The biggest shopping channel in a generation is growing under your nose, and your SEO team's playbook doesn't cover it.
ChatGPT hit 900 million weekly active users as of early 2026 (per DemandSage's tracking of OpenAI disclosures). Morgan Stanley surveyed U.S. consumers and found that 16% of all Americans have already made a purchase influenced by ChatGPT. Their bull case projects agentic commerce reaching $385 billion in U.S. online spending by 2030. Walmart reported that customers using its AI assistant Sparky have a 35% higher average order value than those who don't (per its Q4 2025 earnings call, covered by AdExchanger).
These aren't projections about some distant future. This is happening right now, and there's an emerging discipline designed to address it. I call it Agentic Commerce Optimization, or ACO.
Agentic Commerce Optimization (ACO) is the practice of optimizing product data, feeds, and digital presence so AI shopping agents - ChatGPT, Google AI Mode, Perplexity - recommend and sell your products. It extends traditional SEO to cover the AI commerce channel where, according to research from Semrush, over 93% of AI Mode searches end without a click.
How Did We Get from SEO to ACO?
Every major shift in how people find and buy things online has produced a corresponding optimization discipline. Understanding the progression helps clarify why ACO is different and why it matters.
SEO (Search Engine Optimization) has been the backbone of digital marketing since the late 1990s. You optimize for keywords, backlinks, page speed, and structured data. The goal is traffic. Users search, click a blue link, land on your site, and hopefully buy something. After 25 years, the playbook is mature and well understood.
AEO (Answer Engine Optimization) emerged as Google started answering questions directly in search results. Featured snippets, knowledge panels, voice search results - these changed the game because users could get answers without clicking through. If you optimized for AEO, you were fighting for that answer box at the top of the page. The goal shifted from "get them to my site" to "be the answer Google displays."
GEO (Generative Engine Optimization) showed up alongside ChatGPT and other large language models. When someone asks an AI "what's the best running shoe for flat feet," the AI generates a response that may or may not mention your brand. GEO is about influencing those AI-generated responses through content strategy, brand mentions across the web, and structured information that LLMs can pick up during training or retrieval.
ACO (Agentic Commerce Optimization) goes a critical step further. It optimizes for the entire purchase flow within AI-powered experiences. The user asks ChatGPT for a running shoe recommendation, the AI surfaces product cards with prices and specs, and the user clicks through to purchase. The goal with ACO is not traffic or mentions - it's getting your product surfaced, recommended, and selected by AI agents as the best match for the user's intent.
The key difference between GEO and ACO: GEO gets you mentioned. ACO gets you bought.
Why Does Your SEO Playbook Fall Short?
I want to be specific about this, because the instinct most marketing teams have is to assume their existing SEO work translates. Some of it does. A lot of it doesn't.
What still matters: Product schema markup, clean site architecture, fast page loads, and accurate structured data remain important. AI agents still crawl the web, and they use the same underlying data that search engines use. If your product pages have solid Schema.org implementation, that's a foundation you can build on.
What doesn't translate: Keyword density, meta tag optimization, internal linking strategies, and most of the tactical SEO work your team does every week have minimal impact on how AI agents evaluate and recommend products. An AI agent deciding whether to recommend your running shoe doesn't care about your title tag or your H1 hierarchy. It cares about whether your product data answers the user's question completely enough to make a confident recommendation.
Consider a concrete example. A traditional SEO-optimized product page might have a title like "Men's Running Shoes - Brand X CloudRunner - Free Shipping." That's great for Google's ranking algorithm. But when an AI agent is comparing your shoe against four competitors to answer "what's the best cushioned running shoe under $150 for someone who runs 30 miles a week," it needs to know the cushioning technology, the intended weekly mileage range, the surface type it's designed for, the weight, the drop, and whether it works with orthotics. Most product pages don't have that information in a structured, parseable format.
AI agents parse product data differently than search crawlers. A Googlebot is looking at HTML structure, links, and keyword relevance. An AI shopping agent is trying to build a complete understanding of your product so it can compare it meaningfully against alternatives and make a recommendation it's confident in. The depth and specificity of your product information becomes the competitive advantage, not your keyword strategy.
What Numbers Should Worry SEO Teams?
Research from Semrush analyzing tens of millions of AI search sessions found over 93% of AI Mode searches end without a single click. That finding was published in early 2026 and it has significant implications for anyone whose strategy depends on organic click-through rates.
Meanwhile, the AI shopping channel is growing fast. Amazon claims its AI assistant Rufus generated $12 billion in incremental annualized sales by late 2025, with over 300 million users engaging with it (reported by CNBC and AdExchanger). Google's Universal Commerce Protocol is already live with Etsy and Wayfair, with Shopify, Target, and Walmart coming soon. Perplexity launched integrated shopping with PayPal checkout. Every major platform is building commerce directly into the AI experience.
McKinsey projects the global agentic commerce market at $3 to $5 trillion by 2030. Gartner forecasts that agentic AI will overtake chatbot spending by 2027.
The shift is accelerating faster than most teams realize. What took Amazon years to build in terms of merchant adoption, AI commerce platforms are compressing into months.
What Should SEO Teams Start Doing Now?
I've talked to dozens of retailers about this over the past year. The ones who are moving fastest tend to start with these practical steps:
1. Audit your AI bot access. Check your robots.txt file. Many sites inadvertently block GPTBot, ClaudeBot, PerplexityBot, and other AI crawlers. If AI agents can't access your product pages, you're invisible in AI shopping results. This is a 15-minute fix that most teams haven't done.
2. Assess your product data depth. Pull up your top 20 products and ask yourself: does the structured data on this page contain enough information for an AI to confidently recommend this product over a competitor's? If the answer is no, you've found your first project. Think about use cases, compatibility, materials, performance specs, and anything a knowledgeable salesperson would mention when helping a customer in a store. Tools like Paz.ai's AI readiness assessment can help benchmark where your catalog stands.
3. Implement llms.txt. This is an emerging standard (similar to robots.txt) that tells AI agents how to interact with your site. It specifies what content is available, how to access product data, and what actions are supported. Early adoption signals to AI platforms that you're ready for agent-driven commerce.
4. Enrich your catalog for conversational queries. People don't ask AI agents the way they type into Google. Nobody says "best running shoes 2026" to ChatGPT. They say "I need a shoe for long runs on pavement, I overpronate, and I want something under $150." Your product data needs to support that kind of natural-language matching. Add FAQ content, use-case descriptions, and comparison attributes at the product level.
5. Monitor your AI visibility. Start tracking whether your products appear in ChatGPT, Perplexity, and Google AI Mode responses for relevant queries. This is the ACO equivalent of checking your search rankings. If you're not showing up, you know where to focus. Platforms like Paz.ai are building monitoring tools for this, and you can start with manual testing today.
6. Join the commerce protocol programs. Google's Universal Commerce Protocol has an early access program and is already live with major retailers. OpenAI's commerce protocol focuses on product discovery and recommendations within ChatGPT, directing users to retailer sites for checkout. The retailers joining these programs now will have a significant head start as AI-driven shopping scales.
Does ACO Replace SEO?
I want to be clear: I'm not saying fire your SEO team. Organic search still drives enormous volume, and it will for years. But the growth is happening elsewhere, and the skills that make someone great at SEO - analytical thinking, structured data expertise, understanding how algorithms evaluate content - translate well to ACO work.
The smartest move for most organizations is to expand the SEO team's mandate. Give them ownership of AI visibility alongside search visibility. The data infrastructure work (Schema.org, product feeds, structured content) is similar enough that the same team can own both. The strategic thinking is different, but SEO professionals who've navigated algorithm updates for a decade tend to adapt quickly.
What you can't afford to do is wait. The 93%+ zero-click rate in AI Mode means the window where traditional SEO carries the full load is closing. The brands that build ACO capabilities now will have compounding advantages as AI commerce scales. The brands that wait will find themselves in the same position as companies that ignored SEO in 2005 - playing catchup in a game that others already won.
Frequently Asked Questions
What is Agentic Commerce Optimization (ACO)?
ACO is the practice of optimizing product data, feeds, and digital presence so AI shopping agents like ChatGPT, Google AI Mode, and Perplexity recommend your products. It extends traditional SEO to cover AI-driven commerce channels where the majority of searches now end without a click to any website.
How is ACO different from SEO?
SEO optimizes for search engine rankings and click-through rates. ACO optimizes for AI agent recommendations and product selection. While both rely on structured data, ACO requires deeper product information - use cases, comparison attributes, conversational-query matching - because AI agents evaluate products differently than search crawlers.
Do I need a separate team for ACO?
Not necessarily. The skills that make someone great at SEO (structured data, analytical thinking, algorithm adaptation) translate well to ACO. Most organizations can expand their existing SEO team's mandate to include AI visibility. The key is starting now while the discipline is still emerging.
Which AI shopping platforms should I prioritize?
Focus on the three with the largest user bases: ChatGPT (900 million weekly active users), Google AI Mode (integrated into the dominant search engine), and Amazon Rufus (300 million+ users). Perplexity is smaller but growing quickly and worth monitoring. Make sure your robots.txt isn't blocking any of their crawlers.
Dor Shany is the CEO of Paz.ai, an agentic commerce platform that helps retailers sell through AI shopping agents. This article reflects his analysis of publicly available information. More at paz.ai/blog.
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