How Microsoft's AI search actually picks brands-and what marketers can do about it

How Microsoft's AI search actually picks brands - and what marketers can do now

Microsoft Advertising released an updated AI marketer's guide on February 11, 2026, and it does what most playbooks don't: it explains how brands get pulled into AI answers, step by step. The big shift is simple: queries look like conversations, and assistants can answer before anyone clicks. Visibility now depends on how machines parse your data, not just how humans read your pages.

The guide, "Understanding AI search: A guide for modern marketers," moves past buzzwords and maps the mechanics behind AI-driven discovery. If you want your brand cited, recommended, or sourced, the work now spans ads, content, feeds, and structured data - all aligned to how large language models process information.

How LLMs pick brands: the short version

Microsoft lays out two tracks for visibility: paid placements (sponsored answers, multimedia ads) and organic inclusion inside AI responses. For organic, AI builds answers in three stages. Each stage raises the bar for inclusion and confidence.

• Baseline knowledge: What the model learned during training. Good for general facts, weak on freshness and specifics.
• Grounded refinement: Content retrieved from the web to verify, update, and enrich claims. This is where your crawlable pages and citations start to matter.
• Precision signals: Structured, first-party data that settles details like pricing, availability, specs, warranty, and policies. This is your product feeds, schema, and authoritative sources speaking in clear, machine-readable terms.

Microsoft frames this as "grounding" - AI answers backed by verifiable sources, not guesswork. If you want reliable coverage, you need content that can be fetched, checked, and trusted at each layer. For a quick primer on grounding patterns such as Retrieval Augmented Generation (RAG), see Microsoft's "use your data" approach for Azure OpenAI here.

SEO vs. GEO: what actually changed

Microsoft's stance: classic SEO still matters, but Generative Engine Optimization (GEO) adds machine-interpretability. Models don't read a page like a person. They chunk content, extract entities, and look for authoritative signals they can cite with confidence.

• Write for parsing, not prose: Short sections, scannable lists, direct answers. Clear nouns over fluffy adjectives.
• Entity clarity: Make the product, brand, category, and attributes unmistakable. Use consistent names and IDs across site, feeds, and third-party profiles.
• Completeness beats cleverness: Beyond "waterproof jacket," include specifics like weight, packability, return policy, warranty length, ratings, and awards. These become the reasons AI cites you.
• Signals over slogans: Verified ratings, accreditations, stock status, and price guarantees travel farther than copywriting flair.

Yes, the acronyms spark debate. But the technical reality stands: content built for machine precision outperforms content written only for human style. Meanwhile, auctions still reward exact match where it counts - proof that SEO fundamentals and keyword strategy haven't vanished.

Paid placements in AI answers

Microsoft confirms sponsored formats inside AI experiences and reports strong performance lifts when Copilot is involved. Internal data notes doubled click-through rates and a 53% lift in purchases when assistants participate in the path to purchase. Expect more inventory embedded in conversational flows and answer modules.

What the guide says about LLM mechanics (without the jargon)

• Models learn patterns, not gospel truths. Freshness and specificity improve when content can be retrieved and verified.
• Grounding is the backbone. RAG-style patterns insert your verified data into responses so the model isn't guessing.
• Precision sources win. First-party, structured, and authoritative beats vague, unstructured prose every time.

Contributors you'll recognize

The playbook pulls input from practitioners across SEO, AI, and product: Aleyda Solis, Britney Muller, Crystal Carter, Lily Ray, Michael King, Myriam Jessier, and Pedro Bojikian. The mix signals a clear message: winning in AI discovery is a cross-discipline job, not a single-channel tweak.

What to do now: a practical checklist

1) Make content machine-readable

• Give every key page a crisp summary and FAQs that directly answer intent-rich questions.
• Use concise headings, lists, and short paragraphs so content chunks cleanly.
• Standardize product names, attributes, and categories across site, feeds, and CRM.

2) Fortify structured data and feeds

• Implement schema for products, reviews, organization, FAQs, and how-tos.
• Keep feeds fresh with availability, price, shipping, returns, and warranty details.
• Push ratings, awards, and editorial quotes as structured attributes where possible.

3) Earn citations

• Publish clear, verifiable statements that other sites and assistants can cite.
• Strengthen digital PR that produces editorial mentions with clean source formatting.
• Ensure your brand profiles (and knowledge panels) are consistent across major platforms.

4) Optimize for GEO without losing SEO

• Keep technical SEO tight: crawlability, internal linking, speed, and canonical hygiene.
• Map "AI answer" intents and produce authoritative, current explainer pages.
• Write attribute-rich copy: specs, policies, guarantees, and social proof.

5) Lean into AI-native ad formats

• Test sponsored answers and conversational placements where available.
• Use precise audiences and first-party data. Exact match still matters in auctions.
• Measure assistant-assisted paths separately; expect higher CTR and conversion rates.

6) Measurement and feedback loops

• Track AI citations via Bing Webmaster Tools updates as they roll out.
• Monitor server logs for assistant user agents and RAG-like retrieval patterns.
• Tag "answer-led" journeys with UTMs and compare to classic search funnels.

Market signals you can't ignore

• Mediaocean reported 54% of marketers plan to increase AI media investment vs. 47% for search.
• ChatGPT began ad tests for 700M weekly active users, opening a major new channel.
• Microsoft Ads growth slowed to 10% last quarter, but AI ad infrastructure investment continues and is showing stronger CTR and purchase lifts with Copilot involvement.
• Agentic AI is heating up: McKinsey tracked $1.1B in 2024 equity funding and a 985% jump in related job postings.

How this shifts your plan

• Rebalance budgets: blend search, AI answer ads, and retail media. Test small, scale fast where unit economics hold.
• Treat entity clarity and structured data like your new design system. It feeds both organic inclusion and paid relevance.
• Run AI-native creative experiments: short, declarative copy that can be quoted, plus attribute-rich product detail.

Timeline highlights

• Feb 2023: Microsoft bakes ChatGPT-style features into Bing.
• May 2024: Google AI Overviews rolls out, changing classic result pages.
• Apr-Aug 2025: Microsoft Ads revenue tops $20B; Copilot-linked CTRs rise 73% in some periods.
• Nov 2025: Amazon launches an ads agent; AI assistants spread across ad stacks.
• Jan 2026: Microsoft pushes AEO/GEO playbooks and expands Performance Max-style options.
• Feb 11, 2026: Microsoft releases the updated "Understanding AI search" guide.

Where to get the guide

Microsoft's updated guide is available on its advertising site. Watch the blog for updates and rollouts tied to grounding and AI citation visibility here.

If you want structured practice

For hands-on training that maps these concepts to campaigns, content, and analytics, see the AI Certification for Marketing Specialists at Complete AI Training.

Bottom line

Assistants now act like product researchers. They verify, compare, and cite. To earn inclusion, give them clean facts, current feeds, and clear signals - then meet them with ad formats built for conversational contexts. The brands that win will look boring on the surface (structured, consistent, precise) and brilliant in performance.