AEO vs SEO for eCommerce: What Actually Gets You Cited in 2026

SEO is about being found by people. AEO is about being the source the answer is built from. They sound similar. The work, the timelines and the buyer expectations are not. We have spent the last twelve months running AI visibility baselines, share-of-model studies and GEO audits across spirits, consumer health, sports nutrition, kitchenware and functional drinks. The same pattern keeps showing up. Brands invest in SEO playbooks that no longer move the commercial needle. The discovery moment has moved into ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews and Amazon Rufus. The brands that show up there are not always the ones who rank highest on Google. They are the ones who are easiest for a model to extract from and credible enough for a model to cite. This is a buyer-facing piece for eCommerce directors, marketing leads and founders who are comparing agencies that all claim to do AI search. The goal is to give you a clear mechanism, a sober view of where the work pays off and a useful set of questions to ask before you sign anything. **The mechanism in plain English** A traditional search engine returns ten blue links and hopes one of them is good. An answer engine builds a single response by reading multiple sources, deciding which ones to trust and merging them into something coherent. That changes what gets rewarded. Three things matter to an AI engine when it decides what to say about a product or a brand. It has to reach your content. It has to extract a clean fact from it. And it has to trust the source enough to repeat the fact in an answer. SEO has historically optimised for the first one, treated the second one as a tagging exercise and largely ignored the third. AEO sits on all three at once. The work looks like SEO from a distance because some of the levers overlap, but the order of priority and the success metric are different. **Where the SEO playbook breaks** Three big shifts have undone parts of the old playbook. Google deprecated FAQ rich results in May 2026 and HowTo rich results went the same way earlier. The visible reward for piling schema onto product and category pages has shrunk. Many agency audits still recommend FAQ markup as a high-ROI fix. It isn't. The content inside an FAQ block can still be useful because AI engines extract from well-shaped question and answer content directly, but the markup is doing very little of the work. Click-through from organic results has thinned because AI Overviews now summarise the answer before the user clicks. You can rank in position one and still not get the visit. Conversion now lives inside the summary, not the link. The citation surface has fragmented. ChatGPT leans heavily on LinkedIn and on a handful of authoritative news and reference domains. Claude leans on Reddit, Wikipedia and forums. Perplexity leans on traditional news and primary sources. Google AI Overviews still pulls from classic SEO surfaces but blends them with its own knowledge graph. Treating AI search as one monolithic surface produces work that pleases nobody. If your AEO agency is still showing you FAQ schema implementations and generic "we will improve your search visibility" deliverables, they are selling 2024 work at 2026 prices. **What AEO actually optimises** The work that earns AI citations breaks into four streams. The first is reachability. AI engines crawl with their own user agents. OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot, Google-Extended and Applebot-Extended are the priority list. Many brand sites have robots.txt rules, CDN configurations or firewall policies written when only Googlebot mattered. Those rules now silently block half the AI surface. We have audited brand sites where the most expensive content on the domain is invisible to ChatGPT and Claude because nobody updated the bot allow list. The fix takes a day. The downstream content work is pointless if you skip it. The second is extraction clarity. Models read your page and pull facts from it. They are looking for entities, not paragraphs. What is this product. What category does it sit in. What is it for. What is it not for. Who is it for. What does it cost. What problems does it solve. Pages built around long brand storytelling with the actual product facts buried four scroll-lengths down read poorly to a model. Pages built around a clear product entity with structured attributes, named comparisons and concrete claims read well. The third is entity-level structured data. We have stopped recommending FAQ and HowTo schema in audits. We still recommend Organization, Service, Product and Article schema because these help engines disambiguate your brand from competitors with similar names and place your products in the right category. This is the schema work that still earns its keep. The fourth, and the one most agencies underplay, is authority. Models cite sources they consider credible. Credibility is largely an authority signal: backlinks, referring domains, third-party mentions, reviews, expert quotes and presence on the platforms each engine leans on. Lmo7's own site scores 88 out of 100 on AI readability. Our domain rating is 9. We are technically excellent and yet still functionally invisible, as we are such a new organisation. Both problems need fixing. Neither is solved by the other. **Two tracks, two timelines** The most useful frame we give clients is that AI search work has two tracks. The quick wins track covers content clarity, page structure, entity schema, Q&A coverage, comparison content and the bots check. These are the levers that show movement inside a quarter. Trip Drinks moved their average position across the major LLMs from 7th to 3rd in 60 days and lifted AI referral traffic by 33%, mostly through this track. The work was site signals, content upgrades on the DTC product pages and a focused round of citation source optimisation tied to a moving commercial number. [Barnaby Whicher](https://www.linkedin.com/in/barnaby-whicher-58903b153/), their eCommerce lead, summed it up cleanly: "Lmo7 have provided us real clarity on our AI search visibility across the major models and a practical plan to improve it." Practical plan is doing the work in that sentence. Clarity without an action plan never moves the needle. The long game track covers authority. Backlinks, citations, digital PR, expert quotes, retail presence, review density and category mentions. This is slower, more expensive and usually outside what most monthly AEO retainers cover. Any agency telling you they will lift your AI citation rate in 30 days through content alone is either over-promising or quietly relying on you already having authority they can switch on. A credible AEO engagement names both tracks, prices both and is honest about what month three will and will not look like. **Engine-specific work** If your category leans on a specific platform, your citation strategy should reflect that. A B2B services audience that lives on LinkedIn will be picked up disproportionately by ChatGPT. If you sell into that audience, your LinkedIn presence and the article surface around it is part of your AI search programme, not a separate marketing thing. A consumer audience that compares products on Reddit will affect Claude's answer disproportionately. Pretending Reddit is not part of your discovery surface because it does not show up in your existing analytics is leaving a citation source on the table. A news-heavy category, particularly anything regulated or with a trust dimension such as consumer health, supplements or financial services, will be picked up disproportionately by Perplexity. Press, expert commentary and third-party authority content matter more here than schema work. Google AI Overviews still rewards traditional SEO discipline. That is the engine where the legacy playbook does most of its remaining work. Most brands do not need to win all four. They need to win the two that map to where their buyers actually compare and decide. **So what should you do next** If you are doing AI search work for the first time, start with a baseline. Find out where you stand across the engines that matter to your category. Do not commission optimisation work before you have one. Optimising in the dark is the most expensive way to do this. If you have a baseline and you have not moved on it inside a quarter, the diagnosis is usually one of three things. The action plan inside the baseline was too vague to execute. The internal owner was never named. Or the work assumed quick wins would carry the whole load and the authority track never got funded. All three are fixable. If you are choosing between agencies, the question is not who has the slickest deck. It is which agency understands how AI engines actually build answers, can show you the mechanism, can name what they will and will not move in the first 90 days and ends every deliverable with two or three priorities and an owner. That is the work we do at Lmo7. If you want to see what a baseline looks like for your brand, we can run one. Two-week turnaround, fixed scope, ends with a prioritised action plan you can hand to a team. From there, the Challenger Stack covers the hands-on optimisation work month to month and the Enterprise workshop sits above it for portfolios that need stakeholder alignment first. Either way, ask for the mechanism - not the magic! --- Stephen Honight is the founder of Lmo7, the AI-native agency helping challenger consumer brands win in AI-powered discovery and agentic commerce. Lmo7 works with brands including Trip Drinks, Veloforte, Brown-Forman and Haleon across Amazon, D2C and AI search surfaces.