From SEO to AEO and GEO: Ignore this shift and risk going invisible

New Delhi: As AI assistants and AI-led search experiences begin shaping what consumers see, compare and buy online, marketers are being pushed beyond traditional SEO to newer playbooks such as Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), with the focus shifting from driving clicks to influencing AI-generated answers and recommendations.

The shift has been outlined in a new guide by Microsoft Advertising titled From discovery to influence: A guide to AEO and GEO, which argued that visibility is increasingly determined inside LLM-powered environments where assistants and generative search engines summarise information, surface product lists, and make recommendations.

The guide defined Answer or Agentic Engine Optimisation (AEO) as optimising content so AI assistants and agents can find, interpret and present answers accurately. It defines Generative Engine Optimisation (GEO) as optimising brand and product content for generative AI search environments so it is discoverable, trustworthy and authoritative.

It added that while SEO remains foundational, brands now need to ensure their catalogues, product pages and site architecture are machine-readable and consistently enriched, because AI systems are increasingly consuming structured data, feeds, APIs, reviews and live website signals to decide what to recommend.

The guide flagged that the change has implications across leadership functions. It said CMOs need to ensure AI systems correctly understand and elevate brand differentiators, while growth and performance leaders must adapt to AI-led journeys. 

It added that digital and e-commerce leaders should plan new measurement approaches for AI-intermediated journeys, CTOs must make the stack AI-readable and AI-accessible, and data and analytics leaders need strategies for early-funnel research that is increasingly happening inside AI conversations and is not always visible through conventional tracking.

The report described an evolving AI shopping ecosystem where AI browsers, assistants and agents overlap. It said AI browsers can interpret what a user is viewing in real time and surface contextual information while browsing. AI assistants support conversational discovery and decision-making, while AI agents can act end-to-end, including navigating websites, filling forms, clicking buttons and completing purchases.

In this environment, the guide argued, the key question for retailers is not which AI “category” will dominate but what data and content the AI can access and trust. It lists product feeds, structured markup, inventory and pricing APIs, reviews and images as critical inputs, and stresses that these must be accurate, comprehensive and current.

It outlines how AI systems interpret shopping queries through a reasoning phase, pulling from sources such as knowledge graphs, real-time web search, product databases, pre-trained knowledge and contextual signals. It also lists relevance and ranking inputs, including freshness, text relevance, commercial signals and contextual relevance, alongside page-level data and user information such as location, sizing and brand affinity.

To explain how that plays out, the guide uses an example of a consumer asking an assistant for a rain jacket recommendation under a price threshold. It said crawled web data shapes category understanding and baseline brand perception, while product feeds contribute current pricing, availability and key specs, influencing whether a product appears among the final recommendations.

The guide said retailers need to manage visibility across three key “data surfaces”: crawled data, product feeds and APIs, and live website data. It describes crawled data as information learned during training or retrieved from indexed web pages that grounds AI responses. Product feeds and APIs are positioned as structured data retailers actively pushing to platforms, giving more control over representation in comparisons and recommendations. Live website data, it adds, is what AI agents see on-site in real time, including rich media, reviews, dynamic pricing and transaction readiness.

It also stressed that traditional SEO remains essential because AI systems perform real-time web searches frequently throughout the shopping journey, not only at the final purchase stage. In that context, it said a retailer’s site still needs to rank well to be discovered, evaluated and recommended by AI systems.

On commerce readiness, the guide warned that even if crawled data and feeds are accurate, conversions can fail if the live website cannot support an AI-led purchase flow. It noted that AI agents may be able to add products to cart, apply promo codes, calculate shipping, complete purchases using saved payments and provide confirmations and tracking, but the journey can break if the site experience is not functioning properly.

The report mentioned three broad action areas for retailers: data structure and consistency, intent-driven content enrichment and trust signals.

On technical foundations, it recommended making catalogues machine-readable and ensuring dynamic fields such as price, availability, colour, size, SKU, GTIN and dateModified are available and consistently updated. 

It recommends ItemList markup for collections and category pages so AI systems understand groupings, and flags localisation fields such as inLanguage and priceCurrency for multi-region operations. It also lists schema types, including Product, Offer, AggregateRating, Review, Brand, ItemList and FAQ.

It also called for real-time synchronisation across feeds and on-site schema, including exposing dateModified and availability, providing start and end dates for promotions, keeping values consistent across feeds and site schema, ensuring the rendered DOM contains the same facts users see, and syncing price and inventory between feeds and on-site structured data.

On content enrichment, the guide said AI assistants interpret queries as intents and advises brands to structure content to answer real-world questions directly. It recommended front-loading product descriptions with benefits, adding use-case context, and writing titles that pair product names with differentiators. 

It also pushed modular, citable content such as Q&A blocks, specs in key-value formats, feature lists, comparison tables and “goes well with” data for complementary products.

It further highlighted multi-modal signals, recommending video transcripts that clearly describe features, detailed alt text supported by ImageObject schema, and ensuring mobile and voice experiences expose the same structured data and facts as desktop.

On trust signals, the guide said AI systems prioritise reliable sources. It recommends verified reviews tagged with Review and AggregateRating schema, surfacing review volumes and verified purchase ratios, and highlighting review sentiment that can support natural-language recommendations. 

It also pointed to authoritative brand identity signals such as links to expert reviews and articles, certifications and sustainability badges expressed as factual entities, and brand identifiers linked to official social or retailer sources in structured data. 

The report cautioned against exaggerated or unverifiable claims, saying low-trust language can be penalised, and calls for a consistent brand voice and structured FAQs and help resources.

The guide concluded that retailers already hold many of the data signals that influence AI ranking and product visibility, but that these signals are not always surfaced in product feeds. It said enriching feeds and content assets with attributes and trust-based data can help AI systems understand not only what a product is, but also why users value it and when it performs best, framing this as “AI ranking readiness” for conversational commerce.