attributes to intent, and skips anything it can't parse.
And most retail leaders don’t lack a desire to participate in this new era. But they do lack bandwidth.
However, here’s the good news: the work to get agentic-ready is more foundational than it is complex.
Here's how.
What's actually happening in AI shopping today
It's helpful to separate what's changing into two layers:
1) The technology layer: The commerce plumbing is here
AI systems are no longer just answering questions, they are now interacting with tools and coordinating actions.
UCP (Universal Commerce Protocol), co-developed by Google and Shopify, standardizes how AI agents interact with real commerce: product catalogs, inventory, pricing, checkout logic, fulfillment. Think of it as the shared language that means a merchant doesn't need a separate technical integration with every AI platform that emerges.
2) The behavior layer: Consumers are using AI to research and, now, to buy
Consumers have already adopted AI as a research tool, compressing what used to be 20 browser tabs into a single conversation. They're asking for gift ideas with specific constraints, getting product explanations on sizing, fit, compatibility, and ingredients, and running comparisons across brands and price points in seconds.
What's changed more recently is that these conversations are starting to end in purchases. On major AI platforms, product recommendations can now lead to transactions (powered by Agentic Storefronts) letting customers actually shop the recommendations that they're receiving.
It's worth noting: the agentic landscape is moving fast. This pace of change can feel disorienting, but it's also an argument for building on infrastructure that absorbs that complexity for you, rather than chasing those shifts (and their maintenance) yourself.
The real bottlenecks (and they're not what you think)
Most retailers today are in the early adopter phase: experimentation is high, but repeatable playbooks are still forming. The bottlenecks we see most often aren't related to any agentic technology itself. They're based on data and infrastructure.
Agentic commerce requires clean product data preferably sent to AI platforms via direct API (like with Shopify Catalog). Messy product data could mean no discovery at all for your products.
The cost of getting this wrong
This is the part that deserves more attention than it usually gets. Agentic commerce works as well as the information it can understand.
If your product, pricing, inventory, shipping, and policy details are not accurate or structured in a machine-readable way, the information provided will be incomplete or inaccurate, and agents might skip recommending you altogether. And unlike a low-ranking Google result, there's no "page two" to scroll to. The agent simply moves to the brand that's easiest to understand.
It is still early days, but if you wait on this, you'll be trying to retrofit foundations under pressure while competitors compound. This is the same dynamic we saw with mobile and performance marketing: once the ecosystem matured, early movers had already locked in better data, cleaner operations, and faster test-and-learn cycles.
How to measure AI pilots
If you're running pilots in agentic commerce, measure impact in P&L and operational terms, not just engagement. For example:
- Traffic and orders originating from AI sources (where attribution is possible)
- Conversion impact on assisted sessions (AI-assisted vs. baseline)
- AOV / attach rate (are recommendations improving baskets?)
- Return rate / refund contacts (a real proxy for recommendation quality)
- Abandoned cart rates (baseline vs. agentic traffic)
One metric to insist on: Understand that AI can influence decisions without being the last click. Your measurement needs to acknowledge influence without letting it become a blank check.
How leaders can get agentic-ready
The window for first mover advantage is open right now. Early movers have an incredible opportunity, especially if they focus on the fundamentals that make agentic commerce work.
The goal this quarter is to move now on that unglamorous work: structured product data, policy clarity, governance, and measurement. So, you can capture the benefits (and set yourself up for future success) as new AI surfaces and experiences increase in popularity.
1) Fix the inputs: data readiness
Before you optimize experiences, you have to fix the inputs so agents can confidently discover and compare your products, as well as make recommendations that are actually shoppable. This is the least glamorous step and the most important one. Everything downstream (GEO, agentic storefronts, measurement) breaks down if the underlying data is incomplete or poorly structured.
The best way to do this is Shopify Catalog (available to all Shopify merchants or via the Agentic Plan without a Shopify online store). Catalog syndicates your product data to major AI platforms via direct API, so AI can actually understand what you sell, match shopper intent to the right variant, and recommend with confidence. This is what makes Catalog a fundamentally different kind of asset from a standard product feed.
Here are the things to focus on:
- Build or complete a product attribute strategy (taxonomy, variants, materials, fit, compatibility, use cases)
- Enrich metadata with structured fields
- Normalize imagery (consistent angles, alt text, naming conventions)
- Ensure policy clarity (shipping, returns, warranty) is machine-readable and current
For a detailed breakdown, check out our recent deep dive: Agentic-Ready Product Data: How to Get It & the Cost of Inaction.
2) Treat GEO as a discipline
GEO (Generative Engine Optimization) is about making your content easy for AI search and chat tools to understand, trust, and pull into their answers. It's about providing the right inputs for your product data to be accurately surfaced in any potential AI recommendation it could qualify for.
If AI agents don't have accurate or up-to-date information, that means you could lose high-intent shoppers at the moment they're ready to buy. Either from out-dated or inaccurate information being presented, or your products being skipped entirely (even if they were a valid choice).
Here's what to think about:
- Don't neglect SEO fundamentals. AI chatbots do still use search engines to provide inputs for answers, so ranking high in SEO helps with GEO.
- Build a high-trust brand. Brands are a shortcut to trust, and AI wants to recommend trusted brands. Authority, reputation, relevance, and popularity matter.
- Give AI direct API access to your data. AI can scrape your website, but it would much rather deliver real-time inventory, accurate pricing, and complete product attributes through a direct API call with Shopify Catalog.
There's a layer beyond product data that is also often not getting enough focus: brand context. LLMs don't just need to know what you sell; they need to know who you are, what you stand for, what your return policy is, and how you'd answer a customer's edge-case question. On Shopify, the Knowledge Base App is how you feed that context directly to AI systems, so you can actually influence how agents represent you.
For a more in-depth exploration of GEO, read The GEO Playbook: How (& Why) to Optimize for AI Discovery.
3) Run a pragmatic, phased approach
The instinct is to do everything at once. But for high complexity businesses, a phased approach lets you ship real improvements while ensuring you’re getting the fundamentals right.
Depending on the size and complexity of your business, exact timelines will vary. But with Shopify Catalog and Agentic Storefronts, all of the below gets easier: lower lift for your team and a faster path to market.
Phase 1: Get the fundamentals "agent-ready"
- Pick an owner + success metrics. Assign a cross-functional lead and define what "good" looks like (e.g., improved conversion from AI-driven sessions, more AI-referred traffic, etc.)
- Audit your product data. Identify gaps in attributes, variant structure, inventory accuracy, pricing consistency, shipping promises, returns/warranty language, sitemap.
- Standardize the basics. Lock a product attribute taxonomy, clean up variant naming, normalize imagery/alt text, and ensure shipping/returns policies are current and consistent across site, FAQs, and support.
- Create a "machine-readable" source of truth. Make sure key fields (title, description, images, price, inventory, weight) live in structured fields, not only in long-form descriptions.
- Scope integration work. Explore direct API access: Will you build and maintain the integrations yourself? Or reduce the lift with Shopify Catalog and Agentic Storefronts?
Phase 2: Make it discoverable and measurable
- Treat GEO like a weekly practice. Refresh PDP templates, FAQs, and collection pages, so AI systems can easily extract unambiguous answers (sizing, compatibility, care instructions, delivery windows).
- Strengthen trust signals. Tighten review coverage, retailer policies, contact/support clarity, and "about" credibility. Anything that reduces uncertainty for both customers and AI systems.
- Improve instrumentation. Set up monitoring for AI-driven traffic and behavior (landing pages, query patterns, conversion rate, return rate, support contact rate) and add logging for AI-referred mismatches (check for individual products falling below your AI-referred traffic baselines).
- Run controlled experiments. Test 1–2 high-intent categories or bestsellers: enrich attributes, add structured FAQs, tighten imagery standards, and compare performance vs. a control group.
Phase 3: Scale
- Codify governance. Formalize review processes for catalog changes, policy updates, and claim approvals; define who signs off and how issues are triaged.
- Scale what works. Expand the attribute strategy and GEO improvements from the pilot set to the next set of products/categories, based on measured lift.
- Build a continuous improvement loop. Monitor traffic, real customer/agent questions, complaints, and returns reasons to update attributes, FAQs, policies, and support workflows regularly.
Getting the infrastructure right
The compounding challenge for most retailers isn't a lack of agentic options, it's the maintenance burden of connecting to each one separately.
Every new AI platform is another integration to build, another data feed to keep current, another checkout flow to test and monitor. And as the landscape shifts, most retailers don't have the engineering bandwidth to keep up with that on their own.
Shopify's approach is that merchants shouldn't have to manage that complexity. When the ecosystem shifts, you're not rebuilding from scratch.
In practice, that means: your products are syndicated to major AI platforms without separate integrations. Your inventory, pricing, and policies are accurate in real-time, so agents aren't working from stale information. And when purchases happen through new AI channels, orders flow cleanly into your existing operations with full attribution, so you can measure what's working and scale it.
This gives you infrastructure that keeps pace with a fast-moving ecosystem, so your team can focus on the strategy and product work that moves the needle.
Build the foundations now
Agentic commerce isn't a single product you "launch." It's a shift in how shoppers discover, evaluate, and buy. And it's already underway.
The brands that will win aren't the ones who wait for the channel to mature before taking it seriously. They're the ones building clean foundations now. The tools are here and the path is clear.
That's the opportunity this quarter. Do the work now, get ahead of the curve, and earn the right to scale fast when the channel demands it.
