You've probably seen the term floating around. AEO (Agentic Engine Optimisation) is getting a lot of attention right now, and for good reason.

But most of the content on it is either too technical or too vague to be useful. So here's what eCommerce founders actually need to know.

What AEO Actually Is

For the past decade, the goal was ranking. Get your site to the top of Google, drive traffic, convert.

That model is shifting.

Zero-click Google searches rose from 56% in 2024 to 69% in 2025. That means more than two thirds of searches now end without anyone clicking through to a website. Instead, an AI pulls an answer and surfaces it directly

At the same time, consumers are increasingly using platforms like Gemini, Perplexity, and ChatGPT to find products, compare options, and make decisions — all without visiting a traditional search result.

AEO is the practice of optimising your brand's content and product data so that AI agents can find you, understand you, and recommend you — not just rank you.

The distinction matters. SEO gets you found. AEO gets you used.

Why This Is Particularly Relevant for eCommerce

Adobe reports a 4,700% year-over-year surge in AI-driven traffic to eCommerce sites in 2025, and 41% of consumers now trust AI product recommendations over paid search ads.

This isn't a future trend. It's already affecting how your products are discovered.

When someone asks ChatGPT "what's the best magnesium supplement for sleep" or "which Australian skincare brand is worth trying," AI platforms are generating answers and citing specific brands. If your product data isn't structured in a way that AI agents can read and trust, you're not in that conversation.

Agentic commerce compresses days of research, discovery, and comparison into near-instant moments of evaluation, changing how consumers allocate attention and make choices. That's a significant shift in how the purchase decision actually happens — and most brands haven't adjusted for it yet.

What to Optimise: 8 Practical Starting Points

1. Your Product Descriptions

AI shopping agents don't interpret vague copy. Products with well-structured, self-contained descriptions covering price, reviews, features, and availability are more likely to be retrieved and cited in AI recommendations.

Write descriptions that answer questions directly. What is it, who is it for, what does it do, and what makes it different. Specificity matters more than creativity here. Both need to coexist.

2. Structured Data and Schema Markup

Schema markup provides the vocabulary that LLMs require to ingest product specifications accurately. Without structured data detailing price, availability, and ratings, an AI engine is forced to guess — which usually results in the product being excluded from the consideration set entirely.

For eCommerce, the priority schema types are Product, Review, and FAQ. If you're on Shopify, most themes support this natively, but it's worth auditing whether it's actually implemented correctly.

3. Customer Reviews

Reviews aren't just a conversion tool. They're a trust signal for AI agents.

AI engines look for agreement across multiple credible sources before citing something as factual. When multiple high-authority sources repeat the same core facts, AI models see your information as verified and are more likely to quote it.

Volume, recency, and specificity all count. A review that mentions a specific product benefit ("helped with my sleep within a week") is more useful to an AI agent than a generic five-star rating.

4. FAQ Sections on Key Pages

AI agents are designed to answer questions. Pages structured around questions perform better in that environment.

Adding a concise FAQ section to your product and collection pages — built around real questions customers ask — gives AI agents something to pull from directly. Think: "Is this suitable for sensitive skin?" "What's the return policy?" "How does this compare to X?"

5. Brand Authority Signals

If an AI cannot verify who produced the content or where the data comes from, it deems the content unsafe to cite.

This means your About page, founder story, press mentions, and third-party coverage all matter. A brand that exists credibly across multiple sources is easier for AI agents to trust and reference. If your brand lives almost exclusively on your own website, that's a gap worth addressing.

6. Site Speed and Crawlability

None of this works if AI agents can't access and read your site efficiently.

Page speed, mobile optimisation, clean URL structures, and removing content hidden behind scripts or images are all foundational. As AI search becomes more conversational, content must be extractable and contextual. If it's buried in JavaScript or behind a slow load, it may as well not exist.

7. Content That Answers Real Questions

Blog content, buying guides, and educational pages serve a dual purpose in an AEO context. They perform in traditional search and give AI agents substance to pull from when customers are in research mode.

The key is writing for genuine intent. Create content for humans, but package it for AI — clear headings, short paragraphs, direct answers, and structure that makes it easy to extract a specific point.

8. Consistency Across Channels

If your product is described one way on your website, another way on your socials, and inconsistently across third-party stockists or review platforms, that creates noise for AI agents trying to form a clear picture of your brand.

Consistent product naming, descriptions, and positioning across every touchpoint strengthens your authority signal. It also just makes for better marketing.

Where to Start

AEO doesn't require a complete overhaul. It requires intention.

Audit your product pages first. Are descriptions specific and structured? Is schema in place? Do your pages answer the questions your customers are actually asking?

Then look outward. Where does your brand appear beyond your own site? Are those mentions consistent, credible, and current?

The brands that will compound visibility over the next few years are the ones building for both the human reader and the AI agent — at the same time.

That's not a technical challenge. It's a strategic one.