Microsoft released a detailed sixteen-page guide on optimizing content for AI-driven search and chat experiences. While many recommendations overlap with traditional SEO, some specifically target the nuances of AI search platforms. The guide’s key insights revolve around two new concepts: Agentic Engine Optimization (AEO) and Generative Engine Optimization (GEO), both critical for surfacing content within AI-powered systems. **Understanding AEO and GEO** Microsoft highlights a paradigm shift in AI search from "ranking for clicks" to "being understood and recommended by AI. " Traditional SEO remains foundational but mainly helps content get found, whereas AEO and GEO decide if content is actually presented in AI-driven contexts. Distinct from the commonly known "Answer Engine Optimization, " AEO here means Agentic Engine Optimization, focusing on structuring content to be easily retrieved, interpreted, and directly answered by AI assistants. GEO, on the other hand, targets making content discoverable and persuasive within generative AI by enhancing clarity, trustworthiness, and authority. Microsoft emphasizes that these shifts impact all organizational teams: - Marketing must redefine brand differentiation. - Growth teams adjust to AI-influenced customer journeys. - Ecommerce measures success differently. - Data teams surface richer signals. - Engineering ensures systems are AI-readable and reliable. AI shopping is framed not as a single channel but as three overlapping consumer touchpoints: AI browsers (contextual browsing), AI assistants (interactive Q&A and decision guidance), and AI agents (actions like navigation and purchases). The core necessity is accurate, structured, and trustworthy product information accessible across these systems. **The Continued Role of SEO** Although AI search shifts focus from discovery to influence, SEO remains essential. Microsoft notes SEO primarily aids product discoverability, AEO enhances the AI’s ability to explain products clearly, and GEO builds credibility so AI confidently recommends them. Thus, winning in AI-enhanced shopping experiences requires more than ranking—it demands enabling AI to understand why a product should be chosen. **How AI Systems Recommend Products** Using Microsoft’s Copilot as an example, the guide explains that AI assistants process user queries by combining web data (general knowledge, category insights, brand positioning) with product feed data (prices, availability, specifications). AI tends to prioritize products based on these criteria, such as price and stock status. Upon user interaction, AI agents also scan website content for further context—like detailed reviews, explanatory videos, promotions, and delivery estimates—which inform their guidance. Microsoft categorizes data involved as: 1. **Crawled Data:** Indexed web content shaping brand perception and product grounding. 2.

**Product Feeds/APIs:** Structured, real-time data submitted by brands for accuracy in representation. 3. **Live Website Data:** Rich, current site information such as media, reviews, dynamic pricing, and transactions. Each source plays a vital role throughout the shopping journey, reinforcing that SEO remains key since AI carries out ongoing real-time web searches. **Microsoft's Three-Part Action Plan** 1. **Technical Foundations:** Ensure product catalogs are machine-readable, consistent, and up-to-date using structured data (schema) for products, offers, reviews, FAQs, and brands. Dynamic fields like pricing and availability must be current and aligned between feeds and on-page data, avoiding discrepancies. 2. **Optimize Content for Intent and Clarity:** Craft product descriptions emphasizing benefits and real use cases, use questions-friendly headings, and incorporate modular content like FAQs, specifications, key features, and comparisons. Support multi-modal AI interpretation with proper alt text, video transcripts, and structured image metadata, and provide contextual product information such as complementary items and bundles. 3. **Trust Signals (Authority and Credibility):** Boost credibility through verified and voluminous reviews, foster brand authority with real-world validations (press, certifications, partnerships), and maintain consistent, factual claims to avoid trust erosion. Use structured data to clarify brand legitimacy and identity, as AI assistants prioritize trustworthy, verified content for recommendations. **Summary of Takeaways** The shift to AI-powered search moves the goal from merely ranking high to earning strong recommendations. SEO remains foundational, but success hinges on AEO and GEO, which ensure AI understands, explains, and prefers your content. AI shopping operates as an ecosystem of browsers, assistants, and agents that rely on trustworthy data across crawled content, structured product feeds, and live website interactions. Brands excelling in this space maintain consistent machine-readable data and clear, context-rich content easily digestible by AI. Microsoft’s blog post offers the full downloadable guide titled *From Discovery to Influence: A Guide to AEO and GEO* for further details. *Featured Image by Shutterstock/Kues*