AEO for Ecommerce: Your Playbook for the AI Shopping Era

AEO is the practice of optimizing digital content to be chosen and delivered as the definitive response by AI-powered search engines, voice assistants, and chatbots.

Rather than a shopper finding your product in search engine results pages (SERPs, your brand’s website, a marketplace, a retailer, or a social media platform, AEO enables large language models (LLMs) to recommend your products directly in chat interfaces.

“When I say … SEO, AEO, GEO, [we’re looking at] all those acronyms with the same intent, and that is increasing visibility, increasing how many times we're cited and recommended by the agents,” Marilia Moreira, digital marketing director at Whirlpool Corporation, shared in an episode of the “Unpacking the Digital Shelf” podcast, “Running a Spin Cycle of Content for the Digital and Agentic Shelf.”

But how do AEO, SEO, and GEO differ?

The ultimate goals of SEO, GEO, and AEO interrelate — that is, optimizing product content so the right shoppers find your products (which is still important, even in this new era of shopping).

AEO for ecommerce is when brands become the response when shoppers consult AI-powered search engines, voice assistants, and chatbots for product recommendations that meet their unique needs. AI-referred retail traffic is up 393% year over year (YoY), according to analysis by Adobe, and AI traffic sources have 42% higher conversion rates than non-AI traffic.

AEO matters for ecommerce because it helps brands become the response in chat interfaces. Becoming the response means becoming the purchase. Becoming the purchase means fostering brand loyalty and building trust.

AI is increasingly used as a shopping assistant, as these agents can sort through enormous amounts of data — in this case, product data — cross-reference customer reviews, compare similar products, and present personalized recommendations for shoppers. As a result, shopping is increasingly becoming contextual thanks to this technology, saving shoppers time and effort in finding the best option.

According to Salsify’s “2026 Consumer Research” report, one of the top reasons shoppers prefer online shopping in general is convenience (64%). The top reasons they’d use an AI agent are similar: time savings (32%) and convenience (32%). AEO also matters for ecommerce because it allows smaller, newer, or digitally native brands and retailers to build awareness and compete with larger legacy options.

If textual and visual product data is machine-readable, correct across channels, and discussed favorably online, brands have a greater chance of being recommended and winning the sale.

AEO and SEO differ slightly across their focus, primary goal, and success metrics. With AEO, the focus is on product content being read and recommended by answer engines and AI shopping assistants. With SEO, the focus is on product listings showing up in traditional search engines to drive traffic and sales.

To succeed at AEO, brands must refine content for completeness and correctness — and expand how content is structured to match increasingly nuanced, contextual conversations and Q&As shoppers have with AI agents.

To succeed at SEO, a brand must optimize product content to include relevant keywords and search phrases and be referenced favorably across different sites to rank and build authority. From there, brands can hope to be clicked on and win the sale.

Traditional SEO drives clicks to a page, while AEO optimizes data so LLMs can recommend your products directly in chat interfaces.

“The question is not, ‘What do I eat?’” Molly Schonthal, managing director of agentic commerce transformation for VML/WPP Enterprise Solutions, shared at a 2026 Digital Shelf Summit session. “No one's asking an agent, ‘What do I eat?’ It's a complex question. It starts with an opening about who I am or the situation that I'm in.” 

Shopping for Running Shoes: An SEO, GEO, and AEO Example

Here are SEO, GEO, and AEO in practice for a shopper searching for women’s running shoes for wide feet.

• SEO: A shopper types in simple keywords, “women’s running shoes wide,” and the best-ranking brands and product listings are shown.

• GEO: A shopper asks an AI shopping assistant or chatbot about the shoes and gets personalized recommendations. The AI assistant can recommend three top options based on ratings and reviews it scraped and a shopper’s presumed preferences, such as color or past brands of interest.

• AEO: A shopper can get more specific in their prompts to the agent. Based on the shopper’s preferences and the agent’s contextual search capabilities, the shopper can ask, “What’s the best running shoe for me if I have wide feet and want to run on the beach next week?” The agent can even find an available option and purchase it on the shopper’s behalf so they’re ready to go.

The buyer’s journey hasn’t been linear for ages: Physical bricks turned into digital clicks, and now, shoppers are clicking (in a different way) with AI shopping assistants and agents.

But why did this happen? What makes AI so great for shopping?

Artificial intelligence is any computer system that can perform tasks normally requiring human intelligence, such as learning, reasoning, and problem-solving. With these capabilities, AI chatbots were developed, adding machine learning (ML) and natural language processing (NLP) to communicate and provide answers.

Beginning as rigid, rule-based customer service chatbots that fielded simple questions, AI shopping assistants have developed into what we know now thanks to the introduction of generative AI (GenAI), or more advanced AI that uses human inputs, data, or queries to create content.

AI shopping assistants, now more conversational, can help shoppers in the buying journey — primarily in the finding/discovery or research/consideration phase — to help them choose the best product or service that meets their needs. These agents act as a concierge, providing fast, personalized results based on prompts entered or questions asked by a shopper.

“When faced with infinite options, finding the right one — the best product, the perfect gift, the trusted solution — can feel overwhelming. AI brings clarity to that complexity. It has the power to make shopping more intuitive, more helpful, and more human again,” says Rob Gonzalez, Salsify co-founder, in his whitepaper, “The PXM AI Manifesto.”

How Did Agentic Commerce Develop?

Search engines on the digital shelf drive clicks to complete a purchase. The development of GenAI and autonomous agent technology turns conversation into commerce (agentic commerce).

Agentic commerce is possible due to agents’ ability to take further action on a shopper’s behalf without constant intervention. Agents collect data from shoppers’ prompts, then synthesize it with product information they crawl across the digital shelf. The result? They find, research, and even purchase the best option that meets the shopper’s needs.

“Now, search engines exercise agency and infer unasked questions,” according to Forrester. “They anticipate intent, interpret natural language, and guide users on complex, click-free journeys.”

Though this technology requires shoppers to give up some control, comfort and trust in agentic commerce is already building:

• 51% of shoppers are comfortable with agentic commerce, or an AI shopping agent automatically discovering, researching, and purchasing gifts, according to Salsify’s “2026 Holiday Pulse Report”

• 48% of consumers already trust an agent to recommend products on their behalf, according to research from VML Intelligence.

On the flip side, however, some shoppers are less enthusiastic:

• 55% of shoppers aren’t comfortable with AI agents making purchases on their behalf, according to the “Agentic Commerce Pulse” survey from fraud protection platform Riskified.

• 34% of shoppers shared they believe AI will make gift-giving more efficient, but less meaningful, according to Salsify research.

What’s the Difference Between the Physical, Digital, and Agentic Shelf?

The interrelated system of physical, digital, and agentic touchpoints has created a new omnichannel reality for brands — a three-shelf reality that consistency of product content and brand presence must span.

"When ecommerce and the digital shelf started becoming a thing, it's not like you could do that instead of physical retail,” says Rob Gonzalez, Salsify co-founder. “You had to do physical retail and [the] digital shelf. And now you've got to do the physical shelf, the digital shelf, and the agentic shelf."
Again, it’s not a matter of excelling on one shelf to win in this new era: Not all shoppers will participate in just one channel, but rather, a mix. 

“There seems to be this belief amongst AI maximalists that agentic commerce will somehow take over all other forms of commerce,” Gonzalez told Tech Bullion. “This is nonsense. It’ll just be another way to shop. Sometimes it will make sense, sometimes it won’t."

Brands must learn how to adapt and evolve to succeed on all three shelves with product and brand content optimization tactics and a combination of SEO, GEO, and AEO.

AI shopping assistants find and analyze product information from readable sources across the digital shelf.

“At the heart of any AI agent is product data,” Yana Kaszubska, ecommerce lead for Southern Glazer’s Wine & Spirits, shared at a 2026 Digital Shelf Summit session. “We can't escape this. We have to make sure that this is not only accurate, but it's also complete.”

It’s a combination of technical, structured product data like meta descriptions and unstructured product data like reviews that provide color and additional context needed to win the citation or recommendation.

According to research by AI search and AEO platform AirOps, approximately 83% of citations come from pages updated within the last year.

But where exactly should brands fan out their updating and AEO optimization efforts for ecommerce?

• Merchant centers: These are digital dashboards that enable brands to curate listings, advertise, directly integrate their product catalog, and even receive performance insights. Examples related to AI shopping assistants include Google Merchant Center, Commerce Manager (Meta), OpenAI / ChatGPT Connectors & Crawlers, Perplexity Merchant/Sourced Feeds, and Claude Search Integration.
• Product schema: This content can enable brands and products to show up in AI overviews. It should include attributes like the product name, price and currency, availability, aggregate rating, and brand/SKU.
• Product feeds: These are based on structured data that AI shopping assistants love. They should include correct product IDs/SKUs, clear titles and descriptions, pricing and availability details, images and media, and other attributes. Common file formats can include XML, CSV/TXT, and JSON.
• Brand websites: These .com, direct-to-consumer (D2C) sites like Levi’s or Nike are prime places for shopping assistants to pull information since they should be relevant and authoritative, as it comes directly from the brand. As long as crawling is enabled on data, AI search engines and shopping assistants will read, analyze, and use this product information to provide shoppers with recommendations based on their preferences.
• Retailers: AI shopping assistants get product information from multiple retailers like Target and Walmart. This can get tricky, however, if brands don’t monitor how they’re being presented on larger or third-party retailers. Discrepancies can result in lost citations, recommendations, and sales.
• Reviews: AI shopping assistants (especially LLMs) love organic, human discussion to help them understand how to converse and answer queries reliably and within context. Reviews found on a brand.com site, marketplaces and merchants, third-party sites, social media platforms, and so on, provide fuel for agents to recommend your listing (or not). Beyond the agent, shoppers (33%) prefer online shopping over in-store shopping because of access to reviews, according to Salsify research.
• Editorial content: This type of content enriches the context in which products can be found, which is important information for AI shopping assistants to crawl to best answer shopper queries. For example, editorials might discuss the “best guest dresses and outfits for hot summer weddings,” or the “most durable, kid-friendly umbrellas.”
• Community discussions: Community discussions can include forums like Reddit or other social platforms. According to AirOps research, 48% of citations come from community platforms like Reddit and YouTube, and 85% of brand mentions originate from third-party pages rather than owned domains.
• Manufacturer specifications: This product information includes materials, components, assembly steps, and quality standards required to build a product and can provide important context to answer shoppers’ questions. For example, “Help me find sustainable wood bookcases with easy assembly.”
• Pricing feeds: Pricing feeds are structured data streams that deliver real-time or bulk price information for different products. Especially in volatile market conditions, affordable options are important to shoppers. In fact, Salsify research showed the top reason shoppers (30%) would want an AI shopping assistant is to help them find the best deal.
• Inventory feeds: Inventory feeds are structured data streams that deliver real-time stock information for ecommerce products. Product availability is crucial to winning recommendations from AI shopping assistants, conversions, as well as building trust and loyalty.

The AI Trust Gap

According to Capital One Shopping, “63% of consumers want to shop with help from generative AI.” As previously mentioned, Salsify’s research shows more than half of shoppers are comfortable with agentic commerce in the context of purchasing gifts. The percentage of shoppers using

AI chatbots and shopping assistants for gift ideas alone nearly doubled since last year.

But increased adoption, conversions, even enthusiasm don’t always guarantee trust.

As discussed in Salsify’s “2026 Consumer Research” report, just 14% of shoppers trust AI recommendations alone to make a purchase, and at least 27% say they trust AI but verify recommendations with other sources. And according to Salsify’s “Mastering the Agentic Shelf” playbook, up to 95% of shoppers verify AI suggestions on a product detail page (PDP) before buying.

This is the AI trust gap.

As AI shopping assistants and agents need complete, correct, structured content to make recommendations to consumers, consumers need AI results with the following to feel confident enough to convert, according to Salsify research:

• Most important: Detailed product description and specifications (31%)
• Important: Brand recognition (27%)
• Important: Personalized explanation for why it fits my needs (25%)
• Important: Customer reviews or ratings (25%)
• Less important: Product comparisons of similar products (21%)

Therefore, satisfying AI agents with enough high-quality content to win the recommendation can also easily win the sale with shoppers — if they come across your content via a chat window, traditional search, on social media, or somewhere else on the digital shelf.

However, there’s also another trust gap brewing that isn’t being talked about as much: Just how much AI agents are allowed to do, and how much agreement there is between this technology and retailers, for example.

“There’s also trust between the agent and the retailer that I don’t think has been talked about enough, but is starting to be talked about. So the question is, ‘What is the agent allowed to do?’” shared Collin Colburn, VP, commerce and retail media for IAB, in an episode of the “Unpacking the Digital Shelf” podcast, “The Shopper Is REALLY in Control Now.”

For agentic shopping to truly go mainstream, brands and retailers will need to create processes, workflows, and ecommerce governance that foster greater transparency and accountability.

Modern AI search engines can find, compare, and analyze enormous amounts of data and synthesize it into a concise answer — and in the context of shopping and ecommerce products, they’re getting better at it.

Generative engines and AI shopping assistants look for human-written content that demonstrates knowledge of the subject matter. Confident and factual content is the most valuable resource.

EEAT: Experience, Expertise, Authoritativeness, Trustworthiness

EEAT — experience, expertise, authoritativeness, and trustworthiness — are all critical components of effective GEO and AEO.

These components all tie together: Experience means familiarity and longevity, expertise means specific knowledge, authoritativeness means experience and expertise, and trustworthiness means reliability and repeatability of all of these factors.

“You need to be prepared for trust signals,” says Swagat Choudhury, global digital commerce director of Perfect Digital Store, for Mars. “Machines love trust signals.”

For example, a shopper searches for breathable workout clothing in stock for summer that’s on sale: an agent determines that natural fabrics are more breathable than synthetic ones, checks availability with different brands, looks for discounts, and makes a recommendation. Or, more specifically, a shopper asks: “Is X a good brand for workout shorts?” wherein the agent again cross-checks for trust signals based on the brand’s product data and reputation.

So the evaluation is multifaceted: It’s well-structured and machine-oriented, but it’s also human voices and input.

Forrester’s vision report, “Turn AEO in Brand Growth,” illustrates AI shopping assistants' relationship to human voices well: “Social media’s aggregation of FAQ-style conversations featuring vulnerable, authentic voices — rather than brand and product marketing speak — makes it highly visible across answer engines. Viral narratives supply the language, examples, and sentiment answer engines rely on, making social media a primary influence on AEO outcomes.”

Product Content Checklist: How To Win AI Shopping Assistant Recommendations

AI shopping assistants aren’t loyal to brands; they’re loyal to shoppers. To win the agent recommendation (and hopefully the sale), your product content must achieve the following.

• Structured data: Information organized in a specific format. Structured data has a predefined schema and is machine-readable for efficient analysis, like metadata or alt text with details such as a product’s size, weight, material, etc. AI can help AI, too, as some product experience management (PXM) tools include automation for structured data like alt text.
• Relevance: Your content must answer the questions shoppers ask AI agents.
• Completeness: This product data is properly structured and populated for every agent. Gonzalez says brands should ensure “every single product [is] completely filled out, completely accurately, everywhere.”
• Consistency: Content and product data match from channel to channel; you can detect and correct AI hallucinations at scale.
• Freshness: Product content is timely and appropriate for the season or shopping moment someone finds themselves in. It’s up-to-date against your product catalog and compliant with retailers.
• Authority: Demonstrate experience and expertise through thoughtful, accurate product and brand content. Be sure to include detailed Q&As and FAQs about your products from brand professionals. Have authentic conversations with consumers about what you offer and respond to complaints and comments.
• Reviews: As mentioned, AI shopping assistants thrive on real (human) user-generated content (UGC) to determine a brand’s authority and trustworthiness and help them make reliable recommendations. AI shopping assistants can find and scrape reviews from a variety of channels across the web.
• Availability: Keep products in stock. If your products are consistently unavailable, shoppers won’t view you favorably, and agents will have no reason to recommend you.

“To win, brands must design content for AI discovery, not just search,” Gemma Spence, CEO of VML / WPP Luxembourg, shared in the “The PXM AI Manifesto” whitepaper. “That means new collaboration across marketing, data, and creative teams, and a relentless focus on predictive readiness — be findable, preferable, and trustworthy wherever the shopper’s journey begins.”

Completeness, context, citations, correctness, and customer acquisition — those are five C words for ecommerce leaders to prioritize in the era of AI shopping, according to a framework from The Digital Shelf Institute (DSI) and Azoma.

It’s just 5 Cs and four steps to AEO excellence.

Step 1: Optimize Product Feeds for AI Extraction

Generative AI engines like ChatGPT and Google Gemini pull heavily from Google Merchant Center and structured product feeds, rather than just scraping HTML. Rich, descriptive titles — that still follow retailer requirements — and highly accurate attributes are critical.

Step 2: Implement Perfect Schema Markup

Schema is the language of answer engines. The different types of schema you need on PDPs include product information, FAQ and Q&A pages, and reviews.

Step 3: Write Answer-First Content and FAQs

Burying the answer in a 2,000-word post no longer works. Advocate for structured Q&A and FAQ sections on category and product pages that address buyer pain points directly.

Step 4: Ensure Data Consistency Across the Digital Shelf

AI models aggregate data from multiple sources. If your pricing or product specs are inconsistent across retailers, AI loses trust and drops your product from its recommendations.

With new processes to implement, teammates to rally, and more data to wrangle and optimize than ever, mistakes are likely. However, mistakes — such as a lack of relevance or context — can make or break whether your brand gets discovered by AI shopping assistants at all.

Starting is perhaps the most important part: “First-mover advantage isn’t theoretical; it’s algorithmic,” writes Gonzalez. “The faster you can surface, adapt, and optimize product information across channels, the more likely you are to be featured, recommended, and purchased by humans and agents alike.”

But winning wherever shoppers are ready to buy doesn’t require brands to entirely abandon their digital shelf strategy — or chase every shiny new AI optimization trick that appears.

While working through adjustments in your processes or evaluating your tech stack, watch out for some common (costly) AEO mistakes:

• Missing schema: Missing structured data means less for crawlers to read or interpret, or a reason for them to forgo the citation.

• Thin product descriptions: Without basic details, your listings won’t satisfy SEO or GEO in the first place. Shoppers and agents alike won’t pay any mind or won’t be able to find your listing, either.

• Duplicate manufacturer copy: Don’t say the same thing twice. Though there’s an estimated 5x increase in the content needed to keep pace with consumer agentic behaviors according to Adobe, make the data count.

• Inconsistent specs: Inconsistency results in a lack of trust and fewer citations. If human shoppers can’t determine what’s true, AI can’t either. 

• Broken feeds: If data can’t be fed to agents, it won’t power your brand’s digital shelf or agentic shelf presence. 

• Missing GTINs: Global Trade Item Numbers (GTINs) are internationally recognized ID codes and help verify items priced, ordered, or invoiced in a supply chain. Without them, online marketplaces like Amazon or Google Shopping can’t list items correctly, and agents won’t be able to track sales of physical products in digital databases.

• No FAQs: AI needs content to learn from and relay to shoppers; it thrives off of a Q&A structure, since that’s the basis of its chat interface.

• Hidden answers: Don’t bury the lead in unreadable data. If AI agents can’t find an answer, shoppers probably can’t either.

• Outdated pricing: This makes your brand seem unreliable and untrustworthy. It can cause cart abandonment and cause crawlers to turn away.

• Conflicting retailer information: Monitor your channels for accuracy and consistency. Agents can sniff out discrepancies and won’t recommend you.

“Keep doing the basics brilliantly,” Gonzalez says. “They’re going to help you in existing ecommerce no matter what — it’s a no-lose move.”

To succeed in the era of AI commerce, your product experience management (PXM) solution must include powerful product information management (PIM) capabilities to satisfy the extreme volume of complete, correct data agents need.

“The job of PXM, the job of people who are working on the digital shelf, is to make every product experience matter,” Gonzalez says.

However, the brands that will win as this era evolves must have a scalable, repeatable plan in place with an intelligent PXM solution that enables them to:

1. Centralize and create: Organize data into one central source of truth to generate compelling content across all PDPs, including structured Q&A and use case content that aligns with how AI engines answer.
   

2. Syndicate and publish: Ensure data reaches every channel, retailer, and every AI-readable source; not just one, but all.
   

3. Measure and optimize with automation: Track traffic from traditional sources, bots, and brand mentions across top AI engines and search engines.
   

Intelligent PXM means automation and AI-powered tools with humans kept in the loop.

“Intelligence alone isn't enough,” says Julie Marobella, chief product officer at Salsify. “You can't have a brain without a body. So, through Angie, Intelligence Suite, PXM agents, and workflows, we're turning intelligence into action. We're creating a system that doesn't just inform your work, but it transforms how your work gets done.”

Centralize and Create Use Case and Context-Rich Content

Shoppers need a variety of great content, while AI engines index Q&A pairs and structured descriptions above all other content types.

• Ensure all PDPs have complete, correct, compelling content pulled from a single source of truth.
  

• Generate context-rich data for conversational search.
  

• Extract product insights and automate optimized answers.
  

Publish Wherever Eyes (and AIs) Can See It

Maximize global visibility — ensure wherever human shoppers and  AI can find you, they do.

• Syndicate to all your desired channels while meeting retailer requirements with a tool connected — and powerful enough — to scale with you.
  

• Leverage intuitive catalog solutions that empower your brand to be indexed instantly by AI with one switch of a toggle (llms.txt, markdown, etc.).
  

• Enable AI crawlers to reach your brand.com and D2C site data (Shopify, Salesforce).
  

• Be part of AI and retail channels by becoming an OpenAI/Google Merchant and feeding content directly to AI assistants, like Amazon’s Alexa for Shopping.
  

Measure and Optimize With Automation for Now and Later

Drive performance by measuring for maximized reach, accuracy, and impact while leveraging automation to boost efficiency and save time.

• Determine if content is reaching the right audiences and AI crawlers.
  

• Confirm that your content is being represented accurately.
  

• Ensure your content is driving traffic and conversions.
  

• Use workflows to automatically ensure content remains compliant, up-to-date, and overall, killer.
  

“Both AI and humans are at their best when they’re working together,” Gonzalez says. “This 'copilot' model enables humans to innovate, create, and supervise, while positioning AI as a tool for doing more and doing it better.” 

When evaluating your own product content — before the agents even get to it — here’s a checklist for ecommerce leaders to consider. Evaluate each of these facets through the lens of completeness, correctness, relevance, and EEAT.

• Product feeds: How accessible is your product information? Does it flow from a central source of truth?

• Schema: Are you able to keep your product information up to date?

• FAQs: Are you answering new and outstanding questions about your products thoroughly and thoughtfully? Are you keeping tabs on what’s happening in your vertical/industry?

• Reviews: How effective is your brand at collecting and implementing feedback from reviews? Are they public? Are there more platforms where you can gather data or interact with your customers?

• Merchant Center: Are these platforms able to access your product information efficiently and effectively?

• Consistent specs: Do you have discrepancies across retailers or different channels? How quickly are you able to find and fix errors? Is it a manual process, or is it automated?

• Accurate inventory: How well can you keep up with product demand — and convey this to your audiences?

• Internal linking: Is your product and brand content mapping/organization effective on your site internally and externally? Across other channels that you publish to?

• Rich product attributes: Do you expand on what your product offers beyond the basics? Do you talk about product compatibility or how it might compare to other options? Who the product is right for?

• Data governance: How well can your teams manage your product experiences? Are you set up with PXM technology that’s intelligent and powerful enough to scale in the era of AI shopping?

What is AEO for ecommerce?

AEO is the practice of optimizing digital content to be chosen and delivered as the definitive response by AI-powered search engines, voice assistants, and chatbots. AEO for ecommerce is when brands become the response when shoppers consult AI-powered search engines, voice assistants, and chatbots for product recommendations that meet their unique needs.

Why is AEO important for ecommerce brands?

AEO is important for ecommerce brands because of the increasing use and capabilities of AI as a shopping assistant. AI shopping assistants and agents can sort through enormous amounts of data — in this case, product data — cross-reference customer reviews, compare against similar products, and present personalized recommendations for shoppers.

Brands can’t afford to ignore that shopping is becoming increasingly contextual thanks to this technology. Shoppers are seeking the best product meeting the specific needs of the moment. Since shoppers can discuss these needs with their very own agent capable of finding, researching, and even purchasing on their behalf, this practice can save time, effort, and potentially money that traditional search or shopping experiences might require.

According to Salsify’s “2026 Consumer Research” report, the top reasons shoppers prefer online shopping in general are better pricing (65%) and convenience (64%). The top reasons they’d use an AI agent are similar: time savings (32%) and convenience (32%).

AEO also presents an opportunity for smaller, newer, or digitally native brands and retailers to compete with larger legacy options. If textual and visual product data is machine-readable, correct across channels, and has evidence from reviews of being well-liked across the web, brands have a great chance of being recommended and winning the sale.

Is AEO replacing traditional SEO?

AEO is not replacing traditional SEO; it’s an extension of SEO’s search principles in the AI era. If anything, SEO is the foundation for both GEO and AEO. Traditional search optimization models and tactics will continue to play a critical role in ensuring content can be found and trusted in the first place. AEO simply extends those same principles into how information is organized, interpreted, and surfaced when AI systems generate answers directly within the search experience.

What's the difference between SEO and AEO?

With SEO, a brand must optimize its product content to include relevant keywords and search phrases, as well as be referenced favorably on different sites to rank and build authority. From there, brands can hope to be clicked on and win the sale. 

With AEO, brands must refine their content for total completeness and correctness, and expand how they structure content to match increasingly nuanced contextual conversations shoppers have with AI agents. 

Unlike traditional search, these agents can synthesize information from several sources, interpret what the shopper actually needs, and recommend a short list of options outright; this makes showing up as a recommendation more competitive, but a sale more likely.

What's the difference between GEO and AEO?

GEO gets your product data to be the source material for an AI recommendation. If your product specs, features, and brand narrative are strong (i.e., complete, consistent, and compelling), it will be pulled into a generated summary when an AI engine builds a curated response for a shopper. 

AEO gets your product content to be the clear answer to a specific buying question. Ideally, your metadata is structured so an answer engine can understand it immediately and serve it up without hesitation. 

The methods for achieving GEO and AEO are similar: Content must be complete, correct, and increasingly detailed so AI assistants and agents can not only find but confidently recommend your listing.

What is artificial intelligence optimization (AIO)?

Artificial intelligence optimization (AIO) is related to answer engine optimization (AEO). Not all AI is an answer engine necessarily, but all answer engines are understood to be driven by artificial intelligence. Therefore, AIO and AEO have most of the same requirements. SEO, GEO, and AEO are more understandably interconnected and related — perhaps a reason why the acronym AEO is more prominent.

What is agentic commerce?

Agentic commerce is shopping that occurs on the agentic shelf — fueled by the digital shelf — wherein autonomous or semi-autonomous AI agents discover, compare, and purchase products on a consumer's behalf. 

These agents rely heavily on structured data and robust contextual information about a product’s specific uses, considerations, compatibility, etc., so they can not only find your listing, but recommend it to a shopper with confidence.

More than half of shoppers are already comfortable with agentic commerce, according to Salsify research.

What is an AI shopping assistant?

An AI shopping assistant is a search engine powered by artificial intelligence that helps shoppers in the buying journey — primarily in the finding/discovery or research/consideration phase — to help them choose the best product or service that meets their needs. It acts as a concierge providing fast, personalized results based on prompts entered or questions asked by a shopper.

How do AI search engines find ecommerce products?

AI search engines find ecommerce products by crawling product content and data across the web — including text and visual content on brand sites, marketplaces and retailers, social media, review sites, editorial content, merchant centers, and more — to find personalized results for a shopper’s queries. 

The product content they crawl and evaluate includes structured and technical data like size and material as well as unstructured data like customer feedback. AI search engines can analyze this data from site to site to check for inaccuracies, validity, and compatibility with the shopper’s preferences, as well as compare the brand or product against competitors or related options. 

Ultimately, AI search and generative engines evaluate product and brand data for relevance and EEAT — experience, expertise, authority, and trustworthiness — and will recommend the highest-scoring options to the shopper.

Where do AI assistants get product information?

AI shopping assistants find and analyze product information from readable sources across the digital shelf. Because of the complex nature of shoppers’ searches, they need a wide variety of product information to recommend brands effectively.

Product information is pulled from merchant centers (related to Google, Meta, OpenAI, Perplexity, Claude), product schema, product feeds (XML, CSV/TXT, and JSON), brand websites, retailers, reviews, editorial content, community discussions (Reddit), manufacturer specifications, pricing feeds, and inventory feeds.

How do large language models evaluate product data?

A large language model (LLM) is an AI that can rapidly analyze large datasets, such as product data, to understand and generate human language. Shoppers can interact and converse with an LLM like a personal shopper, but with access to the entirety of the digital shelf, not just knowledge of one store. 

The LLM can find, analyze, and synthesize front- and back-end product data to provide the best (or at least most personalized) recommendation for the shopper’s search based on past browsing data or preferences.

Why do some products appear in AI recommendations while others don't?

Some products appear in AI recommendations while others don’t based on the completeness and breadth of their structured data, like product schema, and unstructured data, like reviews. AI shopping assistants evaluate product and brand content for relevance and EEAT — experience, expertise, authority, and trustworthiness — to see if it fits the context of the shopper’s question. 

If brands lack these parameters, or are found to have inconsistencies in their data, missing data, outdated data, or so on, they won’t be recommended.

Can AI compare products across multiple retailers?

Yes, AI can compare products across multiple retailers. As long as crawling is enabled on data, AI search engines and shopping assistants will read, analyze, and use this product information to provide shoppers with recommendations based on what they’re looking for and their preferences. Discrepancies in product information across retailers, however, will likely result in a brand or product not being recommended.

What makes content "answer-first"?

Content is answer-first if it provides direct, complete information in the first few words or opening sentences before background information, supporting examples, or narratives are given. Benefits of answer-first content for the audience (either human or agent) include that it saves time, it’s scannable/readable, and it’s easy to determine if the source or additional information is relevant and reliable.

What happens when product information is inconsistent across retailers?

Inconsistent product information across retailers costs brands sales and can damage their reputation, no matter how shoppers discover their product or if they’re shopping on the physical, digital, or agentic shelf.

In the case of agentic commerce, agents flag any inconsistent product information across retailers; they then deem the information incorrect or unreliable and not worth recommending. (Though agents are capable of serving inaccurate information about a brand to shoppers, i.e., hallucination, they’re capable of differentiating conflicting information about a brand from one site to the next.)

Other product data inconsistencies can cost brands recommendations and sales, too, such as if an agent finds outdated or misleading product information about a brand’s products based on consumer feedback it finds on the web. The same can apply if shoppers discover these same warning signs about brands when they browse products without using an agent.

In traditional search or ecommerce, 34% of shoppers have abandoned a sale due to incomplete or poorly written product details, and 38% have opted out of a purchase due to inconsistent product data across websites, according to Salsify research.

What is the agentic shelf?

The agentic shelf represents a digital shopping experience — and touchpoints — where autonomous, AI-powered agents and search engines find, browse, and purchase products on behalf of a consumer.

The digital shelf fuels the agentic shelf — sometimes called “the invisible shelf,” because shoppers can’t physically see the mechanisms of AI agents analyzing their interests to make decisions on their behalf.

This is what’s different about the agentic shelf versus the physical or digital shelf: AI agents can make decisions — not just human shoppers. Agents can restock inventory, make purchases, and generate offers based on customer interests. 

How long does it take to see results from AEO initiatives?

AEO moves faster than traditional SEO because AI engines can quickly crawl and extract structured facts. So, rather than a six-to-12-month timeline with SEO initiatives, AEO initiatives can produce results far sooner: signals in weeks, citations in a month or a few months, and, if sustainable, stable visibility thereafter.

How does product experience management (PXM) support AEO?

“Agentic AI makes PXM the connective tissue between brands, technology, and consumers,” says Gemma Spence, chief digital commerce officer, VML/WPP. 

Product experience management (PXM) supports AEO because PXM tools and principles set brands and their product data up for success through centralizing, creating, publishing, measuring, and optimizing structured and unstructured data.

Data is organized into a central source of truth with PIM and data asset management (DAM) for completeness and correctness, while further content is created or generated; data is syndicated to appropriate channels to be crawled, demonstrate experience, build authority, and win citations; and, finally, data is measured and optimized to build out further context and support further customer acquisition.